diff --git a/.eslintignore b/.eslintignore deleted file mode 100644 index 11613fc..0000000 --- a/.eslintignore +++ /dev/null @@ -1,4 +0,0 @@ -/.cache -/node_modules -/public -/coverage \ No newline at end of file diff --git a/.eslintrc.js b/.eslintrc.js deleted file mode 100644 index 10b33e7..0000000 --- a/.eslintrc.js +++ /dev/null @@ -1,56 +0,0 @@ -module.exports = { - env: { - browser: true, - es2021: true, - }, - extends: ["plugin:react/recommended", "airbnb", "airbnb/hooks", "prettier"], - parserOptions: { - ecmaFeatures: { - jsx: true, - }, - ecmaVersion: 12, - sourceType: "module", - }, - plugins: ["react"], - rules: { - "no-else-return": 0, - "react/jsx-props-no-spreading": 0, - "react/prop-types": 0, - "import/prefer-default-export": 0, - "react/function-component-definition": [ - 1, - { - namedComponents: "arrow-function", - unnamedComponents: "function-expression", - }, - ], - }, - settings: { - "import/resolver": { - alias: { - map: [ - ["config", "./config"], - ["src", "./src"], - ], - extensions: [".js", ".jsx", ".mdx"], - }, - }, - "import/order": [ - "error", - { - pathGroups: [ - { - pattern: "config/**", - group: "external", - position: "after", - }, - { - pattern: "src/**", - group: "external", - position: "after", - }, - ], - }, - ], - }, -}; diff --git a/.node-version b/.node-version deleted file mode 100644 index 72c7744..0000000 --- a/.node-version +++ /dev/null @@ -1 +0,0 @@ -18.12.1 \ No newline at end of file diff --git a/.prettierignore b/.prettierignore deleted file mode 100644 index de6a1aa..0000000 --- a/.prettierignore +++ /dev/null @@ -1,8 +0,0 @@ -/.cache -/.husky -/node_modules -/public -/coverage -/package-lock.json -*.mdx -00000000000000000000.json diff --git a/static/quickstart_docker/0.8.1_2.3.0/Dockerfile b/0.8.1_2.3.0/Dockerfile similarity index 100% rename from static/quickstart_docker/0.8.1_2.3.0/Dockerfile rename to 0.8.1_2.3.0/Dockerfile diff --git a/static/quickstart_docker/0.9.0_2.3.0/Dockerfile b/0.9.0_2.3.0/Dockerfile similarity index 100% rename from static/quickstart_docker/0.9.0_2.3.0/Dockerfile rename to 0.9.0_2.3.0/Dockerfile diff --git a/static/quickstart_docker/Dockerfile_delta_quickstart b/Dockerfile_delta_quickstart similarity index 100% rename from static/quickstart_docker/Dockerfile_delta_quickstart rename to Dockerfile_delta_quickstart diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 261eeb9..0000000 --- a/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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Please follow the steps from the Docker website to install Docker locally. Based on your local machine operating system, please choose the appropriate option listed on the [Get Docker](https://docs.docker.com/get-docker/) page. -This site requires Node 18, which you can install with `brew install node@18`. +Follow the steps below to build an Apache SparkTM image with Delta Lake installed, run a container, and follow the quickstart in an interactive notebook or shell with any of the options like Python, PySpark, Scala Spark or even Rust. -Install the dependencies with `npm install`. +1. [Working with Docker](#working-with-docker) + 1. [Build the image](#build-the-image) + 2. [Docker Hub](#docker-hub) +2. [Choose an interface](#choose-an-interface) -Run the site on your localhost with `npm start`. +> Note: Python version available in this Docker image is 3.9.2 and is available as `python3`. -## Contributing +## Working with Docker -Open a pull request or create an issue if you would like to report bugs or request changes. +### Build the Image -For big changes, it's best to first open an issue and get buy-in from the Delta Lake open source community before spending the time to create a pull request. +1. Clone this repo +2. Navigate to the cloned folder +3. Navigate to the `quickstart_docker` folder +4. Open a bash shell (if on windows use git bash, WSL, or any shell configured for bash commands) +5. Execute the following from the `static/quickstart_docker` folder + + ```bash + docker build -t delta_quickstart -f Dockerfile_delta_quickstart . + ``` + +#### Build Entry Point + +Your entry point for this locally built docker file is + +```bash +docker run --name delta_quickstart --rm -it --entrypoint bash delta_quickstart +``` + +### Docker Hub + +You can also download the image from DockerHub at [Delta Lake DockerHub](https://go.delta.io/dockerhub) + +Note, there are different versions of the Delta Lake docker + +| Tag | Platform | Python | Rust | Delta-Spark | Spark | JupyterLab | Pandas | ROAPI | +| ----------------- | -------- | ------ | ------ | ----------- | ----- | ---------- | ------ | ----- | +| 0.8.1_2.3.0 | amd64 | 0.8.1 | latest | 2.3.0 | 3.3.2 | 3.6.3 | 1.5.3 | 0.9.0 | +| 0.8.1_2.3.0_arm64 | arm64 | 0.8.1 | latest | 2.3.0 | 3.3.2 | 3.6.3 | 1.5.3 | 0.9.0 | +| 1.0.0_3.0.0 | amd64 | 0.12.0 | latest | 3.0.0 | 3.5.0 | 3.6.3 | 1.5.3 | 0.9.0 | +| 1.0.0_3.0.0_arm64 | arm64 | 0.12.0 | latest | 3.0.0 | 3.5.0 | 3.6.3 | 1.5.3 | 0.9.0 | +| latest | amd64 | 0.12.0 | latest | 3.0.0 | 3.5.0 | 3.6.3 | 1.5.3 | 0.9.0 | +| latest | arm64 | 0.12.0 | latest | 3.0.0 | 3.5.0 | 3.6.3 | 1.5.3 | 0.9.0 | + +> Note, the arm64 version is built for ARM64 platforms like Mac M1 + +Download the appropriate tag, e.g.: + +- `docker pull deltaio/delta-docker:latest` for the standard Linux docker +- `docker pull deltaio/delta-docker:latest_arm64` for running this optimally on your Mac M1 + +#### Image Entry Point + +Your entry point for the Docker Hub image is: + +```bash +# Running locally on Mac M1 +docker run --name delta_quickstart --rm -it --entrypoint bash deltaio/delta-docker:latest_arm64 +``` + +```bash +# Running on Linux VM +docker run --name delta_quickstart --rm -it --entrypoint bash deltaio/delta-docker:latest +``` + +Once the image has been built or you have downloaded the correct image, you can then move on to running the quickstart in a notebook or shell. + +## Choose the Delta Package version + +In the following instructions, the variable `${DELTA_PACKAGE_VERSION}` refers to the Delta Lake Package version. + +The current version is `delta-spark_2.12:3.0.0` which corresponds to Apache Spark 3.5.x release line. + +## Choose an Interface + +- [delta-rs Python](#delta-rs-python) +- [JupyterLab Notebook](#jupyterlab-notebook) +- [PySpark Shell](#pyspark-shell) +- [Scala Shell](#scala-shell) +- [Delta Rust API](#delta-rust-api) +- [ROAPI](#optional-roapi) + +### delta-rs Python + +1. Open a bash shell (if on windows use git bash, WSL, or any shell configured for bash commands) + +1. Run a container from the image with a bash entrypoint ([build](#build-entry-point) | [DockerHub](#image-entry-point)) + +1. Launch a _python_ interactive shell session with `python3` + + ```bash + python3 + ``` + + > Note: The Delta Rust Python bindings are already installed in this docker. To do this manually in your own environment, run the command: `pip3 install deltalake==0.12.0` + +1. Run some basic commands in the shell to write to and read from Delta Lake with Pandas + + ```python + import pandas as pd + from deltalake.writer import write_deltalake + from deltalake import DeltaTable + + # Create a Pandas DataFrame + df = pd.DataFrame({"data": range(5)}) + + # Write to the Delta Lake table + write_deltalake("/tmp/deltars_table", df) + + # Append new data + df = pd.DataFrame({"data": range(6, 11)}) + write_deltalake("/tmp/deltars_table", df, mode="append") + + # Read the Delta Lake table + dt = DeltaTable("/tmp/deltars_table") + + # Show the Delta Lake table + dt.to_pandas() + ``` + + ```python + ## Output + data + 0 0 + 1 1 + 2 2 + ... + 8 9 + 9 10 + ``` + +1. Review the files + + ```python + # List files for the Delta Lake table + dt.files() + ``` + + ```python + ## Output + ['0-6944fddf-60e3-4eab-811d-1398e9f64073-0.parquet', '1-66c7ee6e-6aab-4c74-866d-a82790102652-0.parquet'] + ``` + +1. Review history + + ```python + # Review history + dt.history() + ``` + + ```python + ## Output + [{'timestamp': 1698002214493, 'operation': 'WRITE', 'operationParameters': {'mode': 'Append', 'partitionBy': '[]'}, 'clientVersion': 'delta-rs.0.17.0', 'version': 1}, {'timestamp': 1698002207527, 'operation': 'CREATE TABLE', 'operationParameters': {'mode': 'ErrorIfExists', 'protocol': '{"minReaderVersion":1,"minWriterVersion":1}', 'location': 'file:///tmp/deltars_table', 'metadata': '{"configuration":{},"created_time":1698002207525,"description":null,"format":{"options":{},"provider":"parquet"},"id":"bf749aab-22b6-484b-bd73-dc1680ee4384","name":null,"partition_columns":[],"schema":{"fields":[{"metadata":{},"name":"data","nullable":true,"type":"long"}],"type":"struct"}}'}, 'clientVersion': 'delta-rs.0.17.0', 'version': 0}] + ``` + +1. Time Travel (load older version of table) + + ```python + # Load initial version of table + dt.load_version(0) + + # Show table + dt.to_pandas() + ``` + + ```python + ## Output + data + 0 0 + 1 1 + 2 2 + 3 3 + 4 4 + ``` + +1. Follow the delta-rs Python documentation [here](https://delta-io.github.io/delta-rs/python/usage.html#) + +1. To verify that you have a Delta Lake table, you can list the contents within the folder of your Delta Lake table. For example, in the previous code, you saved the table in `/tmp/deltars-table`. Once you close your `python3` process, run a list command in your Docker shell and you should get something similar to below. + + ```bash + $ ls -lsgA /tmp/deltars_table + ``` + + ```bash + total 12 + 4 -rw-r--r-- 1 NBuser 1689 Oct 22 19:16 0-6944fddf-60e3-4eab-811d-1398e9f64073-0.parquet + 4 -rw-r--r-- 1 NBuser 1691 Oct 22 19:16 1-66c7ee6e-6aab-4c74-866d-a82790102652-0.parquet + 4 drwxr-xr-x 2 NBuser 4096 Oct 22 19:16 _delta_log + ``` + +1. [Optional] Skip ahead to try out the [Delta Rust API](#delta-rust-api) and [ROAPI](#optional-roapi) + +### JupyterLab Notebook + +1. Open a bash shell (if on windows use git bash, WSL, or any shell configured for bash commands) + +2. Run a container from the image with a JuypterLab entrypoint + + ```bash + # Build entry point + docker run --name delta_quickstart --rm -it -p 8888-8889:8888-8889 delta_quickstart + ``` + + ```bash + # Image entry point (M1) + docker run --name delta_quickstart --rm -it -p 8888-8889:8888-8889 -entrypoint bash deltaio/delta-docker:latest_arm64 + ``` + +3. Running the above command gives a JupyterLab notebook URL, copy that URL and launch a browser to follow along the notebook and run each cell. + + > **Note that you may also launch the pyspark or scala shells after launching a terminal in JupyterLab** + +### PySpark Shell + +1. Open a bash shell (if on windows use git bash, WSL, or any shell configured for bash commands) + +2. Run a container from the image with a bash entrypoint ([build](#build-entry-point) | [DockerHub](#image-entry-point)) + +3. Launch a pyspark interactive shell session + + ```bash + + $SPARK_HOME/bin/pyspark --packages io.delta:${DELTA_PACKAGE_VERSION} \ + --conf spark.driver.extraJavaOptions="-Divy.cache.dir=/tmp -Divy.home=/tmp" \ + --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" \ + --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog" + ``` + + > Note: `DELTA_PACKAGE_VERSION` is set in `./startup.sh` + +4. Run some basic commands in the shell + + ```python + # Create a Spark DataFrame + data = spark.range(0, 5) + + # Write to a Delta Lake table + (data + .write + .format("delta") + .save("/tmp/delta-table") + ) + + # Read from the Delta Lake table + df = (spark + .read + .format("delta") + .load("/tmp/delta-table") + .orderBy("id") + ) + + # Show the Delta Lake table + df.show() + ``` + + ```python + ## Output + +---+ + | id| + +---+ + | 0| + | 1| + | 2| + | 3| + | 4| + +---+ + ``` + +5. Continue with the quickstart [here](https://docs.delta.io/latest/quick-start.html#create-a-table&language-python) + +6. To verify that you have a Delta Lake table, you can list the contents within the folder of your Delta Lake table. For example, in the previous code, you saved the table in `/tmp/delta-table`. Once you close your `pyspark` process, run a list command in your Docker shell and you should get something similar to below. + + ```bash + $ ls -lsgA /tmp/delta-table + ``` + + ```bash + total 52 + 4 drwxr-xr-x 2 NBuser 4096 Oct 22 19:23 _delta_log + 4 -rw-r--r-- 1 NBuser 296 Oct 22 19:23 part-00000-dc0fd6b3-9c0f-442f-a6db-708301b27bd2-c000.snappy.parquet + 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:23 .part-00000-dc0fd6b3-9c0f-442f-a6db-708301b27bd2-c000.snappy.parquet.crc + 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:23 part-00001-d379441e-1ee4-4e78-8616-1d9635df1c7b-c000.snappy.parquet + 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:23 .part-00001-d379441e-1ee4-4e78-8616-1d9635df1c7b-c000.snappy.parquet.crc + 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:23 part-00003-c08dcac4-5ea9-4329-b85d-9110493e8757-c000.snappy.parquet + 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:23 .part-00003-c08dcac4-5ea9-4329-b85d-9110493e8757-c000.snappy.parquet.crc + 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:23 part-00005-5db8dd16-2ab1-4d76-9b4d-457c5641b1c8-c000.snappy.parquet + 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:23 .part-00005-5db8dd16-2ab1-4d76-9b4d-457c5641b1c8-c000.snappy.parquet.crc + 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:23 part-00007-cad760e0-3c26-4d22-bed6-7d75a9459a0f-c000.snappy.parquet + 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:23 .part-00007-cad760e0-3c26-4d22-bed6-7d75a9459a0f-c000.snappy.parquet.crc + 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:23 part-00009-b58e8445-07b7-4e2a-9abf-6fea8d0c3e3f-c000.snappy.parquet + 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:23 .part-00009-b58e8445-07b7-4e2a-9abf-6fea8d0c3e3f-c000.snappy.parquet.crc + ``` + +### Scala Shell + +1. Open a bash shell (if on windows use git bash, WSL, or any shell configured for bash commands) + +2. Run a container from the image with a bash entrypoint ([build](#build-entry-point) | [DockerHub](#image-entry-point)) + +3. Launch a scala interactive shell session + + ```bash + $SPARK_HOME/bin/spark-shell --packages io.delta:${DELTA_PACKAGE_VERSION} \ + --conf spark.driver.extraJavaOptions="-Divy.cache.dir=/tmp -Divy.home=/tmp" \ + --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" \ + --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog" + ``` + +4. Run some basic commands in the shell + + > note: if you've already written to the Delta table in the python shell example, use `.mode("overwrite")` to overwrite the current delta table. You can always time-travel to rewind. + + ```scala + // Create a Spark DataFrame + val data = spark.range(0, 5) + + // Write to a Delta Lake table + + (data + .write + .format("delta") + .save("/tmp/delta-table") + ) + + // Read from the Delta Lake table + val df = (spark + .read + .format("delta") + .load("/tmp/delta-table") + .orderBy("id") + ) + + // Show the Delta Lake table + df.show() + ``` + + ```scala + ## Output + +---+ + | id| + +---+ + | 0| + | 1| + | 2| + | 3| + | 4| + +---+ + ``` + +5. Follow the quickstart [here](https://docs.delta.io/latest/quick-start.html#create-a-table&language-scala) + +6. To verify that you have a Delta Lake table, you can list the contents within the folder of your Delta Lake table. For example, in the previous code, you saved the table in `/tmp/delta-table`. Once you close your Scala Spark process [`spark-shell`], run a list command in your Docker shell and you should get something similar to below. + + ```bash + $ ls -lsgA /tmp/delta-table + ``` + + ```bash + total 52 + 4 drwxr-xr-x 2 NBuser 4096 Oct 22 19:28 _delta_log + 4 -rw-r--r-- 1 NBuser 296 Oct 22 19:28 part-00000-f1f417f7-df64-4c7c-96f2-6a452ae2b49e-c000.snappy.parquet + 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:28 .part-00000-f1f417f7-df64-4c7c-96f2-6a452ae2b49e-c000.snappy.parquet.crc + 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:28 part-00001-b28acb6f-f08a-460f-a24e-4d9c1affee86-c000.snappy.parquet + 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:28 .part-00001-b28acb6f-f08a-460f-a24e-4d9c1affee86-c000.snappy.parquet.crc + 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:28 part-00003-29079c58-d1ad-4604-9c04-0f00bf09546d-c000.snappy.parquet + 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:28 .part-00003-29079c58-d1ad-4604-9c04-0f00bf09546d-c000.snappy.parquet.crc + 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:28 part-00005-04424aa7-48e1-4212-bd57-52552c713154-c000.snappy.parquet + 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:28 .part-00005-04424aa7-48e1-4212-bd57-52552c713154-c000.snappy.parquet.crc + 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:28 part-00007-e7a54a4f-bee4-4371-a35d-d284e28eb9f8-c000.snappy.parquet + 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:28 .part-00007-e7a54a4f-bee4-4371-a35d-d284e28eb9f8-c000.snappy.parquet.crc + 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:28 part-00009-086e6cd9-e8c6-4f16-9658-b15baf22905d-c000.snappy.parquet + 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:28 .part-00009-086e6cd9-e8c6-4f16-9658-b15baf22905d-c000.snappy.parquet.crc + ``` + + + +### Delta Rust API + +> Note: Use a docker volume in case of running into limits "no room left on device" +> `docker volume create rustbuild` > `docker run --name delta_quickstart -v rustbuild:/tmp --rm -it --entrypoint bash deltaio/delta-docker:3.0.0` + +1. Open a bash shell (if on windows use git bash, WSL, or any shell configured for bash commands) + +2. Run a container from the image with a bash entrypoint ([build](#build-entry-point) | [DockerHub](#image-entry-point)) + +3. Execute `examples/read_delta_table.rs` to review the Delta Lake table metadata and files of the `covid19_nyt` Delta Lake table. + + ```bash + cd rs + cargo run --example read_delta_table + ``` + + > You can also use a different location to build and run the examples + + ```bash + cd rs + CARGO_TARGET_DIR=/tmp cargo run --example read_delta_table + ``` + + > If using [Delta Lake DockerHub](https://go.delta.io/dockerhub), sometimes the Rust environment hasn't been configured. To resolve this, run the command `source "$HOME/.cargo/env"` + + ```bash + === Delta table metadata === + DeltaTable(/opt/spark/work-dir/rs/data/COVID-19_NYT) + version: 0 + metadata: GUID=7245fd1d-8a6d-4988-af72-92a95b646511, name=None, description=None, partitionColumns=[], createdTime=Some(1619121484605), configuration={} + min_version: read=1, write=2 + files count: 8 + + + === Delta table files === + [Path { raw: "part-00000-a496f40c-e091-413a-85f9-b1b69d4b3b4e-c000.snappy.parquet" }, Path { raw: "part-00001-9d9d980b-c500-4f0b-bb96-771a515fbccc-c000.snappy.parquet" }, Path { raw: "part-00002-8826af84-73bd-49a6-a4b9-e39ffed9c15a-c000.snappy.parquet" }, Path { raw: "part-00003-539aff30-2349-4b0d-9726-c18630c6ad90-c000.snappy.parquet" }, Path { raw: "part-00004-1bb9c3e3-c5b0-4d60-8420-23261f58a5eb-c000.snappy.parquet" }, Path { raw: "part-00005-4d47f8ff-94db-4d32-806c-781a1cf123d2-c000.snappy.parquet" }, Path { raw: "part-00006-d0ec7722-b30c-4e1c-92cd-b4fe8d3bb954-c000.snappy.parquet" }, Path { raw: "part-00007-4582392f-9fc2-41b0-ba97-a74b3afc8239-c000.snappy.parquet" }] + ``` + +4. Execute `examples/read_delta_datafusion.rs` to query the `covid19_nyt` Delta Lake table using `datafusion` + + ```bash + cargo run --example read_delta_datafusion + ``` + + ```bash + === Datafusion query === + [RecordBatch { schema: Schema { fields: [Field { name: "cases", data_type: Int32, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: None }, Field { name: "county", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: None }, Field { name: "date", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: None }], metadata: {} }, columns: [PrimitiveArray + [ + 1, + 1, + 1, + 1, + 1, + ], StringArray + [ + "Snohomish", + "Snohomish", + "Snohomish", + "Cook", + "Snohomish", + ], StringArray + [ + "2020-01-21", + "2020-01-22", + "2020-01-23", + "2020-01-24", + "2020-01-24", + ]], row_count: 5 }] + ``` + +

+ + +### [Optional] ROAPI + +You can query your Delta Lake table with [Apache Arrow](https://github.com/apache/arrow) and [Datafusion](https://github.com/apache/arrow-datafusion) using [ROAPI](https://roapi.github.io/docs/config/dataset-formats/delta.html) which are pre-installed in this docker. + +> Note: If you need to do this in your own environment, run the command `pip3 install roapi==0.9.0` + +1. Open a bash shell (if on windows use git bash, WSL, or any shell configured for bash commands) + +2. Run a container from the image with a bash entrypoint ([build](#build-entry-point) | [DockerHub](#image-entry-point)) + +3. Start the `roapi` API using the following command. Notes: + + - the API calls are pushed to the `nohup.out` file. + - if you haven't created the `deltars_table` in your container create it via the [delta-rs Python](#delta-rs-python) option above. Alternatively you may omit the following from the command: + `--table 'deltars_table=/tmp/deltars_table/,format=delta'` as well as any steps that call the `deltars_table` + + ```bash + nohup roapi --addr-http 0.0.0.0:8080 --table 'deltars_table=/tmp/deltars_table/,format=delta' --table 'covid19_nyt=/opt/spark/work-dir/rs/data/COVID-19_NYT,format=delta' & + ``` + +4. Check the schema of the two Delta Lake tables + + ```bash + curl localhost:8080/api/schema + ``` + + ```bash + { + "covid19_nyt":{"fields":[ + {"name":"date","data_type":"Utf8","nullable":true,"dict_id":0,"dict_is_ordered":false}, + {"name":"county","data_type":"Utf8","nullable":true,"dict_id":0,"dict_is_ordered":false}, + {"name":"state","data_type":"Utf8","nullable":true,"dict_id":0,"dict_is_ordered":false}, + {"name":"fips","data_type":"Int32","nullable":true,"dict_id":0,"dict_is_ordered":false}, + {"name":"cases","data_type":"Int32","nullable":true,"dict_id":0,"dict_is_ordered":false}, + {"name":"deaths","data_type":"Int32","nullable":true,"dict_id":0,"dict_is_ordered":false} + ]}, + "deltars_table":{"fields":[ + {"name":"0","data_type":"Int64","nullable":true,"dict_id":0,"dict_is_ordered":false} + ]} + } + ``` + +5. Query the `deltars_table` + + ```bash + curl -X POST -d "SELECT * FROM deltars_table" localhost:8080/api/sql + ``` + + ```bash + # output + [{"0":0},{"0":1},{"0":2},{"0":3},{"0":4},{"0":6},{"0":7},{"0":8},{"0":9},{"0":10}] + ``` + +6. Query the `covid19_nyt` table + + ```bash + curl -X POST -d "SELECT cases, county, date FROM covid19_nyt ORDER BY cases DESC LIMIT 5" localhost:8080/api/sql + ``` + + ```bash + [ + {"cases":1208672,"county":"Los Angeles","date":"2021-03-11"}, + {"cases":1207361,"county":"Los Angeles","date":"2021-03-10"}, + {"cases":1205924,"county":"Los Angeles","date":"2021-03-09"}, + {"cases":1204665,"county":"Los Angeles","date":"2021-03-08"}, + {"cases":1203799,"county":"Los Angeles","date":"2021-03-07"} + ] + ``` diff --git a/config/menus.js b/config/menus.js deleted file mode 100644 index fdf24f4..0000000 --- a/config/menus.js +++ /dev/null @@ -1,183 +0,0 @@ -const docs = [ - { - url: "/", - label: "Docs Home", - title: true, - icon: "home", - }, - { - divider: true, - }, - { - url: "/latest/quick-start", - label: "Quick Start", - }, - { - url: "/latest/delta-batch", - label: "Table batch reads and writes", - }, - { - url: "/latest/delta-streaming", - label: "Table streaming reads and writes", - }, - { - url: "/latest/delta-update", - label: "Table deletes, updates, and merges", - }, - { - url: "/latest/delta-change-data-feed", - label: "Change data feed", - }, - { - url: "/latest/delta-utility", - label: "Table utility commands", - }, - { - url: "/latest/delta-constraints", - label: "Constraints", - }, - { - url: "/latest/versioning", - label: "Table protocol versioning", - }, - { - url: "/latest/delta-apidoc", - label: "Delta API Doc", - }, - { - url: "/latest/delta-storage", - label: "Storage configuration", - }, - { - url: "/latest/concurrency-control", - label: "Concurrency Control", - }, - { - url: "/latest/integrations", - label: "Access Delta tables from external data processing engines", - }, - { - url: "/latest/porting", - label: "Migration Guide", - }, - { - url: "/latest/best-practices", - label: "Best Practices", - }, - { - url: "/latest/delta-faq", - label: "Frequently asked questions (FAQ)", - }, - { - url: "/latest/releases", - label: "Releases", - }, - { - url: "/latest/delta-resources", - label: "Delta Lake Resources", - }, - { - url: "/latest/optimizations-oss", - label: "Optimizations", - }, - { - url: "/latest/table-properties", - label: "Delta table properties reference", - }, -]; - -const main = [ - { - label: "Docs", - url: "/", - }, - { - label: "Delta Lake", - url: "https://delta.io", - }, -]; - -const social = [ - { - label: "StackOverflow", - url: "https://stackoverflow.com/questions/tagged/delta-lake", - icon: "stackOverflow", - }, - { - label: "Twitter", - url: "https://twitter.com/DeltaLakeOSS", - icon: "twitter", - }, - { - label: "Slack Group", - url: "https://go.delta.io/slack", - icon: "slack", - }, - { - label: "LinkedIn", - url: "https://www.linkedin.com/company/deltalake", - icon: "linkedin", - }, -]; - -const community = [ - { - url: "https://delta.io/community", - label: "Community", - }, - { - url: "https://delta.io/resources/getting-help", - label: "Getting Help", - }, - { - url: "https://delta.io/resources/contributing-to-delta", - label: "Contributing to Delta", - }, -]; - -const learn = [ - { - label: "Getting Started", - url: "https://delta.io/learn/getting-started", - }, - { - label: "Blogs", - url: "https://delta.io/blog", - }, - { - label: "Tutorials", - url: "https://delta.io/learn/tutorials/", - }, - { - label: "Videos", - url: "https://delta.io/learn/videos/", - }, -]; - -const footer = [ - { - url: "https://delta.io/sharing/", - label: "Sharing", - }, - { - url: "https://delta.io/integrations/", - label: "Integrations", - }, - { - url: "https://delta.io/roadmap", - label: "Roadmap", - }, - { - url: "https://delta.io/blog/", - label: "Blogs", - }, -]; - -module.exports = { - docs, - community, - footer, - learn, - main, - social, -}; diff --git a/config/search.js b/config/search.js deleted file mode 100644 index 193fd35..0000000 --- a/config/search.js +++ /dev/null @@ -1,37 +0,0 @@ -const searchPluginConfig = { - resolve: "gatsby-plugin-local-search", - options: { - name: "search", - engine: "flexsearch", - query: ` - query SearchPluginQuery { - allMdx { - nodes { - id - frontmatter { - title - description - } - excerpt - fields { - path - } - } - } - } - `, - index: ["title"], - normalizer: ({ data }) => - data.allMdx.nodes.map((node) => ({ - id: node.id, - title: node.frontmatter.title, - description: node.frontmatter.description, - url: node.fields.path, - isExternal: false, - })), - }, -}; - -module.exports = { - searchPluginConfig, -}; diff --git a/config/theme.js b/config/theme.js deleted file mode 100644 index 3b97ba4..0000000 --- a/config/theme.js +++ /dev/null @@ -1,204 +0,0 @@ -// Colors -export const colors = { - primary: "#00add4", - link: "#00819e", - accent: "#ffda47", - text: "#002638", - textSecondary: "#536974", - bgLight: "#f5f8f9", - bgDark: "#042436", - border: "#B9C0C1", - info: "#00add4", - warning: "#ffda47", - danger: "red", - lightGreen: "#E6F6E6", - mediumGreen: "#0F9401", - darkGreen: "#023100", -}; - -// Color mixins -export const dark = { - bg: colors.bgDark, - color: "#FFFFFF", - textSecondary: "#73828C", - border: "#2C4553", -}; -export const light = { - bg: colors.bgLight, - color: colors.text, -}; - -// Typography -export const fontSizeBase = 16; -export const rem = (size) => `${size / fontSizeBase}rem`; -export const fontSizes = { - small: rem(14), - primary: rem(18), - secondary: rem(16), - h1: rem(48.83), - h2: rem(39.06), - h3: rem(31.25), - h4: rem(25), - h5: rem(20), - h6: rem(16), - code: rem(16 * 0.9), -}; -export const lineHeightBase = 1.5; -export const lineHeightHeader = 1.2; -export const fontWeightBold = 600; -export const fontFamilyBase = [ - "Source Sans Pro", - "-apple-system", - "BlinkMacSystemFont", - '"Segoe UI"', - "Roboto", - '"Helvetica Neue"', - "Arial", - "sans-serif", - '"Apple Color Emoji"', - '"Segoe UI Emoji"', - '"Segoe UI Symbol"', -].join(","); -export const fontFamilyCode = "Source Code Pro, Consolas, monospace"; - -// Grid -export const spacingRem = { - xs: 6, - sm: 12, - md: 18, - lg: 24, - xl: 30, - xxl: 60, -}; -export const spacing = { - none: rem(0), - xs: rem(6), - sm: rem(12), - md: rem(18), - lg: rem(24), - xl: rem(40), - xxl: rem(60), - xxxl: rem(100), -}; -export const breakpoints = { - xs: 0, - sm: 576, - md: 768, - lg: 992, - xl: 1200, -}; - -export const breakpointGreaterThan = (test) => (size) => { - const sizes = Object.keys(breakpoints); - - return ( - sizes.findIndex((bp) => bp === size) > sizes.findIndex((bp) => bp === test) - ); -}; - -/** - * Creates a function which wraps CSS rules in a media query set to the provided - * breakpoint and above. - * - * @param {string} breakpoint The breakpoint to wrap the CSS rule in. If an invalid breakpoint is passed, nothing will output. - * @returns {function} Function to wrap rules in a media query - */ -export const mediaBreakpointUp = (breakpoint) => (rules) => { - if (!breakpoints[breakpoint]) { - return ""; - } - - return `@media (min-width: ${breakpoints[breakpoint]}px) { - ${rules} - }`; -}; - -/** - * Creates a function which wraps CSS rules in a media query set to below the - * provided breakpoint. - * - * @param {string} breakpoint The breakpoint to wrap the CSS rule in. If an invalid breakpoint is passed, nothing will output. - * @returns {function} Function to wrap rules in a media query - */ -export const mediaBreakpointDown = (breakpoint) => (rules) => { - if (!breakpoints[breakpoint]) { - return ""; - } - - return `@media (max-width: ${breakpoints[breakpoint] - 1}px) { - ${rules} - }`; -}; - -/** - * Creates a function which wraps CSS rules in a media query set to below the - * provided breakpoint. - * - * @param {string} breakpoint The breakpoint to wrap the CSS rule in. If an invalid breakpoint is passed, nothing will output. - * @returns {function} Function to wrap rules in a media query - */ -export const mediaBreakpointBetween = - (minBreakpoint, maxBreakpoint) => (rules) => { - if (!breakpoints[minBreakpoint] || !breakpoints[maxBreakpoint]) { - return ""; - } - - return `@media (min-width: ${ - breakpoints[minBreakpoint] - }px) and (max-width: ${breakpoints[maxBreakpoint] - 1}px) { - ${rules} - }`; - }; - -/** - * Maps rules to breakpoints. - * - * @param {object} ruleMap The rule map. Equal to { [breakpoint]: rules }. - * @returns {string} CSS rules - */ -export const mediaBreakpointMap = (rulesMap) => { - const breakpointSortOrder = Object.keys(breakpoints); - - return Object.entries(rulesMap) - .sort( - ([a], [b]) => - breakpointSortOrder.indexOf(a) - breakpointSortOrder.indexOf(b) - ) - .reduce((styles, [breakpoint, rules]) => { - if (breakpoint === "xs") { - return [...styles, rules]; - } - - if (breakpoints[breakpoint]) { - return [...styles, mediaBreakpointUp(breakpoint)(rules)]; - } - - return styles; - }, []) - .join("\n"); -}; - -/** - * Combines multiple breakpoint maps together - * - * @param {object[]} ruleMaps Collection of ruleMaps - * @returns {string} CSS rules - */ -export const mediaBreakpointMaps = (ruleMaps) => { - const combinedMap = ruleMaps.reduce((combinedRulesMap, ruleMap) => { - let updatedRulesMap = combinedRulesMap; - - Object.entries(ruleMap).forEach(([breakpoint, rules]) => { - updatedRulesMap = { - ...updatedRulesMap, - [breakpoint]: `${ - updatedRulesMap[breakpoint] ? `${updatedRulesMap[breakpoint]}\n` : "" - }${rules}`, - }; - }); - - return updatedRulesMap; - }, {}); - - return mediaBreakpointMap(combinedMap); -}; diff --git a/config/version.js b/config/version.js deleted file mode 100644 index 1e495f1..0000000 --- a/config/version.js +++ /dev/null @@ -1,7 +0,0 @@ -const version = { - latest: "2.2.0", -}; - -module.exports = { - version, -}; diff --git a/gatsby-browser.jsx b/gatsby-browser.jsx deleted file mode 100644 index f2f8c63..0000000 --- a/gatsby-browser.jsx +++ /dev/null @@ -1,8 +0,0 @@ -const React = require("react"); -const { DefaultLayout } = require("./src/layouts/DefaultLayout"); -require("bootstrap/dist/css/bootstrap.min.css"); - -exports.wrapPageElement = ({ element, props }) => ( - // eslint-disable-next-line react/jsx-props-no-spreading - {element} -); diff --git a/gatsby-config.js b/gatsby-config.js deleted file mode 100644 index d9583ec..0000000 --- a/gatsby-config.js +++ /dev/null @@ -1,78 +0,0 @@ -const scala = require("highlight.js/lib/languages/scala"); -const { searchPluginConfig } = require("./config/search"); - -const wrapESMPlugin = - (name) => - (opts) => - async (...args) => { - const mod = await import(name); - const plugin = mod.default(opts); - return plugin(...args); - }; - -module.exports = { - siteMetadata: { - title: "Delta Lake Docs", - siteUrl: "https://docs.delta.io", - description: - "Delta Lake is an open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs for Scala, Java, Rust, Ruby, and Python.", - twitter: "@DeltaLakeOSS", - }, - plugins: [ - "gatsby-plugin-anchor-links", - "gatsby-plugin-styled-components", - "gatsby-plugin-image", - "gatsby-plugin-sitemap", - { - resolve: "gatsby-plugin-manifest", - options: { - name: "Delta Lake", - short_name: "Delta Lake Docs", - start_url: "/", - background_color: "#042436", - theme_color: "#00ADD4", - icon: "src/images/icon.png", - icon_options: { - purpose: "maskable", - }, - }, - }, - { - resolve: "gatsby-plugin-mdx", - options: { - gatsbyRemarkPlugins: [ - { - resolve: "gatsby-remark-images", - options: { - maxWidth: 684 * 2, - }, - }, - { - resolve: "gatsby-remark-copy-linked-files", - options: { - ignoreFileExtensions: ["png", "jpg", "jpeg"], - }, - }, - ], - mdxOptions: { - // eslint-disable-next-line global-require - remarkPlugins: [require("remark-gfm")], - rehypePlugins: [ - wrapESMPlugin("rehype-slug"), - [wrapESMPlugin("rehype-highlight"), { languages: { scala } }], - ], - }, - }, - }, - "gatsby-plugin-sharp", - "gatsby-transformer-sharp", - { - resolve: `gatsby-source-filesystem`, - options: { - name: `pages`, - path: `${__dirname}/src/pages`, - }, - }, - searchPluginConfig, - ], -}; diff --git a/gatsby-node.js b/gatsby-node.js deleted file mode 100644 index 3c0e34b..0000000 --- a/gatsby-node.js +++ /dev/null @@ -1,38 +0,0 @@ -const path = require("path"); -const { createFilePath } = require("gatsby-source-filesystem"); - -/** - * 1. Add "path" field to MDX pages so we can link to them in search results - */ -exports.onCreateNode = ({ node, getNode, actions }) => { - const { createNodeField } = actions; - - // [1] - if (node.internal.type === "Mdx") { - const pathField = createFilePath({ - node, - getNode, - }); - - createNodeField({ - node, - name: "path", - value: pathField, - }); - } -}; - -/** - * 1. Add support to resolve top-level directories - */ -exports.onCreateWebpackConfig = ({ actions }) => { - // [1] - actions.setWebpackConfig({ - resolve: { - alias: { - config: path.resolve("./config"), - src: path.resolve("./src"), - }, - }, - }); -}; diff --git a/gatsby-ssr.jsx b/gatsby-ssr.jsx deleted file mode 100644 index 6cc07da..0000000 --- a/gatsby-ssr.jsx +++ /dev/null @@ -1,12 +0,0 @@ -const React = require("react"); -const { DefaultLayout } = require("./src/layouts/DefaultLayout"); -require("bootstrap/dist/css/bootstrap.min.css"); - -exports.onRenderBody = ({ setHtmlAttributes }) => { - setHtmlAttributes({ lang: "en" }); -}; - -exports.wrapPageElement = ({ element, props }) => ( - // eslint-disable-next-line react/jsx-props-no-spreading - {element} -); diff --git a/jsconfig.json b/jsconfig.json deleted file mode 100644 index 278cded..0000000 --- a/jsconfig.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "compilerOptions": { - "module": "es6", - "moduleResolution": "node", - "jsx": "react", - "paths": { - "config/*": ["./config/*"], - "src/*": ["./src/*"] - } - }, - "include": ["gatsby-config.js", "config", "src"] -} diff --git a/netlify.toml b/netlify.toml deleted file mode 100644 index a22c37f..0000000 --- a/netlify.toml +++ /dev/null @@ -1,103 +0,0 @@ -[[plugins]] -package = "@netlify/plugin-gatsby" - -[[redirects]] - from = "/*" - to = "https://d1urk4s4laun85.cloudfront.net/:splat" - status = 200 - -# [[redirects]] -# from = "/2.1.0/*" -# to = "https://d1urk4s4laun85.cloudfront.net/2.1.0/:splat" -# status = 200 -# force = true - -# [[redirects]] -# from = "/2.0.1/*" -# to = "https://d1urk4s4laun85.cloudfront.net/2.0.1/:splat" -# status = 200 -# force = true - -# [[redirects]] -# from = "/2.0.0/*" -# to = "https://d1urk4s4laun85.cloudfront.net/2.0.0/:splat" -# status = 200 -# force = true - -# [[redirects]] -# from = "/1.2.1/*" -# to = "https://d1urk4s4laun85.cloudfront.net/1.2.1/:splat" -# status = 200 -# force = true - -# [[redirects]] -# from = "/1.2.0/*" -# to = "https://d1urk4s4laun85.cloudfront.net/1.2.0/:splat" -# status = 200 -# force = true - 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-1,91 +0,0 @@ -import styled from "styled-components"; -import Link from "src/components/Link"; -import color from "color"; -import { bool, oneOf } from "prop-types"; - -const getButtonColors = (props) => { - const { variant, theme, buttonColors } = props; - - if (buttonColors) { - return buttonColors; - } - - if (variant === "secondary") { - return { - text: theme.colors.primary, - background: "transparent", - border: theme.colors.primary, - textHover: "white", - backgroundHover: theme.colors.primary, - borderHover: theme.colors.primary, - }; - } - - if (variant === "accent") { - return { - text: theme.colors.text, - background: theme.colors.accent, - border: theme.colors.accent, - textHover: theme.colors.text, - backgroundHover: theme.colors.accent, - borderHover: theme.colors.accent, - }; - } - - return { - text: "white", - background: theme.colors.primary, - border: theme.colors.primary, - textHover: "white", - backgroundHover: theme.colors.primary, - borderHover: theme.colors.primary, - }; -}; - -const Button = styled(Link)` - display: inline-block; - padding: ${(props) => { - if (props.size === "small") { - return `${props.theme.spacing.xs} ${props.theme.spacing.sm}`; - } - - return `${props.theme.spacing.sm} ${props.theme.spacing.lg}`; - }}; - line-height: 1; - background-color: ${(props) => - props.borderStyle === "outlined" - ? getButtonColors(props).text - : getButtonColors(props).background}; - color: ${(props) => - props.borderStyle === "outlined" - ? getButtonColors(props).border - : getButtonColors(props).text}; - border-radius: 3px; - text-decoration: none; - font-weight: ${(props) => props.theme.fontWeightBold}; - font-size: ${(props) => { - if (props.size === "small") { - return props.theme.fontSizes.small; - } - - return props.theme.fontSizes.primary; - }}; - white-space: nowrap; - border: 1px solid ${(props) => getButtonColors(props).border}; - - &:hover { - background-color: ${(props) => - color(getButtonColors(props).backgroundHover).lighten(0.1)}; - border-color: ${(props) => - color(getButtonColors(props).borderHover).lighten(0.1)}; - color: ${(props) => getButtonColors(props).textHover}; - } -`; - -Button.propTypes = { - secondary: bool, - size: oneOf(["small", "medium"]), - borderStyle: oneOf(["outlined", "normal"]), -}; - -export default Button; diff --git a/src/components/ButtonRow.jsx b/src/components/ButtonRow.jsx deleted file mode 100644 index 9d4c277..0000000 --- a/src/components/ButtonRow.jsx +++ /dev/null @@ -1,9 +0,0 @@ -import styled from "styled-components"; -import Grid from "src/components/Grid"; - -const ButtonRow = styled(Grid)` - justify-content: center; - margin-bottom: ${(props) => props.theme.spacing.md}; -`; - -export default ButtonRow; diff --git a/src/components/CardDataList.jsx b/src/components/CardDataList.jsx deleted file mode 100644 index 4a5e878..0000000 --- a/src/components/CardDataList.jsx +++ /dev/null @@ -1,298 +0,0 @@ -import * as React from "react"; -import { - arrayOf, - oneOfType, - oneOf, - shape, - string, - number, - objectOf, - object, - bool, -} from "prop-types"; -import Link from "src/components/Link"; -import styled from "styled-components"; -import Grid from "src/components/Grid"; -import Typography, { TypographyContainer } from "src/components/Typography"; -import Embed from "src/components/Embed"; - -const applyFeaturedColumnStyles = ({ spanColumns, theme }, styles) => { - if (spanColumns) { - const columnsMap = Object.entries(spanColumns).reduce( - (obj, [size, numColumns]) => ({ - ...obj, - [size]: numColumns > 1 ? styles({ size, numColumns }) : undefined, - }), - {} - ); - - return theme.mediaBreakpointMap(columnsMap); - } - - return ""; -}; - -const Card = styled.div` - ${(props) => { - const { spanColumns, gutter, theme } = props; - - return applyFeaturedColumnStyles( - { spanColumns, theme }, - ({ numColumns, size }) => ` - grid-column: span ${numColumns}; - margin-bottom: ${theme.spacing.xl}; - border: 1px solid ${theme.colors.border}; - padding: ${numColumns > 2 ? theme.spacing.xl : theme.spacing.lg}; - - ${ - theme.breakpointGreaterThan("md")(size) && - ` - display: grid; - grid-auto-flow: column; - grid-gap: ${theme.spacing[gutter]}; - align-items: center; - grid-auto-columns: ${numColumns > 2 ? "4fr 6fr" : "1fr 1fr"}; - ` - } - - ` - ); - }} -`; - -const ThumbnailContainer = styled.div` - margin-bottom: ${(props) => props.theme.spacing.sm}; - - a { - margin-top: auto; - display: block; - } - - &:hover ~ ${TypographyContainer} a { - text-decoration: underline; - } - - ${(props) => { - const { spanColumns, theme } = props; - - return applyFeaturedColumnStyles({ spanColumns, theme }, ({ size }) => { - if (theme.breakpointGreaterThan("md")(size)) { - return ` - min-height: 100%; - margin-bottom: 0; - display: flex; - align-items: center; - - a { - margin-top: 0; - background-color: white; - } - `; - } - - return ` - margin-bottom: ${theme.spacing.lg}; - max-width: 480px; - `; - }); - }} -`; - -const ContentContainer = styled(TypographyContainer)` - ${(props) => { - const { spanColumns, theme } = props; - - return applyFeaturedColumnStyles( - { spanColumns, theme }, - () => ` - display: flex; - flex-flow: column; - justify-content: center; - ` - ); - }} -`; - -const CardTitle = styled(Typography)` - margin-top: 0; - margin-bottom: ${(props) => props.theme.spacing.xs}; - - ${(props) => { - const { spanColumns, theme } = props; - const { fontSizes } = theme; - - return applyFeaturedColumnStyles( - { spanColumns, theme }, - ({ size }) => - ` - display: flex; - flex-flow: column; - - &::before { - width: fit-content; - content: 'Latest post'; - padding: .35em .5em; - background-color: ${theme.colors.accent}; - font-size: 12px; - font-weight: ${theme.fontWeightBold}; - z-index: 2; - margin-bottom: ${theme.spacing.xs}; - } - - ${ - theme.breakpointGreaterThan("md")(size) && - `font-size: ${fontSizes.h3};` - } - ` - ); - }} -`; - -const CardContent = styled(Typography)` - color: ${(props) => props.theme.colors.textSecondary}; - margin-top: 0; - margin-bottom: ${(props) => props.theme.spacing.xs}; - ${(props) => - props.lineClamp && - ` - display: -webkit-box; - -webkit-box-orient: vertical; - -webkit-line-clamp: ${props.lineClamp}; - overflow: hidden; - `} -`; - -const CardTag = styled.span` - display: inline-block; - margin-right: ${(props) => props.theme.spacing.md}; - font-weight: ${(props) => props.theme.fontWeightBold}; -`; - -const CardDataList = (props) => { - const { - columns, - cards, - density, - thumbnailRatio, - maxWidth, - clampDescriptionLines, - showFeatured, - } = props; - - if (!cards.length) { - return null; - } - - let gutter = "lg"; - - if (density === "dense") { - gutter = "md"; - } - - if (density === "relaxed") { - gutter = "xl"; - } - - return ( - - {cards.map((card, i) => { - const isFeatured = showFeatured && i === 0; - - return ( - - - - - - - - - - {card.title} - - - {card.meta && ( - <> - {card.meta} - - - )} - - {card.docs && ( - - {" "} - docs{" "} - - )} - {card.docs && card.source_code && " | "} - {card.source_code && ( - - {" "} - source code{" "} - - )} - {card.docs && card.source_code &&
} - {card.tags?.length && ( - <> - {card.tags.map((tag) => ( - {tag} - ))} -
- - )} - {card.description} - - - - ); - })} - - ); -}; - -CardDataList.defaultProps = { - clampDescriptionLines: undefined, - density: undefined, - maxWidth: undefined, - showFeatured: false, -}; - -CardDataList.propTypes = { - columns: objectOf(number).isRequired, - cards: arrayOf( - shape({ - title: string.isRequired, - url: string.isRequired, - thumbnail: oneOfType([ - string, - shape({ - // eslint-disable-next-line react/forbid-prop-types - childImageSharp: object, - }), - ]).isRequired, - description: string.isRequired, - docs: string, - source_code: string, - meta: string, - tags: arrayOf(string), - }) - ).isRequired, - clampDescriptionLines: number, - density: oneOf(["dense", "relaxed"]), - thumbnailRatio: arrayOf(number).isRequired, - maxWidth: string, - showFeatured: bool, -}; - -export default CardDataList; diff --git a/src/components/CodeBlock.jsx b/src/components/CodeBlock.jsx deleted file mode 100644 index 00d752f..0000000 --- a/src/components/CodeBlock.jsx +++ /dev/null @@ -1,260 +0,0 @@ -import React, { useEffect, useRef, useState } from "react"; -import styled from "styled-components"; -import Button from "./Button"; -import Icon from "./Icon"; - -const CopyButton = styled(Button).attrs({ - size: "small", - borderStyle: "outlined", - href: "#", - variant: "white", - buttonColors: { - text: "#212121", - background: "#999999", - border: "#999999", - textHover: "#212121", - backgroundHover: "white", - borderHover: "white", - }, -})` - appearance: none; - position: absolute; - top: 0.5rem; - right: 0.5rem; - transition: opacity 100ms linear; - box-shadow: 0 0 0px 3px #212121; -`; - -const CodeBlockContainer = styled.div` - position: relative; - - > ${CopyButton} { - opacity: 0; - } - - &:hover { - > ${CopyButton} { - opacity: 1; - } - } -`; - -const HighlightedCode = styled.pre` - overflow: auto; - - > code { - padding: ${(props) => props.theme.spacing.sm}; - border-radius: 0.25em; - display: block; - overflow-x: auto; - white-space: pre; - } - - /*! - Theme: Material Darker - Author: Nate Peterson - License: ~ MIT (or more permissive) [via base16-schemes-source] - Maintainer: @highlightjs/core-team - Version: 2021.09.0 -*/ - - /* - WARNING: DO NOT EDIT THIS FILE DIRECTLY. - This theme file was auto-generated from the Base16 scheme material-darker - by the Highlight.js Base16 template builder. - - https://github.com/highlightjs/base16-highlightjs -*/ - - /* -base00 #212121 Default Background -base01 #303030 Lighter Background (Used for status bars, line number and folding marks) -base02 #353535 Selection Background -base03 #9A9A9A Comments, Invisibles, Line Highlighting -base04 #B2CCD6 Dark Foreground (Used for status bars) -base05 #EEFFFF Default Foreground, Caret, Delimiters, Operators -base06 #EEFFFF Light Foreground (Not often used) -base07 #FFFFFF Light Background (Not often used) -base08 #F07178 Variables, XML Tags, Markup Link Text, Markup Lists, Diff Deleted -base09 #F78C6C Integers, Boolean, Constants, XML Attributes, Markup Link Url -base0A #FFCB6B Classes, Markup Bold, Search Text Background -base0B #C3E88D Strings, Inherited Class, Markup Code, Diff Inserted -base0C #89DDFF Support, Regular Expressions, Escape Characters, Markup Quotes -base0D #82AAFF Functions, Methods, Attribute IDs, Headings -base0E #C792EA Keywords, Storage, Selector, Markup Italic, Diff Changed -base0F #FF5370 Deprecated, Opening/Closing Embedded Language Tags, e.g. -*/ - - .hljs { - color: #eeffff; - background: #212121; - } - - .hljs::selection, - .hljs ::selection { - background-color: #353535; - color: #eeffff; - } - - /* purposely do not highlight these things */ - .hljs-formula, - .hljs-params, - .hljs-property { - } - - /* base03 - #9A9A9A - Comments, Invisibles, Line Highlighting */ - .hljs-comment { - color: #9a9a9a; - } - - /* base04 - #B2CCD6 - Dark Foreground (Used for status bars) */ - .hljs-tag { - color: #b2ccd6; - } - - /* base05 - #EEFFFF - Default Foreground, Caret, Delimiters, Operators */ - .hljs-subst, - .hljs-punctuation, - .hljs-operator { - color: #eeffff; - } - - .hljs-operator { - opacity: 0.7; - } - - /* base08 - Variables, XML Tags, Markup Link Text, Markup Lists, Diff Deleted */ - .hljs-bullet, - .hljs-variable, - .hljs-template-variable, - .hljs-selector-tag, - .hljs-name, - .hljs-deletion { - color: #f07178; - } - - /* base09 - Integers, Boolean, Constants, XML Attributes, Markup Link Url */ - .hljs-symbol, - .hljs-number, - .hljs-link, - .hljs-attr, - .hljs-variable.constant_, - .hljs-literal { - color: #f78c6c; - } - - /* base0A - Classes, Markup Bold, Search Text Background */ - .hljs-title, - .hljs-class .hljs-title, - .hljs-title.class_ { - color: #ffcb6b; - } - - .hljs-strong { - font-weight: bold; - color: #ffcb6b; - } - - /* base0B - Strings, Inherited Class, Markup Code, Diff Inserted */ - .hljs-code, - .hljs-addition, - .hljs-title.class_.inherited__, - .hljs-string { - color: #c3e88d; - } - - /* base0C - Support, Regular Expressions, Escape Characters, Markup Quotes */ - .hljs-built_in, - .hljs-doctag, /* guessing */ - .hljs-quote, - .hljs-keyword.hljs-atrule, - .hljs-regexp { - color: #89ddff; - } - - /* base0D - Functions, Methods, Attribute IDs, Headings */ - .hljs-function .hljs-title, - .hljs-attribute, - .ruby .hljs-property, - .hljs-title.function_, - .hljs-section { - color: #82aaff; - } - - /* base0E - Keywords, Storage, Selector, Markup Italic, Diff Changed */ - .hljs-type, - /* .hljs-selector-id, */ - /* .hljs-selector-class, */ - /* .hljs-selector-attr, */ - /* .hljs-selector-pseudo, */ - .hljs-template-tag, - .diff .hljs-meta, - .hljs-keyword { - color: #c792ea; - } - .hljs-emphasis { - color: #c792ea; - font-style: italic; - } - - /* base0F - Deprecated, Opening/Closing Embedded Language Tags, e.g. */ - .hljs-meta, - /* - prevent top level .keyword and .string scopes - from leaking into meta by accident - */ - .hljs-meta .hljs-keyword, - .hljs-meta .hljs-string { - color: #ff5370; - } - - .hljs-meta .hljs-keyword, - /* for v10 compatible themes */ - .hljs-meta-keyword { - font-weight: bold; - } -`; - -const buttonLabels = { - default: "Copy", - success: "Copied!", - error: "Could not copy :(", -}; - -const CodeBlock = (props) => { - const { children } = props; - const codeBlock = useRef(); - const [buttonLabel, setButtonLabel] = useState(buttonLabels.default); - - const copyText = async () => { - const codeSnippet = codeBlock.current.innerText; - - try { - await navigator.clipboard.writeText(codeSnippet); - setButtonLabel(buttonLabels.success); - } catch { - setButtonLabel(buttonLabels.error); - } - }; - - useEffect(() => { - if (buttonLabel !== buttonLabels.default) { - setTimeout(() => setButtonLabel(buttonLabels.default), 2000); - } - }, [buttonLabel]); - - return ( - - { - e.preventDefault(); - copyText(); - }} - > - {buttonLabel} - - {children} - - ); -}; - -export default CodeBlock; diff --git a/src/components/CodeTabs.jsx b/src/components/CodeTabs.jsx deleted file mode 100644 index db18fde..0000000 --- a/src/components/CodeTabs.jsx +++ /dev/null @@ -1,71 +0,0 @@ -import React, { Children, useState, isValidElement } from "react"; -import styled from "styled-components"; - -const Tab = styled.button.attrs({ type: "button" })` - appearance: none; - color: ${(props) => (props.isActive ? props.theme.colors.primary : "white")}; - padding: ${(props) => props.theme.spacing.sm}; - line-height: 1; - background-color: transparent; - border: 0; - border-bottom: 2px solid - ${(props) => (props.isActive ? props.theme.colors.primary : "transparent")}; - margin-bottom: -2px; -`; - -const Tabs = styled.div` - display: flex; - background-color: #3f3f3f; - border-bottom: 2px solid #666; - border-radius: 0.25em 0.25em 0 0; -`; - -const CodeBlockWrapper = styled.div` - code { - border-top-left-radius: 0 !important; - border-top-right-radius: 0 !important; - } -`; - -const CodeTabs = (props) => { - const { children, tabs } = props; - const [tabIndex, setTabIndex] = useState(0); - - const codeBlocks = Children.toArray(children) - .filter( - (child) => - isValidElement(child) && - typeof child.type === "function" && - child.type.name === "pre" - ) - .map((codeBlock) => { - let lang = - codeBlock.props.children.props.className.match(/language-(\w+)/)?.[1] ?? - "txt"; - if (lang.toLowerCase() === "sql") { - lang = lang.toUpperCase(); - } else { - lang = lang.charAt(0).toUpperCase() + lang.slice(1); - } - return [lang, codeBlock]; - }); - - return ( -
- - {codeBlocks.map(([language], index) => ( - setTabIndex(index)}> - {(tabs?.length && tabs[index]) ?? language} - - ))} - - - {codeBlocks - .filter((unused, index) => index === tabIndex) - .map(([, codeBlock]) => codeBlock)} - -
- ); -}; - -export default CodeTabs; diff --git a/src/components/Divider.jsx b/src/components/Divider.jsx deleted file mode 100644 index 0d7dece..0000000 --- a/src/components/Divider.jsx +++ /dev/null @@ -1,6 +0,0 @@ -import styled from "styled-components"; - -export const Divider = styled.hr` - border-color: ${(props) => props.theme.colors.border}; - opacity: 1; // TODO: "common" styles -`; diff --git a/src/components/Embed.jsx b/src/components/Embed.jsx deleted file mode 100644 index d69b492..0000000 --- a/src/components/Embed.jsx +++ /dev/null @@ -1,73 +0,0 @@ -import { arrayOf, number, string, any } from "prop-types"; -import * as React from "react"; -import styled from "styled-components"; -import { GatsbyImage, getImage } from "gatsby-plugin-image"; - -const embedStyles = ` - position: absolute; - top: 0; - left: 0; - width: 100%; - height: 100%; -`; - -const EmbedContainer = styled.div` - position: relative; - ${(props) => - !props.src?.childImageSharp && - props.aspectRatio && - `padding-top: ${(props.aspectRatio[1] / props.aspectRatio[0]) * 100}%;`} -`; - -const EmbedImage = styled.img` - ${embedStyles} -`; - -const OtherEmbed = styled.div` - ${embedStyles} -`; - -const determineEmbedComponent = (src, alt) => { - if (/\.(jpe?g|gif|png)/.test(src)) { - return ; - } - - return {src}; -}; - -const Embed = (props) => { - const { alt, aspectRatio, src, maxWidth, className } = props; - - if (src?.childImageSharp) { - return ( -
- -
- ); - } - - const embeddedComponent = determineEmbedComponent(src, alt); - - return ( -
- - {embeddedComponent} - -
- ); -}; - -Embed.defaultProps = { - alt: undefined, - maxWidth: "none", -}; - -Embed.propTypes = { - alt: string, - aspectRatio: arrayOf(number.isRequired).isRequired, - // eslint-disable-next-line react/forbid-prop-types, react/require-default-props - src: any, - maxWidth: string, -}; - -export default Embed; diff --git a/src/components/Form.jsx b/src/components/Form.jsx deleted file mode 100644 index 069b6c1..0000000 --- a/src/components/Form.jsx +++ /dev/null @@ -1,79 +0,0 @@ -import color from "color"; -import * as React from "react"; -import styled from "styled-components"; -import Icon from "src/components/Icon"; - -const iconSizePx = 26; - -const InputContainer = styled.div` - position: relative; -`; - -const InputIcon = styled(Icon)` - color: ${(props) => - props.dark ? props.theme.dark.color : props.theme.light.color}; - font-size: ${iconSizePx}px; - position: absolute; - top: 50%; - left: ${(props) => props.theme.spacing.xs}; - margin-top: -${iconSizePx / 2}px; -`; - -const Input = styled.input` - font-size: ${(props) => props.theme.fontSizes.secondary}; - width: 100%; - border: 1px solid - ${(props) => - props.dark ? props.theme.dark.border : props.theme.colors.border}; - line-height: 1; - padding: ${(props) => { - if (props.hasIcon) { - return `${props.theme.spacing.xs} - ${props.theme.spacing.sm} ${props.theme.spacing.xs} - calc( - ${props.theme.spacing.xs} + ${iconSizePx}px + - ${props.theme.spacing.xs} - );`; - } - - return `${props.theme.spacing.xs} ${props.theme.spacing.sm};`; - }} - background-color: ${(props) => - props.dark ? props.theme.dark.border : "white"}; - color: ${(props) => - props.dark ? props.theme.dark.color : props.theme.light.color}; - - &::placeholder { - color: ${(props) => - props.dark - ? color(props.theme.colors.textSecondary).lighten(1) - : props.theme.colors.textSecondary}; - } - - &:focus { - outline: 0; - background-color: ${(props) => - props.dark ? "rgba(255, 255, 255, .15)" : "white"}; - border-color: ${(props) => - props.dark ? "transparent" : props.theme.colors.primary}; - } -`; - -export const InputField = (props) => { - const { type, value, onChange, placeholder, id, icon, dark } = props; - - return ( - - {icon && } - - - ); -}; diff --git a/src/components/Grid.jsx b/src/components/Grid.jsx deleted file mode 100644 index b404d7b..0000000 --- a/src/components/Grid.jsx +++ /dev/null @@ -1,117 +0,0 @@ -import * as React from "react"; -import styled from "styled-components"; -import { - arrayOf, - number, - string, - oneOf, - oneOfType, - shape, - bool, -} from "prop-types"; -import { breakpoints, spacing } from "config/theme"; - -const columnsRule = (columns) => { - if (typeof columns === "number") { - return `grid-template-columns: ${Array(columns).fill("1fr").join(" ")};`; - } - - return `grid-template-columns: ${columns - .reduce( - (columnSizes, columnSize) => [ - ...columnSizes, - typeof columnSize === "number" ? `${columnSize}fr` : columnSize, - ], - [] - ) - .join(" ")};`; -}; - -const gutterRule = (gutter) => ` - column-gap: ${spacing[gutter]}; - row-gap: ${spacing[gutter]}; -`; - -const GridContainer = styled.div` - display: grid; - ${(props) => props.evenRows && "grid-auto-rows: 1fr;"} - - ${(props) => { - const { theme, columns, gutter } = props; - - const columnsMap = Object.entries(columns).reduce( - (obj, [size, numColumns]) => ({ - ...obj, - [size]: columnsRule(numColumns), - }), - {} - ); - - const gutterMap = Object.entries(gutter).reduce( - (obj, [size, gutterSize]) => ({ - ...obj, - [size]: gutterRule(gutterSize), - }), - {} - ); - - return theme.mediaBreakpointMaps([columnsMap, gutterMap]); - }} -`; - -const Grid = (props) => { - const { evenRows, columns, gutter, className, children } = props; - - return ( - - {children} - - ); -}; - -Grid.defaultProps = { - columns: 1, - gutter: "md", - evenRows: false, -}; - -const columnSizeType = oneOfType([ - oneOfType([number, string]), - arrayOf(oneOfType([number, string])), -]); -const gutterSizeType = oneOf(Object.keys(spacing)); - -const responsiveGutterType = Object.keys(spacing).reduce( - (sizes, size) => ({ - ...sizes, - [size]: gutterSizeType, - }), - {} -); - -const responsiveColumnsType = Object.keys(breakpoints).reduce( - (sizes, size) => ({ - ...sizes, - [size]: columnSizeType, - }), - {} -); - -Grid.propTypes = { - evenRows: bool, - columns: oneOfType([columnSizeType, shape(responsiveColumnsType)]), - gutter: oneOfType([gutterSizeType, shape(responsiveGutterType)]), -}; - -export default Grid; diff --git a/src/components/Icon.jsx b/src/components/Icon.jsx deleted file mode 100644 index 83819f4..0000000 --- a/src/components/Icon.jsx +++ /dev/null @@ -1,68 +0,0 @@ -import { oneOf } from "prop-types"; -import * as React from "react"; -import styled from "styled-components"; - -const paths = { - copy: ( - <> - - - - ), - home: ( - - ), - more: ( - - ), - menu: , - search: ( - - ), - stackOverflow: ( - - ), - twitter: ( - - ), - slack: ( - - ), - externalLink: ( - - ), - linkedin: ( - - ), -}; - -const IconSvg = styled.svg` - font-size: inherit; - fill: currentColor; - width: 1em; - height: 1em; - display: inline-block; - flex-shrink: 0; - user-select: none; -`; - -const Icon = (props) => { - const { icon, className } = props; - - return ( - - ); -}; - -Icon.propTypes = { - icon: oneOf(Object.keys(paths)).isRequired, -}; - -export default Icon; diff --git a/src/components/ImageStrip.jsx b/src/components/ImageStrip.jsx deleted file mode 100644 index c419767..0000000 --- a/src/components/ImageStrip.jsx +++ /dev/null @@ -1,49 +0,0 @@ -import { arrayOf, object, oneOfType, shape, string } from "prop-types"; -import * as React from "react"; -import color from "color"; -import Embed from "src/components/Embed"; -import Link from "src/components/Link"; -import Grid from "src/components/Grid"; -import Typography from "src/components/Typography"; -import styled from "styled-components"; - -const CardThumbnail = styled.div` - background-color: ${(props) => color(props.theme.light.bg).darken(0.1)}; - margin-bottom: ${(props) => props.theme.spacing.sm}; - box-shadow: 0 2px 4px rgba(0, 0, 0, 0.25); -`; - -const ImageStrip = (props) => { - const { items, className } = props; - - return ( - - {items.map((item) => ( - - - - - {item.title && item.title !== "" && ( - {item.title} - )} - - ))} - - ); -}; - -ImageStrip.propTypes = { - items: arrayOf( - shape({ - title: string, - thumbnail: oneOfType([string, object]).isRequired, - url: string.isRequired, - }).isRequired - ).isRequired, -}; - -export default ImageStrip; diff --git a/src/components/Info.jsx b/src/components/Info.jsx deleted file mode 100644 index d99a40f..0000000 --- a/src/components/Info.jsx +++ /dev/null @@ -1,68 +0,0 @@ -import * as React from "react"; -import color from "color"; -import styled from "styled-components"; -import Typography, { TypographyContainer } from "src/components/Typography"; -import { colors } from "config/theme"; -import { oneOf } from "prop-types"; - -const levelStyles = { - info: { - borderColor: colors.info, - backgroundColor: color(colors.info).lighten(1.35), - textColor: color(colors.info).darken(0.5), - }, - warning: { - borderColor: colors.warning, - backgroundColor: color(colors.warning).lighten(0.5), - textColor: color(colors.warning).darken(0.5), - }, - danger: { - borderColor: colors.danger, - backgroundColor: color(colors.danger).lighten(0.9), - textColor: color(colors.danger).darken(0.5), - }, - tip: { - borderColor: colors.mediumGreen, - backgroundColor: colors.lightGreen, - textColor: colors.darkGreen, - }, -}; - -const BlockTitle = styled(Typography)` - font-size: ${(props) => props.theme.fontSizes.secondary}; -`; - -const Block = styled.div` - border: 1px solid ${(props) => levelStyles[props.level].borderColor}; - border-radius: 3px; - background-color: ${(props) => levelStyles[props.level].backgroundColor}; - padding: ${(props) => props.theme.spacing.md}; - margin: ${(props) => props.theme.spacing.lg} 0; - - ${BlockTitle} { - color: ${(props) => levelStyles[props.level].textColor}; - } -`; - -const Info = (props) => { - const { title, level, children } = props; - - return ( - - - {title && {title}} - {children} - - - ); -}; - -Info.defaultProps = { - level: "info", -}; - -Info.propTypes = { - level: oneOf(["info", "warning", "danger"]), -}; - -export default Info; diff --git a/src/components/Link.jsx b/src/components/Link.jsx deleted file mode 100644 index 842b75f..0000000 --- a/src/components/Link.jsx +++ /dev/null @@ -1,76 +0,0 @@ -/* eslint-disable react/jsx-props-no-spreading */ -/* eslint-disable jsx-a11y/anchor-has-content */ -import * as React from "react"; -import { Link as GatsbyLink } from "gatsby"; -import { OutboundLink } from "gatsby-plugin-google-gtag"; -import styled from "styled-components"; - -const externalLinkRegex = /^\w+:\/\//; -const anchorLinkRegex = /^#/; - -const StyledLink = styled.a` - ${(props) => - props.muted === true && - ` - color: inherit; - text-decoration: none; - `} -`; - -const Link = (props) => { - const { - href, - activeClassName, - partiallyActive, - active, - muted, - target, - newTab, - rel, - ...rest - } = props; - const isExternal = externalLinkRegex.test(href); - const isAnchor = anchorLinkRegex.test(href); - const linkTarget = target || newTab ? "_blank" : undefined; - const linkRel = rel || linkTarget === "_blank" ? "noreferrer" : undefined; - - if (isAnchor) { - return ( - - ); - } - - if (isExternal) { - return ( - - ); - } - - return ( - - ); -}; - -export default Link; diff --git a/src/components/LinkList.jsx b/src/components/LinkList.jsx deleted file mode 100644 index 981b34e..0000000 --- a/src/components/LinkList.jsx +++ /dev/null @@ -1,80 +0,0 @@ -import * as React from "react"; -import styled from "styled-components"; -import { Divider } from "src/components/Divider"; -import Link from "src/components/Link"; -import Icon from "src/components/Icon"; -import { rem, spacingRem, fontWeightBold } from "config/theme"; -import { shape, string, arrayOf, func } from "prop-types"; - -const LinkListNav = styled.nav` - display: grid; - grid-template-rows: auto; - row-gap: ${(props) => props.theme.spacing.xs}; - align-content: start; -`; - -const LinkListIcon = styled(Icon)` - font-size: 1.2em; - margin-right: ${(props) => props.theme.spacing.xs}; -`; - -const LinkList = (props) => { - const { - currentPathname, - links, - linkComponent, - renderInCurrentItem, - className, - } = props; - const LinkComponent = linkComponent || Link; - - const renderItems = (items, level = 0) => - items.map((item) => { - if (item.divider) { - return ; - } - - return ( - - - {item.icon && } - {item.label} - - {renderInCurrentItem && - currentPathname === item.url && - renderInCurrentItem(item)} - {item.items && renderItems(item.items, level + 1)} - - ); - }); - - return {renderItems(links)}; -}; - -LinkList.defaultProps = { - currentPathname: undefined, - renderInCurrentItem: undefined, -}; - -LinkList.propTypes = { - currentPathname: string, - links: arrayOf( - shape({ - url: string.isRequired, - label: string.isRequired, - }) - ).isRequired, - renderInCurrentItem: func, -}; - -export default LinkList; diff --git a/src/components/MDX.jsx b/src/components/MDX.jsx deleted file mode 100644 index 994b693..0000000 --- a/src/components/MDX.jsx +++ /dev/null @@ -1,73 +0,0 @@ -/* eslint-disable react/jsx-props-no-spreading */ -import * as React from "react"; -import Link from "src/components/Link"; -import { MDXProvider } from "@mdx-js/react"; -import styled from "styled-components"; -import Typography from "src/components/Typography"; -import CodeBlock from "src/components/CodeBlock"; -import Info from "src/components/Info"; -import ImageStrip from "src/components/ImageStrip"; -import CodeTabs from "src/components/CodeTabs"; - -const MarkdownContent = styled.div` - .align-image-left .gatsby-resp-image-wrapper { - margin-left: 0 !important; - } - , - .github-md-table-format { - table, - th, - td { - border: 1px solid #ddd; - } - table { - border-collapse: collapse; - } - tr:nth-child(even) { - background-color: #f2f2f2; - } - th, - td { - padding: 10px; - } - } -`; - -const Image = styled.img` - max-width: 100%; -`; - -const mdxComponents = { - p: (props) => , - h1: (props) => , - h2: (props) => , - h3: (props) => , - h4: (props) => , - h5: (props) => , - h6: (props) => , - ol: (props) => , - ul: (props) => , - a: Link, - li: (props) => , - hr: (props) => , - thematicBreak: (props) => , - img: (props) => , - pre: (props) => , - - // Custom components - Info, - ImageStrip, - CodeTabs, -}; - -const MDX = (props) => { - const { children } = props; - - return ( - - {children} - - ); -}; - -export default MDX; diff --git a/src/components/PageLayout/HeaderNavItem.jsx b/src/components/PageLayout/HeaderNavItem.jsx deleted file mode 100644 index 3f99f92..0000000 --- a/src/components/PageLayout/HeaderNavItem.jsx +++ /dev/null @@ -1,117 +0,0 @@ -import * as React from "react"; - -import Link from "src/components/Link"; -import { Divider } from "src/components/Divider"; -import { mediaBreakpointDown } from "config/theme"; -import styled from "styled-components"; -import { NavDropdown } from "react-bootstrap"; - -const showingMobileMenu = mediaBreakpointDown("lg"); - -export const HeaderTab = styled.div` - font-size: ${(props) => props.theme.fontSizes.secondary}; - padding: ${(props) => props.theme.spacing.xs} - ${(props) => props.theme.spacing.sm}; - color: inherit; - text-decoration: none; - min-height: 48px; - display: flex; - align-items: center; - border-bottom: 2px solid transparent; - - &.active { - border-bottom-color: ${(props) => props.theme.colors.primary}; - } - - ${(props) => - showingMobileMenu(` - border-bottom: 0; - - &.active { - color: ${props.theme.colors.primary}; - } - `)} - - a { - color: inherit; - text-decoration: none; - } -`; - -export const HeaderDropDown = styled(NavDropdown)` - font-size: ${(props) => props.theme.fontSizes.secondary}; - padding: ${(props) => props.theme.spacing.none} - ${(props) => props.theme.spacing.none}; - title-color: inherit; - text-decoration: none; - min-height: 48px; - display: flex; - align-items: center; - border-bottom: 2px solid transparent; - - &.active { - border-bottom-color: ${(props) => props.theme.colors.primary}; - } - - .nav-link, - .nav-link:focus, - .nav-link:hover { - padding: ${(props) => props.theme.spacing.xs} - ${(props) => props.theme.spacing.sm}; - } - - ${(props) => - showingMobileMenu(` - border-bottom: 0; - - .nav-link, .nav-link:focus, .nav-link:hover { - color: ${props.theme.light.color}; - } - - &.active { - color: ${props.theme.colors.primary}; - } - `)} -`; - -const HeaderNavItem = ({ items, className }) => { - if (items.divider) { - return ; - } - - if (items.submenu) { - return ( - - {items.submenu.map((link) => ( - - - {link.url ? ( - {link.label} - ) : ( - link.label - )} - - - ))} - - ); - } - - return ( - - {items.url ? {items.label} : items.label} - - ); -}; - -export default HeaderNavItem; diff --git a/src/components/PageLayout/PageFooter.jsx b/src/components/PageLayout/PageFooter.jsx deleted file mode 100644 index 1c288f2..0000000 --- a/src/components/PageLayout/PageFooter.jsx +++ /dev/null @@ -1,120 +0,0 @@ -import * as React from "react"; -import styled from "styled-components"; -import Section from "src/components/Section"; -import Grid from "src/components/Grid"; -import Typography from "src/components/Typography"; -import { community, footer, learn, social } from "config/menus"; -import Link from "src/components/Link"; -import logo from "./delta-lake-logo.svg"; -import theLinuxFoundationLogo from "./the-linux-foundation-logo.svg"; - -const menus = [ - { - title: "Delta Lake", - links: footer, - }, - { - title: "Learn", - links: learn, - }, - { - title: "Community", - links: community, - }, - { - title: "Social Channels", - links: social, - }, -]; - -const Copyright = styled.div` - ${(props) => - props.theme.mediaBreakpointUp("lg")(` - width: 250px; - `)} - - a { - color: inherit; - } - - a:hover { - color: white; - } -`; - -const Footer = styled.footer` - color: ${(props) => props.theme.dark.textSecondary}; -`; - -const FooterLogo = styled.img` - display: none; - - ${(props) => - props.theme.mediaBreakpointUp("lg")(` - display: block; - `)} -`; - -const FooterMenu = styled.nav` - display: grid; - grid-template-rows: auto; - row-gap: ${(props) => props.theme.spacing.xs}; - align-content: start; -`; - -const FooterMenuHeader = styled.div` - text-transform: uppercase; - font-weight: ${(props) => props.theme.fontWeightBold}; -`; - -const FooterMenuLink = styled(Link)` - color: ${(props) => props.theme.dark.color}; - - &:hover { - color: white; - } -`; - -const PageFooter = () => ( -
-
- - - - {menus.map((menu) => ( - - {menu.title} - {menu.links.map((link) => ( - - {link.label} - - ))} - - ))} - - - - Copyright © {new Date().getFullYear()} Delta Lake, a series of LF - Projects, LLC. For web site terms of use, trademark policy and other - project polcies please see{" "} - - https://lfprojects.org - - . - - The Linux Foundation - - -
-
-); - -export default PageFooter; diff --git a/src/components/PageLayout/PageHeader.jsx b/src/components/PageLayout/PageHeader.jsx deleted file mode 100644 index 3a3cb2b..0000000 --- a/src/components/PageLayout/PageHeader.jsx +++ /dev/null @@ -1,238 +0,0 @@ -import * as React from "react"; -import Link from "src/components/Link"; -import * as menus from "config/menus"; -import { mediaBreakpointUp, mediaBreakpointDown } from "config/theme"; -import styled from "styled-components"; -import Section from "src/components/Section"; -import Icon from "src/components/Icon"; -import logo from "./delta-lake-logo.svg"; -import PageHeaderSearchInput from "./PageHeaderSearchInput"; -import HeaderNavItem, { HeaderTab } from "./HeaderNavItem"; - -const { useState } = React; - -const showingMobileMenu = mediaBreakpointDown("lg"); -const hidingMobileMenu = mediaBreakpointUp("lg"); - -const Header = styled(Section)` - background-color: ${(props) => props.theme.dark.bg}; - color: ${(props) => props.theme.dark.light}; - min-height: 48px; - display: flex; - align-items: center; -`; - -const HeaderLogo = styled(Link)` - line-height: 0; - - ${showingMobileMenu(` - display: block; - margin-left: auto; - margin-right: auto; - `)} -`; - -const HeaderContainer = styled.div` - display: flex; - align-items: center; - position: relative; -`; - -const HeaderBackdrop = styled.button` - ${(props) => - showingMobileMenu(` - background-color: rgba(0, 0, 0, 0.5); - border: 0; - appearance: none; - position: fixed; - top: 0; - left: 0; - width: 100%; - height: 100%; - z-index: 998; - display: ${props.showing ? "block" : "none"}; - cursor: pointer; - `)} - - ${hidingMobileMenu(` - display: none; - `)} -`; - -const HeaderMenu = styled.div` - ${(props) => - showingMobileMenu(` - position: fixed; - top: 0; - right: 0; - transform: ${props.showing ? "translateX(0)" : "translateX(100%)"}; - bottom: 0; - background-color: white; - color: ${props.theme.light.color}; - z-index: 999; - overflow: auto; - `)} - - ${hidingMobileMenu(` - display: flex; - flex: 1 1 auto; - `)} -`; - -const HeaderNav = styled.div` - display: flex; - flex: 0 0 auto; - padding: 0 ${(props) => props.theme.spacing.md}; - - ${(props) => - showingMobileMenu(` - min-width: 200px; - flex-flow: column; - padding: 0 ${props.theme.spacing.sm}; - `)} -`; - -const DesktopHeaderNavItem = styled(HeaderNavItem)` - ${showingMobileMenu(` - display: none; -`)} -`; - -const MobileHeaderNavItem = styled(HeaderNavItem)` - ${hidingMobileMenu(` - display: none; -`)} -`; - -const HeaderSocialNav = styled(HeaderNav)` - margin-left: auto; - padding-right: 0; - - ${showingMobileMenu(` - margin-left: 0; - flex-flow: row; - `)} -`; - -const HeaderIcon = styled(Icon)` - font-size: ${(props) => props.theme.rem(26)}; -`; - -const HeaderToggle = styled(HeaderTab)` - border: 0; - background-color: transparent; - appearance: none; - position: absolute; - cursor: pointer; - - ${hidingMobileMenu(` - display: none; - `)} -`; - -const HeaderMenuToggle = styled(HeaderToggle)` - right: 0; -`; - -const HeaderSearchToggle = styled(HeaderToggle)` - left: 0; -`; - -const HeaderSearch = styled.div` - ${(props) => - showingMobileMenu(` - position: fixed; - top: 0; - left: 0; - bottom: 0; - width: 75%; - min-width: 300px; - transform: ${props.showing ? "translateX(0)" : "translateX(-100%)"}; - background-color: white; - color: ${props.theme.light.color}; - z-index: 999; - padding: ${props.theme.spacing.md} - `)} - - ${hidingMobileMenu(` - display: none; - `)} -`; - -const DesktopHeaderSearchInput = styled(PageHeaderSearchInput)` - display: none; - - ${(props) => - hidingMobileMenu(` - display: block; - flex: 1 1 auto; - min-height: 48px; - padding: ${props.theme.spacing.xs} 0; - `)} -`; - -const PageHeader = () => { - const [menuShowing, setMenuShowing] = useState(false); - const [searchShowing, setSearchShowing] = useState(false); - const [searchInput, setSearchInput] = useState(""); - - return ( -
- - setSearchShowing(true)}> - - - - Delta Lake - - setMenuShowing(true)}> - - - - - {menus.main.map((link) => ( - - ))} - {menus.docs.map((link) => ( - - ))} - - - - {menus.social.map((link) => { - const { label, url, icon } = link; - - return ( - - - {" "} - - - ); - })} - - - - - - { - setMenuShowing(false); - setSearchShowing(false); - }} - showing={menuShowing || searchShowing} - /> - -
- ); -}; - -export default PageHeader; diff --git a/src/components/PageLayout/PageHeaderSearchInput.jsx b/src/components/PageLayout/PageHeaderSearchInput.jsx deleted file mode 100644 index 99627b1..0000000 --- a/src/components/PageLayout/PageHeaderSearchInput.jsx +++ /dev/null @@ -1,179 +0,0 @@ -import { string, func, bool } from "prop-types"; -import * as React from "react"; -import styled from "styled-components"; -import { InputField } from "src/components/Form"; -import { graphql, useStaticQuery } from "gatsby"; -import { useFlexSearch } from "react-use-flexsearch"; -import Link from "src/components/Link"; -import Icon from "../Icon"; - -const PageHeaderSearchInputRoot = styled.div` - position: relative; - z-index: 99; -`; - -const PageHeaderSearchInputRootCloseOutside = styled.div` - position: fixed; - top: 0; - left: 0; - width: 100%; - height: 100%; - z-index: 98; -`; - -const SearchInput = styled(InputField)` - border-radius: 3px; -`; - -const SearchResultsList = styled.div` - position: absolute; - top: 100%; - left: 0; - right: 0; - background-color: white; - color: ${(props) => props.theme.light.color}; - overflow: auto; - ${(props) => - props.floating - ? ` - box-shadow: 0 2px 4px rgba(0, 0, 0, .15); - max-height: 450px; - z-index: 999; - ` - : ` - margin: 0 -${props.theme.spacing.md}; - `} -`; - -const NoResults = styled.div` - padding: ${(props) => props.theme.spacing.md}; - font-size: ${(props) => props.theme.fontSizes.secondary}; -`; - -const SearchResult = styled(Link)` - display: block; - padding: ${(props) => props.theme.spacing.md}; - text-decoration: none; - background-color: white; - color: inherit; - font-size: ${(props) => props.theme.rem(12)}; - - strong { - display: block; - font-size: ${(props) => props.theme.fontSizes.secondary}; - } - - em { - font-style: normal; - font-weight: normal; - color: ${(props) => props.theme.colors.textSecondary}; - } - - span { - display: block; - width: 100%; - text-overflow: ellipsis; - overflow: hidden; - white-space: nowrap; - } - - &:hover { - background-color: ${(props) => props.theme.light.bg}; - - strong { - text-decoration: underline; - } - } -`; - -const ExternalLinkIcon = styled(Icon)` - vertical-align: baseline; - display: inline-block; - position: relative; - top: 0.125em; - margin-left: 0.125em; - color: ${(props) => props.theme.colors.primary}; -`; - -const query = graphql` - query PageHeaderSearchInput { - localSearchSearch { - index - store - } - } -`; - -const PageHeaderSearchInput = (props) => { - const { input, onChange, dark, floatingResults, className } = props; - const data = useStaticQuery(query); - const { index: searchIndex, store: searchStore } = data.localSearchSearch; - const results = useFlexSearch(input, searchIndex, searchStore); - - return ( - <> - - { - const { value } = e.target; - - // Prevent sending spaces - if (value.trim() === "") { - onChange(""); - } else { - onChange(value); - } - }} - placeholder="Search…" - dark={dark} - /> - {input !== "" && ( - - {results.length === 0 ? ( - No results matched “{input}” - ) : ( - results.map((result) => ( - - {result.type} - - {result.title} - {result.isExternal && ( - - )} - - {result.description} - - )) - )} - - )} - - {floatingResults && input !== "" && ( - onChange("")} /> - )} - - ); -}; - -PageHeaderSearchInput.defaultProps = { - className: "", - dark: false, - floatingResults: false, -}; - -PageHeaderSearchInput.propTypes = { - input: string.isRequired, - onChange: func.isRequired, - dark: bool, - floatingResults: bool, - className: string, -}; - -export default PageHeaderSearchInput; diff --git a/src/components/PageLayout/delta-lake-logo.svg b/src/components/PageLayout/delta-lake-logo.svg deleted file mode 100644 index 456efb9..0000000 --- a/src/components/PageLayout/delta-lake-logo.svg +++ /dev/null @@ -1,8 +0,0 @@ - - - delta-logo-rev - - - - - \ No newline at end of file diff --git a/src/components/PageLayout/index.jsx b/src/components/PageLayout/index.jsx deleted file mode 100644 index 4253b87..0000000 --- a/src/components/PageLayout/index.jsx +++ /dev/null @@ -1,78 +0,0 @@ -import * as React from "react"; -import styled, { createGlobalStyle, ThemeProvider } from "styled-components"; -import * as theme from "config/theme"; -import PageHeader from "./PageHeader"; -import PageFooter from "./PageFooter"; - -const GlobalStyles = createGlobalStyle` - html { - -webkit-font-smoothing: antialiased; - width: 100vw; - overflow-x: hidden; - } - - *, *::before, *::after { - box-sizing: border-box; - } - - body { - font-family: ${(props) => props.theme.fontFamilyBase}; - font-size: 1rem; - line-height: ${(props) => props.theme.lineHeightBase}; - margin: 0; - background-color: ${(props) => props.theme.dark.bg}; - color: ${(props) => props.theme.dark.color}; - } - - input, - select { - font-family: ${(props) => props.theme.fontFamilyBase}; - } - - a { - color: ${(props) => props.theme.colors.link}; - } - - a:hover { - color: inherit; - } - - code { - font-family: ${(props) => props.theme.fontFamilyCode}; - font-size: ${(props) => props.theme.fontSizes.code}; - } - - h1, h2, h3, h4, h5, h6, b, strong { - font-weight: ${(props) => props.theme.fontWeightBold}; - } - - .nav-link, .nav-link:focus, .nav-link:hover { - color: ${(props) => props.theme.dark.color}; - } - - .dropdown-menu-dark { - color: ${(props) => props.theme.dark.color}; - background-color: ${(props) => props.theme.dark.bg}; - border-color: rgba(0,0,0,.15); - } -`; - -const Content = styled.main` - background-color: ${(props) => props.theme.light.bg}; - color: ${(props) => props.theme.light.color}; -`; - -const PageLayout = (props) => { - const { children } = props; - - return ( - - - - {children} - - - ); -}; - -export default PageLayout; diff --git a/src/components/PageLayout/the-linux-foundation-logo.svg b/src/components/PageLayout/the-linux-foundation-logo.svg deleted file mode 100644 index 243ae96..0000000 --- a/src/components/PageLayout/the-linux-foundation-logo.svg +++ /dev/null @@ -1,15 +0,0 @@ - - - Combined Shape - - - - - - - - - - - - \ No newline at end of file diff --git a/src/components/Pagination.jsx b/src/components/Pagination.jsx deleted file mode 100644 index ad6a544..0000000 --- a/src/components/Pagination.jsx +++ /dev/null @@ -1,47 +0,0 @@ -import * as React from "react"; -import { bool, number, string } from "prop-types"; -import Grid from "src/components/Grid"; -import Button from "src/components/Button"; -import styled from "styled-components"; - -const ButtonRow = styled(Grid)` - justify-content: center; - margin-top: ${(props) => props.theme.spacing.xxl}; -`; - -const Pagination = (props) => { - const { hasPreviousPage, hasNextPage, currentPage, basePath } = props; - - if (!hasPreviousPage && !hasNextPage) { - return null; - } - - const buttons = hasNextPage && hasPreviousPage ? 2 : 1; - - return ( - - {hasPreviousPage && ( - - )} - {hasNextPage && ( - - )} - - ); -}; - -Pagination.propTypes = { - hasPreviousPage: bool.isRequired, - hasNextPage: bool.isRequired, - currentPage: number.isRequired, - basePath: string.isRequired, -}; - -export default Pagination; diff --git a/src/components/SEO.jsx b/src/components/SEO.jsx deleted file mode 100644 index 43b0c22..0000000 --- a/src/components/SEO.jsx +++ /dev/null @@ -1,61 +0,0 @@ -import * as React from "react"; -import { number, string } from "prop-types"; -import { graphql, useStaticQuery } from "gatsby"; -import { Helmet } from "react-helmet"; - -const query = graphql` - query SeoQuery { - site { - siteMetadata { - title - description - twitter - siteUrl - } - } - } -`; - -const SEO = (props) => { - const { title, description, thumbnailPath, pageIndex, children } = props; - - const { site } = useStaticQuery(query); - - const documentTitle = `${ - pageIndex > 1 ? `${title} - Page ${pageIndex}` : title - } | ${site.siteMetadata.title}`; - - return ( - - {documentTitle} - {description && } - - {description && } - - - - - - {description && } - {children} - - ); -}; - -SEO.defaultProps = { - pageIndex: 0, - description: "", - thumbnailPath: undefined, -}; - -SEO.propTypes = { - title: string.isRequired, - description: string, - thumbnailPath: string, - pageIndex: number, -}; - -export default SEO; diff --git a/src/components/Section.jsx b/src/components/Section.jsx deleted file mode 100644 index 86a5742..0000000 --- a/src/components/Section.jsx +++ /dev/null @@ -1,133 +0,0 @@ -import * as React from "react"; -import { oneOf, bool, string, node, oneOfType, func } from "prop-types"; -import styled from "styled-components"; -import { breakpoints, rem, spacing } from "config/theme"; -import Typography from "src/components/Typography"; - -export const containerPadding = spacing.sm; -export const containerWidth = { - xs: "100%", - sm: rem(breakpoints.sm - 16 * 2), - md: rem(breakpoints.md - 16 * 2), - lg: rem(breakpoints.lg - 16 * 2), - xl: rem(breakpoints.xl - 16 * 2), -}; - -const Container = styled.div` - padding: 0 ${containerPadding}; - margin: 0 auto; - - ${(props) => - props.theme.mediaBreakpointMap( - Object.entries(containerWidth).reduce( - (obj, [size, width]) => ({ - ...obj, - [size]: `width: ${width};`, - }), - {} - ) - )} -`; - -const SectionRoot = styled.div` - padding: ${(props) => props.theme.spacing[props.padding] || 0} 0; - - ${(props) => { - const { background } = props; - - if (!background) { - return ""; - } - - if (typeof background === "function") { - return `background: ${background(props.theme)};`; - } - - return `background: ${background};`; - }} -`; - -const SectionHeader = styled.div` - ${(props) => props.centered && "text-align: center;"} - margin-bottom: ${(props) => props.theme.spacing.xl}; -`; - -const SectionTitle = styled(Typography)` - margin: 0; - margin-bottom: ${(props) => - props.showingSubtitle ? props.theme.spacing.md : "0"}; -`; - -const SectionSubtitle = styled.div` - max-width: ${(props) => props.theme.rem(props.theme.breakpoints.lg)}; - margin-left: auto; - margin-right: auto; -`; - -const Section = (props) => { - const { - title, - titleSize, - subtitle, - primary, - padding, - background, - container, - centeredHeader, - className, - children, - } = props; - - const ContainerComponent = container ? Container : React.Fragment; - - return ( - - - {(title || subtitle) && ( - - {title && ( - - {title} - - )} - {subtitle && ( - - {typeof subtitle === "function" ? subtitle() : subtitle} - - )} - - )} - {children} - - - ); -}; - -Section.defaultProps = { - padding: undefined, - title: undefined, - titleSize: "h2", - subtitle: undefined, - primary: false, - container: true, - background: undefined, -}; - -Section.propTypes = { - padding: oneOf(Object.keys(spacing)), - title: oneOfType([string, node]), - titleSize: oneOf(["h1", "h2", "h3", "h4", "h5"]), - subtitle: oneOfType([func, node]), - primary: bool, - container: bool, - background: oneOfType([func, string]), -}; - -export default Section; diff --git a/src/components/Table.jsx b/src/components/Table.jsx deleted file mode 100644 index cb1a231..0000000 --- a/src/components/Table.jsx +++ /dev/null @@ -1,61 +0,0 @@ -import { arrayOf, shape, string } from "prop-types"; -import * as React from "react"; -import styled from "styled-components"; - -const StyledTable = styled.table` - th, - td { - min-width: 200px; - padding: ${(props) => props.theme.spacing.xs}; - font-size: ${(props) => props.theme.fontSizes.primary}; - } -`; - -const Table = (props) => { - const { headers, data, className } = props; - - const columns = headers.map((header) => header.field); - - return ( - - - - {headers.map((header) => ( - {header.label} - ))} - - - - {data.map((row) => ( - - {columns.map((column) => ( - {row[column]} - ))} - - ))} - - - ); -}; - -Table.propTypes = { - headers: arrayOf( - shape({ - label: string.isRequired, - field: string.isRequired, - }) - ).isRequired, - data: arrayOf( - shape({ - key: string.isRequired, - // other fields can be added - }) - ).isRequired, - className: string, -}; - -Table.defaultProps = { - className: undefined, -}; - -export default Table; diff --git a/src/components/Tiles.jsx b/src/components/Tiles.jsx deleted file mode 100644 index 5642698..0000000 --- a/src/components/Tiles.jsx +++ /dev/null @@ -1,100 +0,0 @@ -import * as React from "react"; -import styled from "styled-components"; -import Link from "src/components/Link"; -import Grid from "src/components/Grid"; -import Embed from "src/components/Embed"; -import { arrayOf, shape, string, bool, number } from "prop-types"; - -const Tile = styled.div` - padding: ${(props) => props.theme.spacing.lg}; - display: flex; - align-items: center; - justify-content: center; - - ${(props) => - props.$dark && - ` - background-color: white; - box-shadow: 0 2px 4px rgba(0, 0, 0, 0.15); - `} -`; - -const TileImage = styled.img` - width: 100%; - height: auto; - ${(props) => props.maxWidth && `max-width: ${props.maxWidth}px`} -`; - -const TileLabel = styled.div` - text-align: center; - margin-top: ${(props) => props.theme.spacing.sm}; -`; - -const TileLink = styled(Tile).attrs({ as: Link, muted: true })` - display: block; - text-align: center; - color: ${(props) => props.theme.light.color}; - - &:hover { - color: ${(props) => props.theme.light.color}; - text-decoration: underline; - } -`; - -const Tiles = (props) => { - const { - columns, - tiles, - maxImageWidth, - showLabels, - dark, - evenRows, - className, - } = props; - - return ( - - {tiles.map((tile) => { - const TileComponent = tile.url ? TileLink : Tile; - - return ( - - - {showLabels && {tile.label}} - - ); - })} - - ); -}; - -Tiles.defaultProps = { - maxImageWidth: undefined, - dark: false, - showLabels: false, - evenRows: false, - className: undefined, -}; - -Tiles.propTypes = { - columns: Grid.propTypes.columns.isRequired, - tiles: arrayOf( - shape({ - thumbnail: string.isRequired, - label: string.isRequired, - url: string, - }) - ).isRequired, - maxImageWidth: number, - dark: bool, - showLabels: bool, - evenRows: bool, - className: string, -}; - -export default Tiles; diff --git a/src/components/Typography.jsx b/src/components/Typography.jsx deleted file mode 100644 index a4460b2..0000000 --- a/src/components/Typography.jsx +++ /dev/null @@ -1,169 +0,0 @@ -import { oneOf } from "prop-types"; -import * as React from "react"; -import styled from "styled-components"; - -const scaleSize = (amount, variant) => { - if (variant === "dense") { - return amount * 0.4; - } - - if (variant === "relaxed") { - return amount * 1.6; - } - - return amount; -}; - -const blockStyles = (props) => ` - margin-top: 0; - margin-bottom: ${scaleSize(1, props.density)}em; -`; - -const headerStyles = (props) => ` - line-height: ${props.theme.lineHeightHeader}; - margin-top: ${scaleSize(1.5, props.density)}em; - margin-bottom: ${scaleSize(0.5, props.density)}em; - - &:hover .anchor-link { - color: #666; - opacity: 1; - } -`; - -export const TypographyContainer = styled.div` - > :first-child { - margin-top: 0; - } - - > :last-child { - margin-bottom: 0; - } -`; - -const Paragraph = styled.p` - ${(props) => blockStyles(props)} - font-size: ${(props) => props.theme.fontSizes.primary}; -`; - -const Secondary = styled(Paragraph)` - font-size: ${(props) => props.theme.fontSizes.secondary}; -`; - -const H1 = styled.h1` - ${(props) => headerStyles(props)} - font-size: ${(props) => props.theme.fontSizes.h1}; -`; - -const H2 = styled.h2` - ${(props) => headerStyles(props)} - font-size: ${(props) => props.theme.fontSizes.h2}; -`; - -const H3 = styled.h3` - ${(props) => headerStyles(props)} - font-size: ${(props) => props.theme.fontSizes.h3}; -`; - -const H4 = styled.h4` - ${(props) => headerStyles(props)} - font-size: ${(props) => props.theme.fontSizes.h4}; -`; - -const H5 = styled.h5` - ${(props) => headerStyles(props)} - font-size: ${(props) => props.theme.fontSizes.h5}; -`; - -const H6 = styled.h6` - ${(props) => headerStyles(props)} - font-size: ${(props) => props.theme.fontSizes.h6}; -`; - -const OrderedList = styled.ol` - ${(props) => blockStyles(props)} - font-size: ${(props) => props.theme.fontSizes.primary}; -`; - -const UnorderedList = styled.ul` - ${(props) => blockStyles(props)} - font-size: ${(props) => props.theme.fontSizes.primary}; -`; - -const ListItem = styled.li` - ${(props) => blockStyles(props)} -`; - -const HorizontalRule = styled.hr` - margin: ${(props) => scaleSize(1.5, props.density)}em 0; - border: none; - height: 1px; - background: ${(props) => props.theme.colors.border}; -`; - -const AnchorLink = styled.a` - color: #666; - opacity: 0.3; - position: absolute; - transform: translate(-1em, -2px) scale(0.7, 0.7); - margin-left: 10px; - text-decoration: none; -`; - -const elements = { - p: Paragraph, - p2: Secondary, - h1: H1, - h2: H2, - h3: H3, - h4: H4, - h5: H5, - h6: H6, - ol: OrderedList, - ul: UnorderedList, - li: ListItem, - hr: HorizontalRule, -}; - -function getAnchor(text) { - return text - .toString() - .toLowerCase() - .replace(/[^a-z0-9 ]/g, "") - .replace(/[ ]/g, "-"); -} - -const Typography = (props) => { - const { variant, anchor, children, ...rest } = props; - - const Element = elements[variant]; - - if (!anchor) { - return ( - // eslint-disable-next-line react/jsx-props-no-spreading - {children} - ); - } else { - const anchorText = getAnchor(children); - const link = `#${anchorText}`; - return ( - - - § - - {children} - - ); - } -}; - -Typography.defaultProps = { - variant: "p", - density: undefined, -}; - -Typography.propTypes = { - variant: oneOf(Object.keys(elements)), - density: oneOf(["dense", "relaxed"]), -}; - -export default Typography; diff --git a/src/images/icon.png b/src/images/icon.png deleted file mode 100644 index bcf9779..0000000 Binary files a/src/images/icon.png and /dev/null differ diff --git a/src/layouts/DefaultLayout.jsx b/src/layouts/DefaultLayout.jsx deleted file mode 100644 index bfe69f4..0000000 --- a/src/layouts/DefaultLayout.jsx +++ /dev/null @@ -1,36 +0,0 @@ -import * as React from "react"; -import SEO from "src/components/SEO"; -import PageLayout from "src/components/PageLayout"; -import MDX from "src/components/MDX"; -import { TypographyContainer } from "src/components/Typography"; -import * as menus from "config/menus"; -import Section from "src/components/Section"; -import TwoColumnLayout from "./components/TwoColumnLayout"; - -export const DefaultLayout = (props) => { - const { location, pageContext, children } = props; - const { frontmatter = {} } = pageContext; - const { title, description, thumbnail } = frontmatter; - - const sidebarMenu = menus["docs" || frontmatter.menu]; - - return ( - - - -
- - {children} - -
- - - ); -}; diff --git a/src/layouts/components/OneColumnLayout.jsx b/src/layouts/components/OneColumnLayout.jsx deleted file mode 100644 index 0cc1a4a..0000000 --- a/src/layouts/components/OneColumnLayout.jsx +++ /dev/null @@ -1,33 +0,0 @@ -import * as React from "react"; -import styled from "styled-components"; -import Section from "src/components/Section"; -import { oneOf } from "prop-types"; - -const ContentContainer = styled.div` - ${(props) => - props.width !== "full" && - ` - max-width: 800px; - margin: 0 auto; - `} -`; - -const OneColumnLayout = (props) => { - const { width, children } = props; - - return ( -
- {children} -
- ); -}; - -OneColumnLayout.defaultProps = { - width: undefined, -}; - -OneColumnLayout.propTypes = { - width: oneOf(["full"]), -}; - -export default OneColumnLayout; diff --git a/src/layouts/components/TableOfContents.jsx b/src/layouts/components/TableOfContents.jsx deleted file mode 100644 index b28c308..0000000 --- a/src/layouts/components/TableOfContents.jsx +++ /dev/null @@ -1,50 +0,0 @@ -import * as React from "react"; -import Link from "src/components/Link"; -import LinkList from "src/components/LinkList"; -import Typography from "src/components/Typography"; -import styled from "styled-components"; - -const TableOfContentsLinkTitle = styled(Typography)` - text-transform: uppercase; - font-weight: ${(props) => props.theme.fontWeightBold}; - color: ${(props) => props.theme.light.color}; -`; - -const TableOfContentsLink = styled(Link)` - ${(props) => - props.active ? `font-weight: ${props.theme.fontWeightBold};` : ""} - text-decoration: none; - color: ${(props) => props.theme.light.color}; - - &:hover { - text-decoration: underline; - } -`; - -const mapTocItems = (items) => - items.map((item) => ({ - ...item, - label: item.title, - items: item.items ? mapTocItems(item.items) : undefined, - })); - -const TableOfContents = (props) => { - const { currentPathname, items, className, showTitle } = props; - - return ( -
- {showTitle && ( - - On this page - - )} - -
- ); -}; - -export default TableOfContents; diff --git a/src/layouts/components/TwoColumnLayout.jsx b/src/layouts/components/TwoColumnLayout.jsx deleted file mode 100644 index 61058ba..0000000 --- a/src/layouts/components/TwoColumnLayout.jsx +++ /dev/null @@ -1,120 +0,0 @@ -import { string, shape, arrayOf, object } from "prop-types"; -import * as React from "react"; -import Section from "src/components/Section"; -import Grid from "src/components/Grid"; -import styled from "styled-components"; -import Link from "src/components/Link"; -import LinkList from "src/components/LinkList"; -import TableOfContents from "./TableOfContents"; - -const menuSidebarWidthPx = 200; - -const ColumnsGrid = styled(Grid)` - position: relative; - z-index: 1; -`; - -const MainColumn = styled.div` - max-width: 100%; - overflow: hidden; -`; - -const SidebarColumn = styled.div` - display: none; - border-right: 1px solid ${(props) => props.theme.colors.border}; - padding-right: ${(props) => props.theme.spacing.md}; - - ${(props) => - props.theme.mediaBreakpointUp("lg")(` - display: block; - `)} -`; - -const SidebarMenuLink = styled(Link)` - color: inherit; - text-decoration: none; - - &.active { - color: ${(props) => props.theme.colors.primary}; - } - - &:hover { - text-decoration: underline; - } -`; - -const SidebarMenu = styled(LinkList)` - position: sticky; - top: ${(props) => props.theme.spacing.md}; -`; - -const InnerToc = styled(TableOfContents)` - margin-bottom: ${(props) => props.theme.spacing.md}; -`; - -const TwoColumnLayout = (props) => { - const { currentPathname, children, sidebarMenu, tocItems } = props; - const hasSidebar = sidebarMenu || tocItems; - - return ( -
- - {hasSidebar && ( - - {sidebarMenu ? ( - - tocItems ? : null - } - /> - ) : ( - - )} - - )} - -
{children}
- - -
- ); -}; - -TwoColumnLayout.defaultProps = { - currentPathname: undefined, - sidebarMenu: undefined, - tocItems: undefined, -}; - -TwoColumnLayout.propTypes = { - currentPathname: string, - // eslint-disable-next-line react/forbid-prop-types - sidebarMenu: arrayOf(object), - tocItems: arrayOf( - shape({ - title: string, - url: string, - // eslint-disable-next-line react/forbid-prop-types - items: arrayOf(object), - }) - ), -}; - -export default TwoColumnLayout; diff --git a/src/pages/404.jsx b/src/pages/404.jsx deleted file mode 100644 index 2838507..0000000 --- a/src/pages/404.jsx +++ /dev/null @@ -1,53 +0,0 @@ -import * as React from "react"; -import Link from "src/components/Link"; - -// styles -const pageStyles = { - color: "#232129", - padding: "96px", - fontFamily: "-apple-system, Roboto, sans-serif, serif", -}; -const headingStyles = { - marginTop: 0, - marginBottom: 64, - maxWidth: 320, -}; - -const paragraphStyles = { - marginBottom: 48, -}; -const codeStyles = { - color: "#8A6534", - padding: 4, - backgroundColor: "#FFF4DB", - fontSize: "1.25rem", - borderRadius: 4, -}; - -// markup -const NotFoundPage = () => ( -
- Not found -

Page not found

-

- Sorry{" "} - - 😔 - {" "} - we couldn’t find what you were looking for. -
- {process.env.NODE_ENV === "development" ? ( - <> -
- Try creating a page in src/pages/ - . -
- - ) : null} -
- Go home. -

-
-); - -export default NotFoundPage; diff --git a/src/pages/index.mdx b/src/pages/index.mdx deleted file mode 100644 index 6883b3e..0000000 --- a/src/pages/index.mdx +++ /dev/null @@ -1,38 +0,0 @@ ---- -title: Delta Lake Spark -description: Learn about Delta Lake features and resources to learn about Delta Lake. ---- - -[Delta Lake](https://delta.io) is an [open source -project](https://github.com/delta-io/delta) that enables building a [Lakehouse -architecture](https://databricks.com/blog/2020/01/30/what-is-a-data-lakehouse.html) -on top of [data lakes](https://databricks.com/discover/data-lakes/introduction). - -Delta Lake provides [ACID transactions](/latest/concurrency-control), scalable -metadata handling, and unifies [streaming](/latest/delta-streaming) and -[batch](/latest/delta-batch) data processing on top of existing data lakes, such as -S3, ADLS, GCS, and HDFS. - -For a quick overview and benefits of Delta Lake, watch this YouTube video (3 -minutes). - -Specifically, Delta Lake offers: - -- [ACID transactions](/latest/concurrency-control) on Spark: Serializable isolation - levels ensure that readers never see inconsistent data. -- Scalable metadata handling: Leverages Spark distributed processing power to - handle all the metadata for petabyte-scale tables with billions of files at - ease. -- [Streaming](/latest/delta-streaming) and [batch](/latest/delta-batch) unification: A - table in Delta Lake is a batch table as well as a streaming source and sink. - Streaming data ingest, batch historic backfill, interactive queries all just - work out of the box. -- Schema enforcement: Automatically handles schema variations to prevent - insertion of bad records during ingestion. -- [Time travel](/latest/delta-batch/#query-an-older-snapshot-of-a-table-time-travel): Data versioning enables - rollbacks, full historical audit trails, and reproducible machine learning - experiments. -- [Upserts](/latest/delta-update/#upsert-into-a-table-using-merge) and - [deletes](/latest/delta-update/#delete-from-a-table): Supports merge, update and delete - operations to enable complex use cases like change-data-capture, - slowly-changing-dimension (SCD) operations, streaming upserts, and so on. diff --git a/src/pages/latest/best-practices.mdx b/src/pages/latest/best-practices.mdx deleted file mode 100644 index 988da5e..0000000 --- a/src/pages/latest/best-practices.mdx +++ /dev/null @@ -1,202 +0,0 @@ ---- -title: Best Practices -description: Learn best practices when using Delta Lake ---- - -This article describes best practices when using Delta Lake. - -## Choose the right partition column - -You can partition a Delta table by a column. The most commonly used partition column is `date`. Follow these two rules of thumb for deciding on what column to partition by: - -- If the cardinality of a column will be very high, do not use that column for partitioning. For example, if you partition by a column `userId` and if there can be 1M distinct user IDs, then that is a bad partitioning strategy. -- Amount of data in each partition: You can partition by a column if you expect data in that partition to be at least 1 GB. - -## Compact files - -If you continuously write data to a Delta table, it will over time accumulate a -large number of files, especially if you add data in small batches. This can -have an adverse effect on the efficiency of table reads, and it can also affect -the performance of your file system. Ideally, a large number of small files -should be rewritten into a smaller number of larger files on a regular basis. -This is known as compaction. - -You can compact a table by repartitioning it to smaller number of files. In -addition, you can specify the option `dataChange` to be `false` indicates that -the operation does not change the data, only rearranges the data layout. This -would ensure that other concurrent operations are minimally affected due to this -compaction operation. - -For example, you can compact a table into 16 files: - - - -```scala -val path = "..." -val numFiles = 16 - -spark.read - .format("delta") - .load(path) - .repartition(numFiles) - .write - .option("dataChange", "false") - .format("delta") - .mode("overwrite") - .save(path) -``` - -```python -path = "..." -numFiles = 16 - -(spark.read - .format("delta") - .load(path) - .repartition(numFiles) - .write - .option("dataChange", "false") - .format("delta") - .mode("overwrite") - .save(path)) -``` - - - -If your table is partitioned and you want to repartition just one partition -based on a predicate, you can read only the partition using `where` and write -back to that using `replaceWhere`: - - - -```scala -val path = "..." -val partition = "year = '2019'" -val numFilesPerPartition = 16 - -spark.read - .format("delta") - .load(path) - .where(partition) - .repartition(numFilesPerPartition) - .write - .option("dataChange", "false") - .format("delta") - .mode("overwrite") - .option("replaceWhere", partition) - .save(path) -``` - -```python -path = "..." -partition = "year = '2019'" -numFilesPerPartition = 16 - -(spark.read - .format("delta") - .load(path) - .where(partition) - .repartition(numFilesPerPartition) - .write - .option("dataChange", "false") - .format("delta") - .mode("overwrite") - .option("replaceWhere", partition) - .save(path)) -``` - - - - - -Using `dataChange = false` on an operation that changes data can corrupt the data in the table. - - - - - -This operation does not remove the old files. To remove them, run the [VACUUM](/latest/delta-utility/#remove-files-no-longer-referenced-by-a-delta-table) command. - - - -## Replace the content or schema of a table - -Sometimes you may want to replace a Delta table. For example: - -- You discover the data in the table is incorrect and want to replace the content. -- You want to rewrite the whole table to do incompatible schema changes (such as changing column types). - -While you can delete the entire directory of a Delta table and create a new table on the same path, it's _not recommended_ because: - -- Deleting a directory is not efficient. A directory containing very large files can take hours or even days to delete. -- You lose all of content in the deleted files; it's hard to recover if you delete the wrong table. -- The directory deletion is not atomic. While you are deleting the table a concurrent query reading the table can fail or see a partial table. - -- You may experience potential consistency issues on S3, which is only eventually consistent. - -If you don't need to change the table schema, you can -[delete](/latest/delta-update#delete-from-a-table) data from a Delta table and insert -your new data, or [update](/latest/delta-update#update-a-table) the table to fix the -incorrect values. - -If you want to change the table schema, you can replace the whole table -atomically. For example: - - - -```python -dataframe.write \ - .format("delta") \ - .mode("overwrite") \ - .option("overwriteSchema", "true") \ - .partitionBy() \ - .saveAsTable("") # Managed table -dataframe.write \ - .format("delta") \ - .mode("overwrite") \ - .option("overwriteSchema", "true") \ - .option("path", "") \ - .partitionBy() \ - .saveAsTable("") # External table -``` - -```sql -REPLACE TABLE USING DELTA PARTITIONED BY () AS SELECT ... -- Managed table -REPLACE TABLE USING DELTA PARTITIONED BY () LOCATION "" AS SELECT ... -- External table -``` - -```scala -dataframe.write - .format("delta") - .mode("overwrite") - .option("overwriteSchema", "true") - .partitionBy() - .saveAsTable("") // Managed table -dataframe.write - .format("delta") - .mode("overwrite") - .option("overwriteSchema", "true") - .option("path", "") - .partitionBy() - .saveAsTable("") // External table -``` - - - -There are multiple benefits with this approach: - -- Overwriting a table is much faster because it doesn't need to list the - directory recursively or delete any files. -- The old version of the table still exists. If you delete the wrong table you can easily retrieve the old data using [Time Travel](/latest/delta-batch#query-an-older-snapshot-of-a-table-time-travel). -- It's an atomic operation. Concurrent queries can still read the table while you are deleting the table. -- Because of Delta Lake ACID transaction guarantees, if overwriting the table fails, the table will be in its previous state. - -In addition, if you want to delete old files to save storage cost after overwriting the table, you can use [VACUUM](/latest/delta-utility#remove-files-no-longer-referenced-by-a-delta-table) to delete them. It's optimized for file deletion and usually faster than deleting the entire directory. - -## Spark caching - -Databricks does not recommend that you use Spark caching for the following reasons: - -- You lose any data skipping that can come from additional filters added on top of the cached `DataFrame`. - -- The data that gets cached may not be updated if the table is accessed using a different identifier (for example, you do `spark.table(x).cache()` but then write to the table using `spark.write.save(/some/path)`. diff --git a/src/pages/latest/concurrency-control.mdx b/src/pages/latest/concurrency-control.mdx deleted file mode 100644 index 3d51d35..0000000 --- a/src/pages/latest/concurrency-control.mdx +++ /dev/null @@ -1,161 +0,0 @@ ---- -title: Concurrency Control -description: Learn about the ACID transaction guarantees between reads and writes provided by Delta Lake. ---- - -DeltaLake provides ACID transaction guarantees between reads and writes. -This means that: - -- For supported [storage systems](/latest/delta-storage), multiple writers across multiple clusters can simultaneously modify a table partition and see a consistent snapshot view of the table and there will be a serial order for these writes. -- Readers continue to see a consistent snapshot view of the table that the - - Databricks job started with, even when a table is modified during a job. - -- For supported [storage systems](delta-storage.md), multiple writers across - multiple clusters can simultaneously modify a table partition and see a - consistent snapshot view of the table and there will be a serial order for - these writes. -- Readers continue to see a consistent snapshot view of the table that the - ApacheSpark job started with, even when a table is modified during a job. - -In this article: - -- [Optimistic concurrency control](/latest/concurrency-control#optimistic-concurrency-control) - -- [Write conflicts](/latest/concurrency-control#write-conflicts) - -- [Avoid conflicts using partitioning and disjoint command conditions](/latest/concurrency-control#avoid-conflicts-using-partitioning-and-disjoint-command-conditions) - -- [Conflict exceptions](/latest/concurrency-control#conflict-exceptions) - -## Optimistic concurrency control - -DeltaLake uses [optimistic concurrency -control](https://en.wikipedia.org/wiki/Optimistic_concurrency_control) to -provide transactional guarantees between writes. Under this mechanism, writes -operate in three stages: - -1. **Read**: Reads (if needed) the latest available version of the table to - identify which files need to be modified (that is, rewritten). -2. **Write**: Stages all the changes by writing new data files. - -3. **Validate and commit**: Before committing the changes, checks whether the proposed changes conflict - with any other changes that may have been concurrently committed since the - snapshot that was read. If there are no conflicts, all the staged changes are - committed as a new versioned snapshot, and the write operation succeeds. - However, if there are conflicts, the write operation fails with a concurrent - modification exception rather than corrupting the table as would happen with - the write operation on a Parquet table. - -## Write conflicts - -The following table describes which pairs of write operations can conflict. Compaction refers to [file compaction operation](/latest/best-practices#compact-files) written with the option dataChange set to false. - -| | **INSERT** | **UPDATE, DELTE, MERGE INTO** | **OPTIMIZE** | -| ------------------------------ | --------------- | ----------------------------- | ------------ | -| **INSERT** | Cannot conflict | | | -| **UPDATE, DELETE, MERGE INTO** | Can conflict | Can conflict | | -| **COMPACTION** | Cannot conflict | Can conflict | Can conflict | - -## Avoid conflicts using partitioning and disjoint command conditions - -In all cases marked "can conflict", whether the two operations will conflict -depends on whether they operate on the same set of files. You can make the two -sets of files disjoint by partitioning the table by the same columns as those -used in the conditions of the operations. For example, the two commands `UPDATE table WHERE date > '2010-01-01' ...` and `DELETE table WHERE date < '2010-01-01'` will conflict if the table is not partitioned by date, as both can -attempt to modify the same set of files. Partitioning the table by `date` will -avoid the conflict. Hence, partitioning a table according to the conditions -commonly used on the command can reduce conflicts significantly. However, -partitioning a table by a column that has high cardinality can lead to other -performance issues due to large number of subdirectories. - -## Conflict exceptions - -When a transaction conflict occurs, you will observe one of the following -exceptions: - -### ConcurrentAppendException - -This exception occurs when a concurrent operation adds files in the same -partition (or anywhere in an unpartitioned table) that your operation reads. The -file additions can be caused by `INSERT`, `DELETE`, `UPDATE`, or `MERGE` -operations. - -This exception is often thrown during concurrent `DELETE`, `UPDATE`, or `MERGE` -operations. While the concurrent operations may be physically updating different -partition directories, one of them may read the same partition that the other -one concurrently updates, thus causing a conflict. You can avoid this by making -the separation explicit in the operation condition. Consider the following -example. - - - -```scala -// Target 'deltaTable' is partitioned by date and country -deltaTable.as("t").merge( - source.as("s"), - "s.user_id = t.user_id AND s.date = t.date AND s.country = t.country") - .whenMatched().updateAll() - .whenNotMatched().insertAll() - .execute() -``` - - - -Suppose you run the above code concurrently for different dates or countries. -Since each job is working on an independent partition on the target Delta table, -you don't expect any conflicts. However, the condition is not explicit enough -and can scan the entire table and can conflict with concurrent operations -updating any other partitions. Instead, you can rewrite your statement to add -specific date and country to the merge condition, as shown in the following -example. - - - -```scala -// Target 'deltaTable' is partitioned by date and country -deltaTable.as("t").merge( - source.as("s"), - "s.user_id = t.user_id AND s.date = t.date AND s.country = t.country AND t.date = '" + + "' AND t.country = '" + + "'") - .whenMatched().updateAll() - .whenNotMatched().insertAll() - .execute() -``` - - - -This operation is now safe to run concurrently on different dates and countries. - -### ConcurrentDeleteReadException - -This exception occurs when a concurrent operation deleted a file that your -operation read. Common causes are a `DELETE`, `UPDATE`, or `MERGE` operation -that rewrites files. - -### ConcurrentDeleteDeleteException - -This exception occurs when a concurrent operation deleted a file that your -operation also deletes. This could be caused by two concurrent compaction -operations rewriting the same files. - -### MetadataChangedException - -This exception occurs when a concurrent transaction updates the metadata of a -Delta table. Common causes are `ALTER TABLE` operations or writes to your Delta -table that update the schema of the table. - -### ConcurrentTransactionException - -If a streaming query using the same checkpoint location is started multiple -times concurrently and tries to write to the Delta table at the same time. You -should never have two streaming queries use the same checkpoint location and run -at the same time. - -### ProtocolChangedException - -This exception can occur in the following cases: - -- When your Delta table is upgraded to a new version. For future operations to - succeed you may need to upgrade your Delta Lake version. -- When multiple writers are creating or replacing a table at the same time. -- When multiple writers are writing to an empty path at the same time. diff --git a/src/pages/latest/delta-apidoc.mdx b/src/pages/latest/delta-apidoc.mdx deleted file mode 100644 index 1b639bb..0000000 --- a/src/pages/latest/delta-apidoc.mdx +++ /dev/null @@ -1,18 +0,0 @@ ---- -title: Delta Lake APIs -width: full -menu: docs ---- - -For most read and write operations on Delta tables, you can use Apache Spark reader and writer APIs. For examples, see [Table batch reads and writes](/latest/delta-batch) and [Table streaming reads and writes](/latest/delta-streaming). - -However, there are some operations that are specific to DeltaLake and you must use DeltaLake APIs. For examples, see [Table utility commands](/latest/delta-utility). - - - Some DeltaLake APIs are still evolving and are indicated with the **Evolving** - qualifier in the API docs. - - -- [Scala API docs](/latest/api/scala/io/delta/tables/index.html) -- [Java API docs](/latest/api/java/index.html) -- [Python API docs](/latest/api/python/index.html) diff --git a/src/pages/latest/delta-batch.mdx b/src/pages/latest/delta-batch.mdx deleted file mode 100644 index 05c43de..0000000 --- a/src/pages/latest/delta-batch.mdx +++ /dev/null @@ -1,1400 +0,0 @@ ---- -title: Table batch reads and writes -menu: docs ---- - -## Table batch reads and writes - -Delta Lake supports most of the options provided by Apache DataFrame read and write APIs for performing batch reads and writes on tables. - -For many Delta Lake operations on tables, you enable integration with Apache Spark DataSourceV2 and Catalog APIs (since 3.0) by setting configurations when you create a new `SparkSession`. See [Configure SparkSession](#configure-sparksession). - -### Create a table - -Delta Lake supports creating two types of tables --- tables defined in the metastore and tables defined by path. - -To work with metastore-defined tables, you must enable integration with Apache Spark DataSourceV2 and Catalog APIs by setting configurations when you create a new `SparkSession`. See [Configure SparkSesion](#configure-sparksession). - -You can create tables in the following ways. - -- **SQL DDL commands**: You can use standard SQL DDL commands supported in Apache Spark (for example, `CREATE TABLE` and `REPLACE TABLE`) to create Delta tables. - - - -```sql -CREATE TABLE IF NOT EXISTS default.people10m ( - id INT, - firstName STRING, - middleName STRING, - lastName STRING, - gender STRING, - birthDate TIMESTAMP, - ssn STRING, - salary INT -) USING DELTA - -CREATE OR REPLACE TABLE default.people10m ( - id INT, - firstName STRING, - middleName STRING, - lastName STRING, - gender STRING, - birthDate TIMESTAMP, - ssn STRING, - salary INT -) USING DELTA -``` - - - -SQL also supports creating a table at a path, without creating an entry in the Hive metastore. - - - -```sql --- Create or replace table with path -CREATE OR REPLACE TABLE delta.`tmp/delta/people10m` ( - id INT, - firstName STRING, - middleName STRING, - lastName STRING, - gender STRING, - birthDate TIMESTAMP, - ssn STRING, - salary INT -) USING DELTA -``` - - - -- **`DataFrameWriter` API**: If you want to simultaneously create a table and insert data into it from Spark DataFrames or Datasets, you can use the Spark `DataFrameWriter` ([Scala or Java](https://spark.apache.org/docs/latest/api/scala/org/apache/spark/sql/DataFrameWriter.html) and [Python](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql.html#input-and-output)). - - - -```python -# Create table in the metastore using DataFrame's schema and write data to it -df.write.format("delta").saveAsTable("default.people10m") - -# Create or replace partitioned table with path using DataFrame's schema and write/overwrite data to it -df.write.format("delta").mode("overwrite").save("/delta/people10m") -``` - -```scala -// Create table in the metastore using DataFrame's schema and write data to it -df.write.format("delta").saveAsTable("default.people10m") - -// Create table with path using DataFrame's schema and write data to it -df.write.format("delta").mode("overwrite").save("/delta/people10m") -``` - - - -You can also create Delta tables using the Spark `DataFrameWriterV2` API. - -- **`DeltaTableBuilder` API**: You can also use the `DeltaTableBuilder` API in Delta Lake to create tables. Compared to the DataFrameWriter APIs, this API makes it easier to specify additional information like column comments, table properties, and [generated columns](#deltausegeneratedcolumns). - - - -This feature is new and is in Preview. - - - - - -```python -# Create table in the metastore -DeltaTable.createIfNotExists(spark) \ - .tableName("default.people10m") \ - .addColumn("id", "INT") \ - .addColumn("firstName", "STRING") \ - .addColumn("middleName", "STRING") \ - .addColumn("lastName", "STRING", comment = "surname") \ - .addColumn("gender", "STRING") \ - .addColumn("birthDate", "TIMESTAMP") \ - .addColumn("ssn", "STRING") \ - .addColumn("salary", "INT") \ - .execute() - -# Create or replace table with path and add properties -DeltaTable.createOrReplace(spark) \ - .addColumn("id", "INT") \ - .addColumn("firstName", "STRING") \ - .addColumn("middleName", "STRING") \ - .addColumn("lastName", "STRING", comment = "surname") \ - .addColumn("gender", "STRING") \ - .addColumn("birthDate", "TIMESTAMP") \ - .addColumn("ssn", "STRING") \ - .addColumn("salary", "INT") \ - .property("description", "table with people data") \ - .location("/delta/people10m") \ - .execute() -``` - -```scala -// Create table in the metastore -DeltaTable.createOrReplace(spark) - .tableName("default.people10m") - .addColumn("id", "INT") - .addColumn("firstName", "STRING") - .addColumn("middleName", "STRING") - .addColumn( - DeltaTable.columnBuilder("lastName") - .dataType("STRING") - .comment("surname") - .build()) - .addColumn("lastName", "STRING", comment = "surname") - .addColumn("gender", "STRING") - .addColumn("birthDate", "TIMESTAMP") - .addColumn("ssn", "STRING") - .addColumn("salary", "INT") - .execute() - -// Create or replace table with path and add properties -DeltaTable.createOrReplace(spark) - .addColumn("id", "INT") - .addColumn("firstName", "STRING") - .addColumn("middleName", "STRING") - .addColumn( - DeltaTable.columnBuilder("lastName") - .dataType("STRING") - .comment("surname") - .build()) - .addColumn("lastName", "STRING", comment = "surname") - .addColumn("gender", "STRING") - .addColumn("birthDate", "TIMESTAMP") - .addColumn("ssn", "STRING") - .addColumn("salary", "INT") - .property("description", "table with people data") - .location("/delta/people10m") - .execute() -``` - - - -See the [API documentation](/latest/delta-apidoc) for details. - -### Partition data - -You can partition data to speed up queries or DML that have predicates involving the partition columns. To partition data when you create a Delta table, specify a partition by columns. The following example partitions by gender. - - - -```sql --- Create table in the metastore -CREATE TABLE default.people10m ( - id INT, - firstName STRING, - middleName STRING, - lastName STRING, - gender STRING, - birthDate TIMESTAMP, - ssn STRING, - salary INT -) -USING DELTA -PARTITIONED BY (gender) -``` - -```python -df.write.format("delta").partitionBy("gender").saveAsTable("default.people10m") - -DeltaTable.create(spark) \ - .tableName("default.people10m") \ - .addColumn("id", "INT") \ - .addColumn("firstName", "STRING") \ - .addColumn("middleName", "STRING") \ - .addColumn("lastName", "STRING", comment = "surname") \ - .addColumn("gender", "STRING") \ - .addColumn("birthDate", "TIMESTAMP") \ - .addColumn("ssn", "STRING") \ - .addColumn("salary", "INT") \ - .partitionedBy("gender") \ - .execute() -``` - -```scala -df.write.format("delta").partitionBy("gender").saveAsTable("default.people10m") - -DeltaTable.createOrReplace(spark) - .tableName("default.people10m") - .addColumn("id", "INT") - .addColumn("firstName", "STRING") - .addColumn("middleName", "STRING") - .addColumn( - DeltaTable.columnBuilder("lastName") - .dataType("STRING") - .comment("surname") - .build()) - .addColumn("lastName", "STRING", comment = "surname") - .addColumn("gender", "STRING") - .addColumn("birthDate", "TIMESTAMP") - .addColumn("ssn", "STRING") - .addColumn("salary", "INT") - .partitionedBy("gender") - .execute() -``` - - - -To determine whether a table contains a specific partition, use the following statement. If the partition exists, true is returned. - - - -```SQL -SELECT COUNT(*) > 0 AS `Partition exists` FROM default.people10m WHERE gender = "M"; -``` - -```Python -spark.sql("SELECT COUNT(*) > 0 AS `Partition exists` FROM default.people10m WHERE gender = 'M'").show() -``` - -```Scala -spark.sql("SELECT COUNT(*) > 0 AS `Partition exists` FROM default.people10m WHERE gender = 'M'").show() -``` - - - -### Control data location - -For tables defined in the metastore, you can optionally specify the `LOCATION` as a path. Tables created with a specified `LOCATION` are considered unmanaged by the metastore. Unlike a managed table, where no path is specified, an unmanaged table's files are not deleted when you `DROP` the table. - -When you run `CREATE TABLE` with a `LOCATION` that _already_ contains data stored using Delta Lake, Delta Lake does the following: - -- If you specify _only the table name and location_, the table in the metastore automatically inherits the schema, partitioning, and table properties of the existing data. This functionality can be used to "import" data into the metastore. For example: - - - - ```sql - CREATE TABLE default.people10m - USING DELTA - LOCATION '/delta/people10m' - ``` - - - -- If you specify _any configuration_ (schema, partitioning, or table properties), Delta Lake verifies that the specification exactly matches the configuration of the existing data. - - - If the specified configuration does not _exactly_ match the configuration of - the data, Delta Lake throws an exception that describes the discrepancy. - - - - The metastore is not the source of truth about the latest information of a - Delta table. In fact, the table definition in the metastore may not contain - all the metadata like schema and properties. It contains the location of the - table, and the table's transaction log at the location is the source of - truth. If you query the metastore from a system that is not aware of this - Delta-specific customization, you may see incomplete or stale table - information. - - - - -### Use generated columns - - - This feature is new and is in Preview. - - -Delta Lake supports generated columns which are a special type of columns whose values are automatically generated based on a user-specified function over other columns in the Delta table. When you write to a table with generated columns and you do not explicitly provide values for them, Delta Lake automatically computes the values. For example, you can automatically generate a date column (for partitioning the table by date) from the timestamp column; any writes into the table need only specify the data for the timestamp column. However, if you explicitly provide values for them, the values must satisfy the [constraint](https://delta.io/docs/spark/constraints) `( <=> ) IS TRUE` or the write will fail with an error. - - - Tables created with generated columns have a higher table writer protocol - version than the default. See [Table protocol versioning](/latest/versioning) - to understand table protocol versioning and what it means to have a higher - version of a table protocol version. - - -The following example shows how to create a table with generated columns: - - - -```python -DeltaTable.create(spark) \ - .tableName("default.people10m") \ - .addColumn("id", "INT") \ - .addColumn("firstName", "STRING") \ - .addColumn("middleName", "STRING") \ - .addColumn("lastName", "STRING", comment = "surname") \ - .addColumn("gender", "STRING") \ - .addColumn("birthDate", "TIMESTAMP") \ - .addColumn("dateOfBirth", DateType(), generatedAlwaysAs="CAST(birthDate AS DATE)") \ - .addColumn("ssn", "STRING") \ - .addColumn("salary", "INT") \ - .partitionedBy("gender") \ - .execute() -``` - -```scala -DeltaTable.create(spark) - .tableName("default.people10m") - .addColumn("id", "INT") - .addColumn("firstName", "STRING") - .addColumn("middleName", "STRING") - .addColumn( - DeltaTable.columnBuilder("lastName") - .dataType("STRING") - .comment("surname") - .build()) - .addColumn("lastName", "STRING", comment = "surname") - .addColumn("gender", "STRING") - .addColumn("birthDate", "TIMESTAMP") - .addColumn( - DeltaTable.columnBuilder("dateOfBirth") - .dataType(DateType) - .generatedAlwaysAs("CAST(dateOfBirth AS DATE)") - .build()) - .addColumn("ssn", "STRING") - .addColumn("salary", "INT") - .partitionedBy("gender") - .execute() -``` - - - -Generated columns are stored as if they were normal columns. That is, they occupy storage. - -The following restrictions apply to generated columns: - -- A generation expression can use any SQL functions in Spark that always return the same result when given the same argument values, except the following types of functions: - - - User-defined functions. - - Aggregate functions. - - Window functions. - - Functions returning multiple rows. - -- For Delta Lake 1.1.0 and above, `MERGE` operations support generated columns when you set `spark.databricks.delta.schema.autoMerge.enabled` to true. - -Delta Lake may be able to generate partition filters for a query whenever a partition column is defined by one of the following expressions: - -- `CAST(col AS DATE)` and the type of col is `TIMESTAMP`. -- `YEAR(col)` and the type of col is `TIMESTAMP`. -- Two partition columns defined by `YEAR(col)`, `MONTH(col)` and the type of col is `TIMESTAMP`. -- Three partition columns defined by `YEAR(col)`, `MONTH(col)`, `DAY(col)` and the type of col is `TIMESTAMP`. -- Four partition columns defined by `YEAR(col)`, `MONTH(col)`, `DAY(col)`, `HOUR(col)` and the type of col is `TIMESTAMP`. -- `SUBSTRING(col, pos, len)` and the type of col is `STRING` -- `DATE_FORMAT(col, format)` and the type of col is `TIMESTAMP`. - -If a partition column is defined by one of the preceding expressions, and a query filters data using the underlying base column of a generation expression, Delta Lake looks at the relationship between the base column and the generated column, and populates partition filters based on the generated partition column if possible. For example, given the following table: - - - -```Python -DeltaTable.create(spark) \ - .tableName("default.events") \ - .addColumn("eventId", "BIGINT") \ - .addColumn("data", "STRING") \ - .addColumn("eventType", "STRING") \ - .addColumn("eventTime", "TIMESTAMP") \ - .addColumn("eventDate", "DATE", generatedAlwaysAs="CAST(eventTime AS DATE)") \ - .partitionedBy("eventType", "eventDate") \ - .execute() -``` - - - -If you then run the following query: - - - -```python -spark.sql('SELECT * FROM default.events WHERE eventTime >= "2020-10-01 00:00:00" <= "2020-10-01 12:00:00"') -``` - - - -Delta Lake automatically generates a partition filter so that the preceding query only reads the data in partition date=2020-10-01 even if a partition filter is not specified. - -As another example, given the following table: - - - -```python -DeltaTable.create(spark) \ - .tableName("default.events") \ - .addColumn("eventId", "BIGINT") \ - .addColumn("data", "STRING") \ - .addColumn("eventType", "STRING") \ - .addColumn("eventTime", "TIMESTAMP") \ - .addColumn("year", "INT", generatedAlwaysAs="YEAR(eventTime)") \ - .addColumn("month", "INT", generatedAlwaysAs="MONTH(eventTime)") \ - .addColumn("day", "INT", generatedAlwaysAs="DAY(eventTime)") \ - .partitionedBy("eventType", "year", "month", "day") \ - .execute() -``` - - - -If you then run the following query: - - - -```python -spark.sql('SELECT * FROM default.events WHERE eventTime >= "2020-10-01 00:00:00" <= "2020-10-01 12:00:00"') -``` - - - -Delta Lake automatically generates a partition filter so that the preceding query only reads the data in partition `year=2020/month=10/day=01` even if a partition filter is not specified. - -You can use an [EXPLAIN](https://spark.apache.org/docs/latest/sql-ref-syntax-qry-explain.html) clause and check the provided plan to see whether Delta Lake automatically generates any partition filters. - -### Use special characters in column names - -By default, special characters such as spaces and any of the characters `,;{}()\n\t=` are not supported in table column names. To include these special characters in a table’s column name, enable [column mapping](/latest/versioning/#column-mapping). - -### Default table properties - -Delta Lake configurations set in the SparkSession override the default [table properties](#table-properties) for new Delta Lake tables created in the session. The prefix used in the SparkSession is different from the configurations used in the table properties. - -| Delta Lake conf | SparkSession conf | -| --------------- | --------------------------------------------------- | -| `Delta.` | `spark.databricks.delta.properties.defaults.` | - -For example, to set the `delta.appendOnly = true` property for all new Delta Lake tables created in a session, set the following: - - - -```SQL -SET spark.databricks.delta.properties.defaults.appendOnly = true -``` - - - -To modify table properties of existing tables, use [SET TBLPROPERTIES](#table-properties). - -## Read a table - -You can load a Delta table as a DataFrame by specifying a table name or a path: - - - -```sql -SELECT * FROM default.people10m -- query table in the metastore - -SELECT * FROM delta.`/delta/people10m` -- query table by path -``` - -```python -spark.table("default.people10m") # query table in the metastore - -spark.read.format("delta").load("/delta/people10m") # query table by path -``` - -```scala -spark.table("default.people10m") // query table in the metastore - -spark.read.format("delta").load("/delta/people10m") // create table by path - -import io.delta.implicits._ -spark.read.delta("/delta/people10m") -``` - - - -The DataFrame returned automatically reads the most recent snapshot of the table for any query; you never need to run `REFRESH TABLE`. Delta Lake automatically uses partitioning and statistics to read the minimum amount of data when there are applicable predicates in the query. - - - -## Query an older snapshot of a table (time travel) - -Delta Lake time travel allows you to query an older snapshot of a Delta table. Time travel has many use cases, including: - -- Re-creating analyses, reports, or outputs (for example, the output of a machine learning model). This could be useful for debugging or auditing, especially in regulated industries. -- Writing complex temporal queries. -- Fixing mistakes in your data. -- Providing snapshot isolation for a set of queries for fast changing tables. - -This section describes the supported methods for querying older versions of tables, data retention concerns, and provides examples. - - - The timestamp of each version N depends on the timestamp of the log file - corresponding to the version N in Delta table log. Hence, time travel by - timestamp can break if you copy the entire Delta table directory to a new - location. Time travel by version will be unaffected. - - -### Syntax - -This section shows how to query an older version of a Delta table. - -#### SQL `AS OF` syntax - - - -```SQL -SELECT * FROM table_name TIMESTAMP AS OF timestamp_expression -SELECT * FROM table_name VERSION AS OF version -``` - - - -- timestamp_expression can be any one of: - - - `'2018-10-18T22:15:12.013Z'`, that is, a string that can be cast to a timestamp - - `cast('2018-10-18 13:36:32 CEST' as timestamp)` - - `'2018-10-18'`, that is, a date string - - `current_timestamp() - interval 12 hours` - - `date_sub(current_date(), 1)` - - Any other expression that is or can be cast to a timestamp - -- `version` is a long value that can be obtained from the output of `DESCRIBE HISTORY table_spec`. - -Neither `timestamp_expression` nor `version` can be subqueries - -##### Example - - - -```SQL -SELECT * FROM default.people10m TIMESTAMP AS OF '2018-10-18T22:15:12.013Z' -SELECT * FROM delta.`/tmp/delta/people10m` VERSION AS OF 123 -``` - - - -#### DataFrameReader options - -DataFrameReader options allow you to create a DataFrame from a Delta table that is fixed to a specific version of the table. - - - -```python -df1 = spark.read.format("delta").option("timestampAsOf", timestamp_string).load("/delta/people10m") -df2 = spark.read.format("delta").option("versionAsOf", version).load("/delta/people10m") -``` - - - -For `timestamp_string`, only date or timestamp strings are accepted. For example, `"2019-01-01"` and `"2019-01-01T00:00:00.000Z"`. - -A common pattern is to use the latest state of the Delta table throughout the execution of a job to update downstream applications. - -Because Delta tables auto update, a DataFrame loaded from a Delta table may return different results across invocations if the underlying data is updated. By using time travel, you can fix the data returned by the DataFrame across invocations: - - - -```python -history = spark.sql("DESCRIBE HISTORY delta.`/delta/people10m`") -latest_version = history.selectExpr("max(version)").collect() -df = spark.read.format("delta").option("versionAsOf", latest_version[0][0]).load("/delta/people10m") -``` - - - -### Examples - -- Fix accidental deletes to a table for the user `111`: - - - -```python -yesterday = spark.sql("SELECT CAST(date_sub(current_date(), 1) AS STRING)").collect()[0][0] -df = spark.read.format("delta").option("timestampAsOf", yesterday).load("/delta/events") -df.where("userId = 111").write.format("delta").mode("append").save("/delta/events") -``` - - - -- Fix accidental incorrect updates to a table: - - - -```python -yesterday = spark.sql("SELECT CAST(date_sub(current_date(), 1) AS STRING)").collect()[0][0] -df = spark.read.format("delta").option("timestampAsOf", yesterday).load("/delta/events") -df.createOrReplaceTempView("my_table_yesterday") -spark.sql(''' -MERGE INTO delta.`/delta/events` target - USING my_table_yesterday source - ON source.userId = target.userId - WHEN MATCHED THEN UPDATE SET * -''') -``` - - - -- Query the number of new customers added over the last week. - - - -```python -last_week = spark.sql("SELECT CAST(date_sub(current_date(), 7) AS STRING)").collect()[0][0] -df = spark.read.format("delta").option("timestampAsOf", last_week).load("/delta/events") -last_week_count = df.select("userId").distinct().count() -count = spark.read.format("delta").load("/delta/events").select("userId").distinct().count() -new_customers_count = count - last_week_count -``` - - - -### Data retention - -To time travel to a previous version, you must retain _both_ the log and the data files for that version. - -The data files backing a Delta table are _never_ deleted automatically; data files are deleted only when you run [VACUUM](/latest/delta-utility/#remove-files-no-longer-referenced-by-a-delta-table). `VACUUM` _does not_ delete Delta log files; log files are automatically cleaned up after checkpoints are written. - -By default you can time travel to a Delta table up to 30 days old unless you have: - -- Run `VACUUM` on your Delta table. -- Changed the data or log file retention periods using the following [table properties](#table-properties): - - - `delta.logRetentionDuration = "interval "`: controls how long the history for a table is kept. The default is `interval 30 days`. - - Each time a checkpoint is written, Delta Lake automatically cleans up log entries older than the retention interval. If you set this config to a large enough value, many log entries are retained. This should not impact performance as operations against the log are constant time. Operations on history are parallel but will become more expensive as the log size increases. - - - `delta.deletedFileRetentionDuration = "interval "`: controls how long ago a file must have been deleted _before being a candidate for_ `VACUUM`. The default is `interval 7 days`. - - To access 30 days of historical data even if you run `VACUUM` on the Delta table, set `delta.deletedFileRetentionDuration = "interval 30 days"`. This setting may cause your storage costs to go up. - - - Due to log entry cleanup, instances can arise where you cannot time travel to - a version that is less than the retention interval. Delta Lake requires all - consecutive log entries since the previous checkpoint to time travel to a - particular version. For example, with a table initially consisting of log - entries for versions [0, 19] and a checkpoint at version 10, if the log entry - for version 0 is cleaned up, then you cannot time travel to versions [1, 9]. - Increasing the table property `delta.logRetentionDuration` can help avoid - these situations. - - - -## Write to a table - - - -### Append - -To atomically add new data to an existing Delta table, use `append` mode: - - - -```sql -INSERT INTO default.people10m SELECT * FROM morePeople -``` - -```python -df.write.format("delta").mode("append").save("/delta/people10m") -df.write.format("delta").mode("append").saveAsTable("default.people10m") -``` - -```scala -df.write.format("delta").mode("append").save("/delta/people10m") -df.write.format("delta").mode("append").saveAsTable("default.people10m") - -import io.delta.implicits._ -df.write.mode("append").delta("/delta/people10m") -``` - - - -### Overwrite - -To atomically replace all the data in a table, use `overwrite` mode: - - - -```sql -INSERT OVERWRITE TABLE default.people10m SELECT * FROM morePeople -``` - -```python -df.write.format("delta").mode("overwrite").save("/delta/people10m") -df.write.format("delta").mode("overwrite").saveAsTable("default.people10m") -``` - -```scala -df.write.format("delta").mode("overwrite").save("/delta/people10m") -df.write.format("delta").mode("overwrite").saveAsTable("default.people10m") - -import io.delta.implicits._ -df.write.mode("overwrite").delta("/delta/people10m") -``` - - - -Using DataFrames, you can also selectively overwrite only the data that matches an arbitrary expression. This feature is available in Delta Lake 1.1.0 and above. The following command atomically replaces events in January in the target table, which is partitioned by `start_date`, with the data in `df`: - - - -```python -df.write \ - .format("delta") \ - .mode("overwrite") \ - .option("replaceWhere", "start_date >= '2017-01-01' AND end_date <= '2017-01-31'") \ - .save("/delta/events") -``` - -```scala -df.write - .format("delta") - .mode("overwrite") - .option("replaceWhere", "start_date >= '2017-01-01' AND end_date <= '2017-01-31'") - .save("/delta/events") -``` - - - -This sample code writes out the data in `df`, validates that it all matches the predicate, and performs an atomic replacement. If you want to write out data that doesn't all match the predicate, to replace the matching rows in the target table, you can disable the constraint check by setting `spark.databricks.delta.replaceWhere.constraintCheck.enabled` to false: - - - -```python -spark.conf.set("spark.databricks.delta.replaceWhere.constraintCheck.enabled", False) -``` - -```scala -spark.conf.set("spark.databricks.delta.replaceWhere.constraintCheck.enabled", false) -``` - - - -In Delta Lake 1.0.0 and below, `replaceWhere` overwrites data matching a predicate over partition columns only. The following command atomically replaces the month in January in the target table, which is partitioned by `date`, with the data in `df`: - - - -```python -df.write \ - .format("delta") \ - .mode("overwrite") \ - .option("replaceWhere", "birthDate >= '2017-01-01' AND birthDate <= '2017-01-31'") \ - .save("/delta/people10m") -``` - -```scala -df.write - .format("delta") - .mode("overwrite") - .option("replaceWhere", "birthDate >= '2017-01-01' AND birthDate <= '2017-01-31'") - .save("/delta/people10m") -``` - - - -In Delta Lake 1.1.0 and above, if you want to fall back to the old behavior, you can disable the `spark.databricks.delta.replaceWhere.dataColumns.enabled` flag: - - - -```python -spark.conf.set("spark.databricks.delta.replaceWhere.dataColumns.enabled", False) -``` - -```scala -spark.conf.set("spark.databricks.delta.replaceWhere.dataColumns.enabled", false) -``` - - - -#### Dynamic Partition Overwrites - -Delta Lake 2.0 and above supports _dynamic_ partition overwrite mode for partitioned tables. - -When in dynamic partition overwrite mode, we overwrite all existing data in each logical partition for which the write will commit new data. Any existing logical partitions for which the write does not contain data will remain unchanged. This mode is only applicable when data is being written in overwrite mode: either `INSERT OVERWRITE` in SQL, or a DataFrame write with `df.write.mode("overwrite")`. - -Configure dynamic partition overwrite mode by setting the Spark session configuration `spark.sql.sources.partitionOverwriteMode` to `dynamic`. You can also enable this by setting the `DataFrameWriter` option `partitionOverwriteMode` to `dynamic`. If present, the query-specific option overrides the mode defined in the session configuration. The default for `partitionOverwriteMode` is `static`. - - - -```SQL -SET spark.sql.sources.partitionOverwriteMode=dynamic; -INSERT OVERWRITE TABLE default.people10m SELECT * FROM morePeople; -``` - -```python -df.write \ - .format("delta") \ - .mode("overwrite") \ - .option("partitionOverwriteMode", "dynamic") \ - .saveAsTable("default.people10m") -``` - -```scala -df.write - .format("delta") - .mode("overwrite") - .option("partitionOverwriteMode", "dynamic") - .saveAsTable("default.people10m") -``` - - - - -Dynamic partition overwrite conflicts with the option replaceWhere for partitioned tables. - -If dynamic partition overwrite is enabled in the Spark session configuration, and `replaceWhere` is provided as a `DataFrameWriter` option, then Delta Lake overwrites the data according to the `replaceWhere` expression (query-specific options override session configurations). - -You’ll receive an error if the `DataFrameWriter` options have both dynamic partition overwrite and `replaceWhere` enabled. - - - - -Validate that the data written with dynamic partition overwrite touches only the expected partitions. A single row in the incorrect partition can lead to unintentionally overwriting an entire partition. We recommend using `replaceWhere` to specify which data to overwrite. - -If a partition has been accidentally overwritten, you can use [Restore a Delta table to an earlier state](/latest/delta-utility/#restore-a-delta-table-to-an-earlier-state) to undo the change. - - - -For Delta Lake support for updating tables, see [Table deletes, updates, and merges](/latest/delta-update). - -### Limit rows written in a file - -You can use the SQL session configuration `spark.sql.files.maxRecordsPerFile` to specify the maximum number of records to write to a single file for a Delta Lake table. Specifying a value of zero or a negative value represents no limit. - -You can also use the `DataFrameWriter` option `maxRecordsPerFile` when using the DataFrame APIs to write to a Delta Lake table. When `maxRecordsPerFile` is specified, the value of the SQL session configuration `spark.sql.files.maxRecordsPerFile` is ignored. - - - -```python -df.write.format("delta") \ - .mode("append") \ - .option("maxRecordsPerFile", "10000") \ - .save("/tmp/delta/people10m") -``` - -```scala -df.write.format("delta") - .mode("append") - .option("maxRecordsPerFile", "10000") - .save("/tmp/delta/people10m") -``` - - - -### Idempotent writes - -Sometimes a job that writes data to a Delta table is restarted due to various reasons (for example, job encounters a failure). The failed job may or may not have written the data to Delta table before terminating. In the case where the data is written to the Delta table, the restarted job writes the same data to the Delta table which results in duplicate data. - -To address this, Delta tables support the following `DataFrameWriter` options to make the writes idempotent: - -- `txnAppId`: A unique string that you can pass on each `DataFrame` write. For example, this can be the name of the job. -- `txnVersion`: A monotonically increasing number that acts as transaction version. This number needs to be unique for data that is being written to the Delta table(s). For example, this can be the epoch seconds of the instant when the query is attempted for the first time. Any subsequent restarts of the same job needs to have the same value for `txnVersion`. - -The above combination of options needs to be unique for each new data that is being ingested into the Delta table and the `txnVersion` needs to be higher than the last data that was ingested into the Delta table. For example: - -- Last successfully written data contains option values as `dailyETL:23423` (`txnAppId:txnVersion`). -- Next write of data should have `txnAppId = dailyETL` and `txnVersion` as at least `23424` (one more than the last written data `txnVersion`). -- Any attempt to write data with `txnAppId = dailyETL` and `txnVersion` as `23422` or less is ignored because the `txnVersion` is less than the last recorded `txnVersion` in the table. -- Attempt to write data with `txnAppId:txnVersion` as `anotherETL:23424` is successful writing data to the table as it contains a different `txnAppId` compared to the same option value in last ingested data. - - - This solution assumes that the data being written to Delta table(s) in - multiple retries of the job is same. If a write attempt in a Delta table - succeeds but due to some downstream failure there is a second write attempt - with same txn options but different data, then that second write attempt will - be ignored. This can cause unexpected results. - - -#### Example - - - -```Python -app_id = ... # A unique string that is used as an application ID. -version = ... # A monotonically increasing number that acts as transaction version. - -dataFrame.write.format(...).option("txnVersion", version).option("txnAppId", app_id).save(...) -``` - -```scala -val appId = ... // A unique string that is used as an application ID. -version = ... // A monotonically increasing number that acts as transaction version. - -dataFrame.write.format(...).option("txnVersion", version).option("txnAppId", appId).save(...) -``` - - - -### Set user-defined commit metadata - -You can specify user-defined strings as metadata in commits made by these operations, either using the DataFrameWriter option `userMetadata` or the SparkSession configuration `spark.databricks.delta.commitInfo.userMetadata`. If both of them have been specified, then the option takes preference. This user-defined metadata is readable in the [history](/latest/delta-utility/#delta-history) operation. - - - -```sql -SET spark.databricks.delta.commitInfo.userMetadata=overwritten-for-fixing-incorrect-data -INSERT OVERWRITE default.people10m SELECT * FROM morePeople -``` - -```python -df.write.format("delta") \ - .mode("overwrite") \ - .option("userMetadata", "overwritten-for-fixing-incorrect-data") \ - .save("/delta/people10m") -``` - -```scala -df.write.format("delta") - .mode("overwrite") - .option("userMetadata", "overwritten-for-fixing-incorrect-data") - .save("/delta/people10m") -``` - - - - - -## Schema validation - -Delta Lake automatically validates that the schema of the DataFrame being written is compatible with the schema of the table. Delta Lake uses the following rules to determine whether a write from a DataFrame to a table is compatible: - -- All DataFrame columns must exist in the target table. If there are columns in the DataFrame not present in the table, an exception is raised. Columns present in the table but not in the DataFrame are set to null. -- DataFrame column data types must match the column data types in the target table. If they don't match, an exception is raised. -- DataFrame column names cannot differ only by case. This means that you cannot have columns such as "Foo" and "foo" defined in the same table. While you can use Spark in case sensitive or insensitive (default) mode, Parquet is case sensitive when storing and returning column information. Delta Lake is case-preserving but insensitive when storing the schema and has this restriction to avoid potential mistakes, data corruption, or loss issues. - -Delta Lake support DDL to add new columns explicitly and the ability to update schema automatically. - -If you specify other options, such as `partitionBy`, in combination with append mode, Delta Lake validates that they match and throws an error for any mismatch. When `partitionBy` is not present, appends automatically follow the partitioning of the existing data. - - - -## Update table schema - -Delta Lake lets you update the schema of a table. The following types of changes are supported: - -- Adding new columns (at arbitrary positions) -- Reordering existing columns - -You can make these changes explicitly using DDL or implicitly using DML. - - - When you update a Delta table schema, streams that read from that table - terminate. If you want the stream to continue you must restart it. - - -### Explicitly update schema - -You can use the following DDL to explicitly change the schema of a table. - -#### Add columns - - - -```sql -ALTER TABLE table_name ADD COLUMNS (col_name data_type [COMMENT col_comment] [FIRST|AFTER colA_name], ...) -``` - - - -By default, nullability is `true`. - -To add a column to a nested field, use: - - - -```sql -ALTER TABLE table_name ADD COLUMNS (col_name.nested_col_name data_type [COMMENT col_comment] [FIRST|AFTER colA_name], ...) -``` - - - -##### Example - -If the schema before running `ALTER TABLE boxes ADD COLUMNS (colB.nested STRING AFTER field1)` is: - -``` -- root -| - colA -| - colB -| +-field1 -| +-field2 -``` - -the schema after is: - -``` -- root -| - colA -| - colB -| +-field1 -| +-nested -| +-field2 -``` - - - Adding nested columns is supported only for structs. Arrays and maps are not - supported. - - -#### Change column comment or ordering - -```sql -ALTER TABLE table_name CHANGE [COLUMN] col_name col_name data_type [COMMENT col_comment] [FIRST|AFTER colA_name] -``` - -To change a column in a nested field, use: - -```sql -ALTER TABLE table_name CHANGE [COLUMN] col_name.nested_col_name nested_col_name data_type [COMMENT col_comment] [FIRST|AFTER colA_name] -``` - -##### Example - -If the schema before running `ALTER TABLE boxes CHANGE COLUMN colB.field2 field2 STRING FIRST` is: - -``` -- root -| - colA -| - colB -| +-field1 -| +-field2 -``` - -the schema after is: - -``` -- root -| - colA -| - colB -| +-field2 -| +-field1 -``` - -#### Replace columns - -```sql -ALTER TABLE table_name REPLACE COLUMNS (col_name1 col_type1 [COMMENT col_comment1], ...) -``` - -##### Example - -When running the following DSL: - -```sql -ALTER TABLE boxes REPLACE COLUMNS (colC STRING, colB STRUCT, colA STRING) -``` - -if the schema before is: - -``` -- root -| - colA -| - colB -| +-field1 -| +-field2 -``` - -the schema after is: - -``` -- root -| - colC -| - colB -| +-field2 -| +-nested -| +-field1 -| - colA -``` - -#### Rename columns - - - This feature is available in Delta Lake 1.2.0 and above. This feature is - currently experimental. - - -To rename columns without rewriting any of the columns’ existing data, you must enable column mapping for the table. See [enable column mapping](/latest/versioning/#column-mapping). - -To rename a column: - -```SQL -ALTER TABLE table_name RENAME COLUMN old_col_name TO new_col_name -``` - -To rename a nested field: - -```SQL -ALTER TABLE table_name RENAME COLUMN col_name.old_nested_field TO new_nested_field -``` - -##### Example - -When you run the following command: - -```SQL -ALTER TABLE boxes RENAME COLUMN colB.field1 TO field001 -``` - -If the schema before is: - -``` -- root -| - colA -| - colB -| +-field1 -| +-field2 -``` - -Then the schema after is: - -``` -- root -| - colA -| - colB -| +-field001 -| +-field2 -``` - -#### Drop columns - - - This feature is available in Delta Lake 2.0 and above. This feature is - currently experimental. - - -To drop columns as a metadata-only operation without rewriting any data files, you must enable column mapping for the table. See [enable column mapping](/latest/versioning/#column-mapping). - - - Dropping a column from metadata does not delete the underlying data for the - column in files. - - -To drop a column: - -```SQL -ALTER TABLE table_name DROP COLUMN col_name -``` - -To drop multiple columns: - -```SQL -ALTER TABLE table_name DROP COLUMNS (col_name_1, col_name_2) -``` - -#### Change column type or name - -Changing a column's type or name or dropping a column requires rewriting the table. To do this, use the `overwriteSchema` option: - -##### Change a column type - -```python -spark.read.table(...) \ - .withColumn("birthDate", col("birthDate").cast("date")) \ - .write \ - .format("delta") \ - .mode("overwrite") \ - .option("overwriteSchema", "true") \ - .saveAsTable(...) -``` - -##### Change a column name - -```python -spark.read.table(...) \ - .withColumnRenamed("dateOfBirth", "birthDate") \ - .write \ - .format("delta") \ - .mode("overwrite") \ - .option("overwriteSchema", "true") \ - .saveAsTable(...) -``` - - - -### Automatic schema update - -Delta Lake can automatically update the schema of a table as part of a DML transaction (either appending or overwriting), and make the schema compatible with the data being written. - -#### Add columns - -Columns that are present in the DataFrame but missing from the table are automatically added as part of a write transaction when: - -- `write` or `writeStream` have `.option("mergeSchema", "true")` - -- `spark.databricks.delta.schema.autoMerge.enabled` is `true` - -When both options are specified, the option from the `DataFrameWriter` takes precedence. The added columns are appended to the end of the struct they are present in. Case is preserved when appending a new column. - -#### `NullType` columns - -Because Parquet doesn't support `NullType`, `NullType` columns are dropped from the DataFrame when writing into Delta tables, but are still stored in the schema. When a different data type is received for that column, Delta Lake merges the schema to the new data type. If Delta Lake receives a `NullType` for an existing column, the old schema is retained and the new column is dropped during the write. - -`NullType` in streaming is not supported. Since you must set schemas when using streaming this should be very rare. `NullType` is also not accepted for complex types such as `ArrayType` and `MapType`. - -## Replace table schema - -By default, overwriting the data in a table does not overwrite the schema. When overwriting a table using `mode("overwrite")` without `replaceWhere`, you may still want to overwrite the schema of the data being written. You replace the schema and partitioning of the table by setting the `overwriteSchema` option to `true`: - -```python -df.write.option("overwriteSchema", "true") -``` - -## Views on tables - -Delta Lake supports the creation of views on top of Delta tables just like you might with a data source table. - -The core challenge when you operate with views is resolving the schemas. If you alter a Delta table schema, you must recreate derivative views to account for any additions to the schema. For instance, if you add a new column to a Delta table, you must make sure that this column is available in the appropriate views built on top of that base table. - -## Table properties - -You can store your own metadata as a table property using TBLPROPERTIES in CREATE and ALTER. You can then SHOW that metadata. For example: - - -```SQL -ALTER TABLE default.people10m SET TBLPROPERTIES ('department' = 'accounting', 'delta.appendOnly' = 'true'); - --- Show the table's properties. -SHOW TBLPROPERTIES default.people10m; - --- Show just the 'department' table property. -SHOW TBLPROPERTIES default.people10m ('department'); - -```` - - -`TBLPROPERTIES` are stored as part of Delta table metadata. You cannot define new `TBLPROPERTIES` in a `CREATE` statement if a Delta table already exists in a given location. - -In addition, to tailor behavior and performance, Delta Lake supports certain Delta table properties: - -- Block deletes and updates in a Delta table: delta.appendOnly=true. -- Configure the [time travel](/latest/delta-batch/#deltatimetravel) retention properties: `delta.logRetentionDuration=` and `delta.deletedFileRetentionDuration=`. For details, see [Data retention](/latest/delta-batch/#data-retention). -- Configure the number of columns for which statistics are collected: `delta.dataSkippingNumIndexedCols=n`. This property indicates to the writer that statistics are to be collected only for the first n columns in the table. Also the data skipping code ignores statistics for any column beyond this column index. This property takes affect only for new data that is written out. - - -Modifying a Delta table property is a write operation that will conflict with other [concurrent write operations](/latest/concurrency-control), causing them to fail. We recommend that you modify a table property only when there are no concurrent write operations on the table. - - -You can also set `delta.`-prefixed properties during the first commit to a Delta table using Spark configurations. For example, to initialize a Delta table with the property `delta.appendOnly=true`, set the Spark configuration `spark.databricks.delta.properties.defaults.appendOnly` to `true`. For example: - - - -```SQL -SET spark.databricks.delta.properties.defaults.appendOnly = true -```` - -```python -spark.conf.set("spark.databricks.delta.properties.defaults.appendOnly", "true") -``` - -```scala -spark.conf.set("spark.databricks.delta.properties.defaults.appendOnly", "true") -``` - - - -See also the [Delta table properties reference](/latest/table-properties). - -## Table metadata - -Delta Lake has rich features for exploring table metadata. -It supports `DESCRIBE TABLE`, `SHOW COLUMNS`, `SHOW PARTITIONS`, and so on. -It also provides the following unique commands: - -- `DESCRIBE DETAIL` -- `DESCRIBE HISTORY` - -### `DESCRIBE DETAIL` - -Provides information about schema, partitioning, table size, and so on. For details, see [delta details](/latest/delta-utility/#delta-detail). - -### `DESCRIBE HISTORY` - -Provides provenance information, including the operation, user, and so on, and operation metrics for each write to a table. Table history is retained for 30 days. For details, see [delta history](/latest/delta-utility/#delta-history). - -## Configure SparkSession - -For many Delta Lake operations, you enable integration with Apache Spark DataSourceV2 and Catalog APIs (since 3.0) by setting the following configurations when you create a new `SparkSession`. - - - -```python -from pyspark.sql import SparkSession - -spark = SparkSession \ - .builder \ - .appName("...") \ - .master("...") \ - .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \ - .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") \ - .getOrCreate() -``` - -```scala -import org.apache.spark.sql.SparkSession - -val spark = SparkSession - .builder() - .appName("...") - .master("...") - .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") - .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") - .getOrCreate() -``` - -```java -import org.apache.spark.sql.SparkSession; - -SparkSession spark = SparkSession - .builder() - .appName("...") - .master("...") - .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") - .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") - .getOrCreate(); -``` - - - -Alternatively, you can add configurations when submitting your Spark application using `spark-submit` or when starting `spark-shell` or `pyspark` by specifying them as command-line parameters. - -```bash -spark-submit --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog" ... -``` - -```bash -pyspark --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog" -``` - -## Configure storage credentials - -Delta Lake uses Hadoop FileSystem APIs to access the storage systems. The credentails for storage systems usually can be set through Hadoop configurations. Delta Lake provides multiple ways to set Hadoop configurations similar to Apache Spark. - -### Spark configurations - -When you start a Spark application on a cluster, you can set the Spark configurations in the form of `spark.hadoop.*` to pass your custom Hadoop configurations. For example, Setting a value for `spark.hadoop.a.b.c` will pass the value as a Hadoop configuration `a.b.c`, and Delta Lake will use it to access Hadoop FileSystem APIs. - -See [Spark docs](http://spark.apache.org/docs/latest/configuration.html#custom-hadoophive-configuration) for more details. - -### SQL session configurations - -Spark SQL will pass all of the current [SQL session configurations](http://spark.apache.org/docs/latest/configuration.html#runtime-sql-configuration) to Delta Lake, and Delta Lake will use them to access Hadoop FileSystem APIs. For example, `SET a.b.c=x.y.z` will tell Delta Lake to pass the value `x.y.z` as a Hadoop configuration `a.b.c`, and Delta Lake will use it to access Hadoop FileSystem APIs. - - - -### DataFrame options - -Besides setting Hadoop file system configurations through the Spark (cluster) configurations or SQL session configurations, Delta supports reading Hadoop file system configurations from `DataFrameReader` and `DataFrameWriter` options (that is, option keys that start with the `fs.` prefix) when the table is read or written, by using `DataFrameReader.load(path)` or `DataFrameWriter.save(path)`. - -For example, you can pass your storage credentails through DataFrame options: - - - -```python -df1 = spark.read.format("delta") \ - .option("fs.azure.account.key..dfs.core.windows.net", "") \ - .read("...") -df2 = spark.read.format("delta") \ - .option("fs.azure.account.key..dfs.core.windows.net", "") \ - .read("...") -df1.union(df2).write.format("delta") \ - .mode("overwrite") \ - .option("fs.azure.account.key..dfs.core.windows.net", "") \ - .save("...") -``` - -```scala -val df1 = spark.read.format("delta") - .option("fs.azure.account.key..dfs.core.windows.net", "") - .read("...") -val df2 = spark.read.format("delta") - .option("fs.azure.account.key..dfs.core.windows.net", "") - .read("...") -df1.union(df2).write.format("delta") - .mode("overwrite") - .option("fs.azure.account.key..dfs.core.windows.net", "") - .save("...") -``` - - - -You can find the details of the Hadoop file system configurations for your storage in [Storage configuration](/latest/delta-storage). diff --git a/src/pages/latest/delta-change-data-feed.mdx b/src/pages/latest/delta-change-data-feed.mdx deleted file mode 100644 index 94fb89d..0000000 --- a/src/pages/latest/delta-change-data-feed.mdx +++ /dev/null @@ -1,233 +0,0 @@ ---- -title: Change Data Feed -description: Learn about the Delta Lake change data feed. ---- - - - This feature is available in Delta Lake 2.0.0 and above. This feature is in experimental support mode. - - -Change Data Feed (CDF) feature allows Delta tables to track row-level changes between versions of a Delta table. When enabled on a Delta table, the runtime records “change events” for all the data written into the table. This includes the row data along with metadata indicating whether the specified row was inserted, deleted, or updated. - -You can read the change events in batch queries using DataFrame APIs (that is, `df.read`) and in streaming queries using DataFrame APIs (that is, `df.readStream`). - -## Use Cases - -Change Data Feed is not enabled by default. The following use cases should drive when you enable the change data feed. - -- **Silver and Gold tables**: Improve Delta performance by processing only row-level changes following initial MERGE, UPDATE, or DELETE operations to accelerate and simplify ETL and ELT operations. -- **Transmit changes**: Send a change data feed to downstream systems such as Kafka or RDBMS that can use it to incrementally process in later stages of data pipelines. -- **Audit trail table**: Capture the change data feed as a Delta table provides perpetual storage and efficient query capability to see all changes over time, including when deletes occur and what updates were made. - -## Enable change data feed - -You must explicitly enable the change data feed option using one of the following methods: - -- **New table**: Set the table property `delta.enableChangeDataFeed = true` in the `CREATE TABLE` command. - - - - ```SQL - CREATE TABLE student (id INT, name STRING, age INT) TBLPROPERTIES (delta.enableChangeDataFeed = true); - ``` - - - -- **Existing table**: Set the table property `delta.enableChangeDataFeed = true` in the `ALTER TABLE` command. - - - - ```SQL - ALTER TABLE myDeltaTable SET TBLPROPERTIES (delta.enableChangeDataFeed = true); - ``` - - - - -- **All new tables**: - - - - ```SQL - SET spark.databricks.delta.properties.defaults.enableChangeDataFeed = true; - ``` - - - - - - -- Once you enable the change data feed option for a table, you can no longer write to the table using Delta Lake 1.2.1 or below. You can always read the table. - -Only changes made _after_ you enable the change data feed are recorded; past changes to a table are not captured. - - - - -### Change data storage - -Delta Lake records change data for `UPDATE`, `DELETE`, and `MERGE` operations in the `_change_data` folder under the Delta table directory. These records may be skipped when Delta Lake detects it can efficiently compute the change data feed directly from the transaction log. In particular, insert-only operations and full partition deletes will not generate data in the `_change_data` directory. - -The files in the `_change_data` folder follow the retention policy of the table. Therefore, if you run the [VACUUM](/latest/delta-utility/#remove-files-no-longer-referenced-by-a-delta-table) command, change data feed data is also deleted. - - -## Read changes in batch queries - -You can provide either version or timestamp for the start and end. The start and end versions and timestamps are inclusive in the queries. To read the changes from a particular start version to the _latest_ version of the table, specify only the starting version or timestamp. - -You specify a version as an integer and a timestamps as a string in the format `yyyy-MM-dd[ HH:mm:ss[.SSS]]`. - -If you provide a version lower or timestamp older than one that has recorded change events, that is, when the change data feed was enabled, an error is thrown indicating that the change data feed was not enabled. - - - -```python -# version as ints or longs -spark.read.format("delta") \ - .option("readChangeFeed", "true") \ - .option("startingVersion", 0) \ - .option("endingVersion", 10) \ - .table("myDeltaTable") - -# timestamps as formatted timestamp -spark.read.format("delta") \ - .option("readChangeFeed", "true") \ - .option("startingTimestamp", '2021-04-21 05:45:46') \ - .option("endingTimestamp", '2021-05-21 12:00:00') \ - .table("myDeltaTable") - -# providing only the startingVersion/timestamp -spark.read.format("delta") \ - .option("readChangeFeed", "true") \ - .option("startingVersion", 0) \ - .table("myDeltaTable") - - -# path based tables -spark.read.format("delta") \ - .option("readChangeFeed", "true") \ - .option("startingTimestamp", '2021-04-21 05:45:46') \ - .load("pathToMyDeltaTable") -``` - -```scala -// version as ints or longs -spark.read.format("delta") - .option("readChangeFeed", "true") - .option("startingVersion", 0) - .option("endingVersion", 10) - .table("myDeltaTable") - -// timestamps as formatted timestamp -spark.read.format("delta") - .option("readChangeFeed", "true") - .option("startingTimestamp", "2021-04-21 05:45:46") - .option("endingTimestamp", "2021-05-21 12:00:00") - .table("myDeltaTable") - -// providing only the startingVersion/timestamp -spark.read.format("delta") - .option("readChangeFeed", "true") - .option("startingVersion", 0) - .table("myDeltaTable") - -// path based tables -spark.read.format("delta") - .option("readChangeFeed", "true") - .option("startingTimestamp", "2021-04-21 05:45:46") - .load("pathToMyDeltaTable") - -``` - - - -## Read changes in streaming queries - - - -```python -# providing a starting version -spark.readStream.format("delta") \ - .option("readChangeFeed", "true") \ - .option("startingVersion", 0) \ - .table("myDeltaTable") - -# providing a starting timestamp -spark.readStream.format("delta") \ - .option("readChangeFeed", "true") \ - .option("startingTimestamp", "2021-04-21 05:35:43") \ - .load("/pathToMyDeltaTable") - -# not providing a starting version/timestamp will result in the latest snapshot being fetched first -spark.readStream.format("delta") \ - .option("readChangeFeed", "true") \ - .table("myDeltaTable") -``` - -```scala -// providing a starting version -spark.readStream.format("delta") - .option("readChangeFeed", "true") - .option("startingVersion", 0) - .table("myDeltaTable") - -// providing a starting timestamp -spark.readStream.format("delta") - .option("readChangeFeed", "true") - .option("startingVersion", "2021-04-21 05:35:43") - .load("/pathToMyDeltaTable") - -// not providing a starting version/timestamp will result in the latest snapshot being fetched first -spark.readStream.format("delta") - .option("readChangeFeed", "true") - .table("myDeltaTable") -``` - - - -To get the change data while reading the table, set the option `readChangeFeed` to `true`. The `startingVersion` or `startingTimestamp` are optional and if not provided the stream returns the latest snapshot of the table at the time of streaming as an `INSERT` and future changes as change data. Options like rate limits (`maxFilesPerTrigger`, `maxBytesPerTrigger`) and `excludeRegex` are also supported when reading change data. - - -Rate limiting can be atomic for versions other than the starting snapshot version. That is, the entire commit version will be rate limited or the entire commit will be returned. - -By default if a user passes in a version or timestamp exceeding the last commit on a table, the error `timestampGreaterThanLatestCommit` will be thrown. CDF can handle the out of range version case, if the user sets the following configuration to `true`. - - - -```SQL -set spark.databricks.delta.changeDataFeed.timestampOutOfRange.enabled = true; -```` - - - -If you provide a start version greater than the last commit on a table or a start timestamp newer than the last commit on a table, then when the preceding configuration is enabled, an empty read result is returned. - -If you provide an end version greater than the last commit on a table or an end timestamp newer than the last commit on a table, then when the preceding configuration is enabled in batch read mode, all changes between the start version and the last commit are be returned. - - - -## Change data event schema - -In addition to the data columns, change data contains metadata columns that identify the type of change event: - -| Column name | Type | Values | -| - | - | - | -| `_change_type` | String | `insert`, `update_preimage` , `update_postimage`, `delete` (1) | -| `_commit_version` | Long | The Delta log or table version containing the change. | -| `_commit_timestamp` | Timestamp | The timestamp associated when the commit was created. | - -**(1)** preimage is the value before the update, postimage is the value after the update. - -## Frequently asked questions (FAQ) - -### What is the overhead of enabling the change data feed? -There is no significant impact. The change data records are generated in line during the query execution process, and are generally much smaller than the total size of rewritten files. - -### What is the retention policy for change records? -Change records follow the same retention policy as out-of-date table versions, and will be cleaned up through `VACUUM` if they are outside the specified retention period. - -### When do new records become available in the change data feed? -Change data is committed along with the Delta Lake transaction, and will become available at the same time as the new data is available in the table. - -## Known limitations -In Delta Lake 2.0.0, CDF reads for both batch and streaming are explicitly blocked on tables with column mapping enabled. In Delta Lake 2.1.0, CDF batch reads are supported on tables with [column mapping](/latest/versioning/#column-mapping) enabled when `DROP COLUMN` and `RENAME COLUMN` have not been used. \ No newline at end of file diff --git a/src/pages/latest/delta-column-mapping.mdx b/src/pages/latest/delta-column-mapping.mdx deleted file mode 100644 index 1eb5b8c..0000000 --- a/src/pages/latest/delta-column-mapping.mdx +++ /dev/null @@ -1,58 +0,0 @@ ---- -title: Delta Column Mapping -description: Learn about column mapping in Delta. -menu: docs ---- - -Delta Lake supports column mapping, which allows Delta table columns and the -corresponding Parquet columns to use different names. Column mapping enables -Delta schema evolution operations such as `RENAME COLUMN` on a Delta table -without the need to rewrite the underlying Parquet files. It also allows users -to name Delta table columns by using characters that are not allowed by Parquet, -such as spaces, so that users can directly ingest CSV or JSON data into Delta -without the need to rename columns due to previous character constraints. - -## Requirements - -- DBR 10.2 or above. -- Column mapping requires the Delta [table version](versioning.md) to be reader - version 2 and writer version 5. For a Delta table with the required table - version, you can enable column mapping by setting `delta.columnMappingMode` to - `name`. You can upgrade the table version and enable column mapping by using a - single `ALTER TABLE` command: - - - -```sql -ALTER TABLE SET TBLPROPERTIES ( -'delta.minReaderVersion' = '2', -'delta.minWriterVersion' = '5', -'delta.columnMapping.mode' = 'name' -) -``` - - - - - After you set these properties in the table, you can only read from and write - to this Delta table by using DBR 10.2 and above. - - -## Supported characters in column names - -When column mapping is enabled for a Delta table, you can include spaces as well -as any of these characters in the table's column names: `,;{}()\n\t=` . - -## Rename a column - -When column mapping is enabled for a Delta table, you can rename a column: - - - -```sql -ALTER TABLE RENAME COLUMN old_col_name TO new_col_name -``` - - - -For more examples, see [\_](/delta/delta-batch.md#rename-columns). diff --git a/src/pages/latest/delta-constraints.mdx b/src/pages/latest/delta-constraints.mdx deleted file mode 100644 index 570b5b4..0000000 --- a/src/pages/latest/delta-constraints.mdx +++ /dev/null @@ -1,78 +0,0 @@ ---- -title: Constraints -menu: docs ---- - -Delta tables support standard SQL constraint management clauses that ensure that the quality and integrity of data added to a table is automatically verified. When a constraint is violated, Delta Lake throws an InvariantViolationException to signal that the new data can’t be added. - - -Adding a constraint automatically upgrades the table writer protocol version. See [Table protocol versioning](/latest/versioning) to understand table protocol versioning and what it means to upgrade the protocol version. - - -Two types of constraints are supported: - -- `NOT NULL`: indicates that values in specific columns cannot be null. -- `CHECK`: indicates that a specified Boolean expression must be true for each input row. - -## NOT NULL constraint - -You specify `NOT NULL` constraints in the schema when you create a table and drop `NOT NULL` constraints using the `ALTER TABLE CHANGE COLUMN` command. - - - -```sql -> CREATE TABLE default.people10m ( - id INT NOT NULL, - firstName STRING, - middleName STRING NOT NULL, - lastName STRING, - gender STRING, - birthDate TIMESTAMP, - ssn STRING, - salary INT - ) USING DELTA; - -> ALTER TABLE default.people10m CHANGE COLUMN middleName DROP NOT NULL; -``` - - - -If you specify a `NOT NULL` constraint on a column nested within a struct, the parent struct is also constrained to not be `null`. However, columns nested within array or map types do not accept `NOT NULL` constraints. - -## CHECK constraint - -You manage `CHECK` constraints using the `ALTER TABLE ADD CONSTRAINT` and `ALTER TABLE DROP CONSTRAINT` commands. `ALTER TABLE ADD CONSTRAINT` verifies that all existing rows satisfy the constraint before adding it to the table. - - - -```sql -> CREATE TABLE default.people10m ( - id INT, - firstName STRING, - middleName STRING, - lastName STRING, - gender STRING, - birthDate TIMESTAMP, - ssn STRING, - salary INT - ) USING DELTA; - -> ALTER TABLE default.people10m ADD CONSTRAINT dateWithinRange CHECK (birthDate > '1900-01-01'); -> ALTER TABLE default.people10m DROP CONSTRAINT dateWithinRange; -``` - - - -`CHECK` constraints are table properties in the output of the `DESCRIBE DETAIL` and `SHOW TBLPROPERTIES` commands. - - - -```sql -> ALTER TABLE default.people10m ADD CONSTRAINT validIds CHECK (id > 1 and id < 99999999); - -> DESCRIBE DETAIL default.people10m; - -> SHOW TBLPROPERTIES default.people10m; -``` - - diff --git a/src/pages/latest/delta-faq.mdx b/src/pages/latest/delta-faq.mdx deleted file mode 100644 index ad99aab..0000000 --- a/src/pages/latest/delta-faq.mdx +++ /dev/null @@ -1,97 +0,0 @@ ---- -title: Frequently asked questions (FAQ) -description: Learn the answers to frequently asked questions about Delta Lake. -menu: docs ---- - -_In this article:_ - -- [What is Delta Lake?](/latest/delta-faq.html#what-is-delta-lake) - -- [How is Delta Lake related to Apache Spark?](/latest/delta-faq.html#how-is-delta-lake-related-to-apache-spark) - -- [What format does Delta Lake use to store data?](/latest/delta-faq.html#what-format-does-delta-lake-use-to-store-data) - -- [How can I read and write data with Delta Lake?](/latest/delta-faq.html#how-can-i-read-and-write-data-with-delta-lake) - -- [Where does Delta Lake store the data?](/latest/delta-faq.html#where-does-delta-lake-store-the-data) - -- [Can I copy my Delta Lake table to another location?](/latest/delta-faq.html#can-i-copy-my-delta-lake-table-to-another-location) - -- [Can I stream data directly into and from Delta tables?](/latest/delta-faq.html#can-i-stream-data-directly-into-and-from-delta-tables) - -- [Does Delta Lake support writes or reads using the Spark Streaming DStream API?](/latest/delta-faq.html#does-delta-lake-support-writes-or-reads-using-the-spark-streaming-dstream-api) - -- [When I use Delta Lake, will I be able to port my code to other Spark platforms easily?](/latest/delta-faq.html#when-i-use-delta-lake-will-i-be-able-to-port-my-code-to-other-spark-platforms-easily) - -- [Does Delta Lake support multi-table transactions?](/latest/delta-faq.html#does-delta-lake-support-multi-table-transactions) - -- [How can I change the type of a column?](/latest/delta-faq.html#how-can-i-change-the-type-of-a-column) - -## What is Delta Lake? - -[Delta Lake](https://delta.io/) is an [open source storage -layer](https://github.com/delta-io/delta) that brings reliability to [data -lakes](https://databricks.com/discover/data-lakes/introduction). Delta Lake -provides ACID transactions, scalable metadata handling, and unifies streaming -and batch data processing. Delta Lake runs on top of your existing data lake -and is fully compatible with Apache Spark APIs. - -## How is Delta Lake related to Apache Spark? - -Delta Lake sits on top of Apache Spark. The format and the compute layer -helps to simplify building big data pipelines and increase the overall -efficiency of your pipelines. - -## What format does Delta Lake use to store data? - -Delta Lake uses versioned Parquet files to store your data in your cloud -storage. Apart from the versions, Delta Lake also stores a transaction log to -keep track of all the commits made to the table or blob store directory to -provide ACID transactions. - -## How can I read and write data with Delta Lake? - -You can use your favorite Apache Spark APIs to read and write data with - -Delta Lake. See [Read a table](/latest/delta-batch#deltadataframereads) and -[Write to a table](/latest/delta-batch#deltadataframewrites). - -## Where does Delta Lake store the data? - -When writing data, you can specify the location in your cloud storage. - -Delta Lake stores the data in that location in Parquet format. - -## Can I copy my Delta Lake table to another location? - -Yes you can copy your Delta Lake table to another location. Remember to copy -files without changing the timestamps to ensure that the time travel with -timestamps will be consistent. - -## Can I stream data directly into and from Delta tables? - -Yes, you can use Structured Streaming to directly write data into Delta tables -and read from Delta tables. See [Stream data into Delta -tables](/latest/delta-streaming#stream-sink) and [Stream data from Delta -tables](/latest/delta-streaming#stream-source). - -## Does Delta Lake support writes or reads using the Spark Streaming DStream API? - -Delta does not support the DStream API. We recommend [Table streaming reads and writes](/latest/delta-streaming). - -## When I use Delta Lake, will I be able to port my code to other Spark platforms easily? - -Yes. When you use Delta Lake, you are using open Apache Spark APIs so you -can easily port your code to other Spark platforms. To port your code, replace -`delta` format with `parquet` format. - -## Does Delta Lake support multi-table transactions? - -Delta Lake does not support multi-table transactions and foreign keys. -Delta Lake supports transactions at the _table_ level. - -## How can I change the type of a column? - -Changing a column's type or dropping a column requires rewriting the table. For -an example, see [Change column type](delta-batch.md#change-column-type). diff --git a/src/pages/latest/delta-intro.mdx b/src/pages/latest/delta-intro.mdx deleted file mode 100644 index 6f706f3..0000000 --- a/src/pages/latest/delta-intro.mdx +++ /dev/null @@ -1,39 +0,0 @@ ---- -title: Introduction -description: Learn about Delta Lake features and resources to learn about Delta Lake. -menu: docs ---- - -[Delta Lake](https://delta.io) is an [open source -project](https://github.com/delta-io/delta) that enables building a [Lakehouse -architecture](https://databricks.com/blog/2020/01/30/what-is-a-data-lakehouse.html) -on top of [data lakes](https://databricks.com/discover/data-lakes/introduction). - -Delta Lake provides [ACID transactions](concurrency-control.md), scalable -metadata handling, and unifies [streaming](delta-streaming.md) and -[batch](delta-batch.md) data processing on top of existing data lakes, such as -S3, ADLS, GCS, and HDFS. - -For a quick overview and benefits of Delta Lake, watch this YouTube video (3 -minutes). - -Specifically, Delta Lake offers: - -- [ACID transactions](concurrency-control.md) on Spark: Serializable isolation - levels ensure that readers never see inconsistent data. -- Scalable metadata handling: Leverages Spark distributed processing power to - handle all the metadata for petabyte-scale tables with billions of files at - ease. -- [Streaming](delta-streaming.md) and [batch](delta-batch.md) unification: A - table in Delta Lake is a batch table as well as a streaming source and sink. - Streaming data ingest, batch historic backfill, interactive queries all just - work out of the box. -- Schema enforcement: Automatically handles schema variations to prevent - insertion of bad records during ingestion. -- [Time travel](delta-batch.md#deltatimetravel): Data versioning enables - rollbacks, full historical audit trails, and reproducible machine learning - experiments. -- [Upserts](delta-update.md#delta-merge) and - [deletes](delta-update.md#delta-delete): Supports merge, update and delete - operations to enable complex use cases like change-data-capture, - slowly-changing-dimension (SCD) operations, streaming upserts, and so on. diff --git a/src/pages/latest/delta-resources.mdx b/src/pages/latest/delta-resources.mdx deleted file mode 100644 index 0e4e920..0000000 --- a/src/pages/latest/delta-resources.mdx +++ /dev/null @@ -1,39 +0,0 @@ ---- -title: Delta Lake Resources -description: Learn about resources for understanding Delta Lake. -menu: docs ---- - -*In this article:* - -* [Blog posts and talks](/latest/delta-resources#blog-posts-and-talks) - -* [VLDB 2020 paper](/latest/delta-resources#vldb-2020-paper) - -* [Examples](/latest/delta-resources#examples) - -* [Delta Lake transaction log specification](/latest/delta-resources#delta-lake-transaction-log-specification) - -## Blog posts and talks - -[Delta Lake blog posts](https://delta.io/blog) - -[Delta Lake tutorials](https://delta.io/learn/tutorials/) - -[Delta Lake videos](https://delta.io/learn/videos/) - -## VLDB 2020 paper - -[Delta Lake: High-Performance ACID Table Storage over Cloud Object -Stores](https://databricks.com/wp-content/uploads/2020/08/p975-armbrust.pdf) - -## Examples - -The Delta Lake GitHub repository has [Scala and Python -examples](https://github.com/delta-io/delta/tree/master/examples/). - -## Delta Lake transaction log specification - -The Delta Lake transaction log has a well-defined open protocol that can be used by -any system to read the log. See [Delta Transaction Log -Protocol](https://github.com/delta-io/delta/blob/master/PROTOCOL.md). diff --git a/src/pages/latest/delta-storage.mdx b/src/pages/latest/delta-storage.mdx deleted file mode 100644 index e067d93..0000000 --- a/src/pages/latest/delta-storage.mdx +++ /dev/null @@ -1,825 +0,0 @@ ---- -title: Storage Configuration -description: Learn how to configure Delta Lake on different storage systems. -menu: docs ---- - -Delta Lake ACID guarantees are predicated on the atomicity and durability -guarantees of the storage system. Specifically, Delta Lake relies on the -following when interacting with storage systems: - -- **Atomic visibility**: There must a way for a file to visible in its entirety - or not visible at all. - -- **Mutual exclusion**: Only one writer must be able to create (or rename) a - file at the final destination. - -- **Consistent listing**: Once a file has been written in a directory, all - future listings for that directory must return that file. - -Because storage systems do not necessarily provide all of these guarantees -out-of-the-box, Delta Lake transactional operations typically go through the -[LogStore -API](https://github.com/delta-io/delta/blob/master/storage/src/main/java/io/delta/storage/LogStore.java) -instead of accessing the storage system directly. To provide the ACID guarantees -for different storage systems, you may have to use different `LogStore` -implementations. This article covers how to configure Delta Lake for various -storage systems. There are two categories of storage systems: - -- **Storage systems with built-in support**: For some storage systems, you do - not need additional configurations. Delta Lake uses the scheme of the path - (that is, `s3a` in `s3a://path`) to dynamically identify the storage system - and use the corresponding `LogStore` implementation that provides the - transactional guarantees. However, for S3, there are additional caveats on - concurrent writes. See the [section on S3](#delta-storage-s3) for details. - -- **Other storage systems**: The `LogStore`, similar to Apache Spark, uses - Hadoop `FileSystem` API to perform reads and writes. So Delta Lake supports - concurrent reads on any storage system that provides an implementation of - `FileSystem` API. For concurrent writes with transactional guarantees, there - are two cases based on the guarantees provided by `FileSystem` implementation. - If the implementation provides consistent listing and atomic - renames-without-overwrite (that is, `rename(... , overwrite = false)` will - either generate the target file atomically or fail if it already exists with - `java.nio.file.FileAlreadyExistsException`), then the default `LogStore` - implementation using renames will allow concurrent writes with guarantees. - Otherwise, you must configure a custom implementation of `LogStore` by setting - the following Spark configuration - - - -```ini -spark.delta.logStore..impl= -``` - - - -where `` is the scheme of the paths of your storage system. This -configures Delta Lake to dynamically use the given `LogStore` -implementation only for those paths. You can have multiple such configurations -for different schemes in your application, thus allowing it to simultaneously -read and write from different storage systems. - - - -- Delta Lake on local file system may not support concurrent transactional - writes. This is because the local file system may or may not provide atomic - renames. So you should not use the local file system for testing concurrent - writes. -- Before version 1.0, Delta Lake supported configuring LogStores by setting - `spark.delta.logStore.class`. This approach is now deprecated. Setting this - configuration will use the configured `LogStore` for all paths, thereby - disabling the dynamic scheme-based delegation. - - - - - -## Amazon S3 - -Delta Lake supports reads and writes to S3 in two different modes: -Single-cluster and Multi-cluster. - -| | Single-cluster | Multi-cluster | -| ------------- | ------------------------------------------------------- | ------------------------------------------------ | -| Configuration | Comes with Delta Lake out-of-the-box | Is experimental and requires extra configuration | -| Reads | Supports concurrent reads from multiple clusters | Supports concurrent reads from multiple clusters | -| Writes | Supports concurrent writes from a _single_ Spark driver | Supports multi-cluster writes | -| Permissions | S3 credentials | S3 and DynamoDB operating permissions | - - - -### Single-cluster setup (default) - -In this default mode, Delta Lake supports concurrent reads from multiple -clusters, but concurrent writes to S3 must originate from a _single_ Spark -driver in order for Delta Lake to provide transactional guarantees. This is -because S3 currently does not provide mutual exclusion, that is, there is no way -to ensure that only one writer is able to create a file. - - - Concurrent writes to the same Delta table from multiple Spark drivers can lead - to data loss. - - -#### Requirements (S3 single-cluster) - -- S3 credentials: [IAM - roles](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles.html) - (recommended) or access keys -- Apache Spark associated with the corresponding Delta Lake version. -- Hadoop's [AWS connector - (hadoop-aws)](https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-aws/) - for the version of Hadoop that Apache Spark is compiled for. - -#### Quickstart (S3 single-cluster) - -This section explains how to quickly start reading and writing Delta tables on -S3 using single-cluster mode. For a detailed explanation of the configuration, -see [\_](#setup-configuration-s3-multi-cluster). - -#. Use the following command to launch a Spark shell with Delta Lake and S3 -support (assuming you use Spark 3.2.1 which is pre-built for Hadoop 3.3.1): - - - -```bash -bin/spark-shell \ - --packages io.delta:delta-core_2.12:$VERSION$,org.apache.hadoop:hadoop-aws:3.3.1 \ - --conf spark.hadoop.fs.s3a.access.key= \ - --conf spark.hadoop.fs.s3a.secret.key= -``` - - - -#. Try out some basic Delta table operations on S3 (in Scala): - - - -```scala -// Create a Delta table on S3: -spark.range(5).write.format("delta").save("s3a:///") - -// Read a Delta table on S3: -spark.read.format("delta").load("s3a:///").show() -``` - - - -For other languages and more examples of Delta table operations, see the -[\_](quick-start.md) page. - -#### Configuration (S3 single-cluster) - -Here are the steps to configure Delta Lake for S3. - -#. Include `hadoop-aws` JAR in the classpath. - -Delta Lake needs the `org.apache.hadoop.fs.s3a.S3AFileSystem` class from the -`hadoop-aws` package, which implements Hadoop's `FileSystem` API for S3. Make -sure the version of this package matches the Hadoop version with which Spark was -built. - -#. Set up S3 credentials. - -We recommend using [IAM -roles](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles.html) for -authentication and authorization. But if you want to use keys, here is one way -is to set up the [Hadoop -configurations](https://spark.apache.org/docs/latest/configuration.html#custom-hadoophive-configuration) -(in Scala): - - - -```scala -sc.hadoopConfiguration.set("fs.s3a.access.key", "") -sc.hadoopConfiguration.set("fs.s3a.secret.key", "") -``` - - - - - -### Multi-cluster setup - - - This support is new and experimental. - - -This mode supports concurrent writes to S3 from multiple clusters and has to be -explicitly enabled by configuring Delta Lake to use the right `LogStore` -implementation. This implementation uses -[DynamoDB](https://aws.amazon.com/dynamodb/) to provide the mutual exclusion -that S3 is lacking. - - - This multi-cluster writing solution is only safe when all writers use this - `LogStore` implementation as well as the same DynamoDB table and region. If - some drivers use out-of-the-box Delta Lake while others use this experimental - `LogStore`, then data loss can occur. - - -#### Requirements (S3 multi-cluster) - -- All of the requirements listed in [\_](#requirements-s3-single-cluster) - section -- In additon to S3 credentials, you also need DynamoDB operating permissions - -#### Quickstart (S3 multi-cluster) - -This section explains how to quickly start reading and writing Delta tables on -S3 using multi-cluster mode. - -#. Use the following command to launch a Spark shell with Delta Lake and S3 -support (assuming you use Spark 3.2.1 which is pre-built for Hadoop 3.3.1): - - - -```bash -bin/spark-shell \ - --packages io.delta:delta-core_2.12:$VERSION$,org.apache.hadoop:hadoop-aws:3.3.1,io.delta:delta-storage-s3-dynamodb:$VERSION$ \ - --conf spark.hadoop.fs.s3a.access.key= \ - --conf spark.hadoop.fs.s3a.secret.key= \ - --conf spark.delta.logStore.s3a.impl=io.delta.storage.S3DynamoDBLogStore \ - --conf spark.io.delta.storage.S3DynamoDBLogStore.ddb.region=us-west-2 -``` - - - -#. Try out some basic Delta table operations on S3 (in Scala): - - - -```scala -// Create a Delta table on S3: -spark.range(5).write.format("delta").save("s3a:///") - -// Read a Delta table on S3: -spark.read.format("delta").load("s3a:///").show() -``` - - - -#### Setup Configuration (S3 multi-cluster) - -#. Create the DynamoDB table. - -You have the choice of creating the DynamoDB table yourself (recommended) or -having it created for you automatically. - -- Creating the DynamoDB table yourself - - This DynamoDB table will maintain commit metadata for multiple Delta tables, - and it is important that it is configured with the [Read/Write Capacity - Mode](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadWriteCapacityMode.html) - (for example, on-demand or provisioned) that is right for your use cases. As - such, we strongly recommend that you create your DynamoDB table yourself. The - following example uses the AWS CLI. To learn more, see the - [create-table](https://docs.aws.amazon.com/cli/latest/reference/dynamodb/create-table.html) - command reference. - - - -```bash -aws dynamodb create-table \ - --region us-east-1 \ - --table-name delta_log \ - --attribute-definitions AttributeName=tablePath,AttributeType=S \ - AttributeName=fileName,AttributeType=S \ - --key-schema AttributeName=tablePath,KeyType=HASH \ - AttributeName=fileName,KeyType=RANGE \ - --billing-mode PAY_PER_REQUEST -``` - - - - - once you select a `table-name` and `region`, you will have to specify them in - each Spark session in order for this multi-cluster mode to work correctly. See - table below. - - -- Automatic DynamoDB table creation - - Nonetheless, after specifying this `LogStore `implementation, if the default - DynamoDB table does not already exist, then it will be created for you - automatically. This default table supports 5 strongly consistent reads and 5 - writes per second. You may change these default values using the - table-creation-only configurations keys detailed in the table below. - -#. Follow the configuration steps listed in -[\_](#configuration-s3-single-cluster) section. - -#. Include the `delta-storage-s3-dynamodb` JAR in the classpath. - -#. Configure the `LogStore` implementation in your Spark session. - -First, configure this `LogStore` implementation for the scheme `s3`. You can -replicate this command for schemes `s3a` and `s3n` as well. - - - -```ini -spark.delta.logStore.s3.impl=io.delta.storage.S3DynamoDBLogStore -``` - - - -Next, specify additional information necessary to instantiate the DynamoDB -client. You must instantiate the DynamoDB client with the same `tableName` and -`region` each Spark session for this multi-cluster mode to work correctly. A -list of per-session configurations and their defaults is given below: - -| Configuration Key | Description | Default | -| ------------------------------------------------------------------- | ----------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | -| spark.io.delta.storage.S3DynamoDBLogStore.ddb.tableName | The name of the DynamoDB table to use | delta_log | -| spark.io.delta.storage.S3DynamoDBLogStore.ddb.region | The region to be used by the client | us-east-1 | -| spark.io.delta.storage.S3DynamoDBLogStore.credentials.provider | The AWSCredentialsProvider\* used by the client | [DefaultAWSCredentialsProviderChain](https://docs.aws.amazon.com/AWSJavaSDK/latest/javadoc/com/amazonaws/auth/DefaultAWSCredentialsProviderChain.html) | -| spark.io.delta.storage.S3DynamoDBLogStore.provisionedThroughput.rcu | (Table-creation-only\*\*) Read Capacity Units | 5 | -| spark.io.delta.storage.S3DynamoDBLogStore.provisionedThroughput.wcu | (Table-creation-only\*\*) Write Capacity Units | 5 | - -- \*For more details on AWS credential providers, see the [AWS - documentation](https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/credentials.html). -- \*\*These configurations are only used when the given DynamoDB table doesn't - already exist and needs to be automatically created. - -#### Production Configuration (S3 multi-cluster) - -By this point, this multi-cluster setup is fully operational. However, there is -extra configuration you may do to improve performance and optimize storage when -running in production. - -#. Adjust your Read and Write Capacity Mode. - -If you are using the default DynamoDB table created for you by this `LogStore` -implementation, its default RCU and WCU might not be enough for your workloads. -You can [adjust the provisioned -throughput](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/ProvisionedThroughput.html#ProvisionedThroughput.CapacityUnits.Modifying) -or [update to On-Demand -Mode](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/WorkingWithTables.Basics.html#WorkingWithTables.Basics.UpdateTable). - -#. Cleanup old DynamoDB entries using Time to Live (TTL). - -Once a DynamoDB metadata entry is marked as complete, and after sufficient time -such that we can now rely on S3 alone to prevent accidental overwrites on its -corresponding Delta file, it is safe to delete that entry from DynamoDB. The -cheapest way to do this is using [DynamoDB's -TTL](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/TTL.html) -feature which is a free, automated means to delete items from your DynamoDB -table. - -Run the following command on your given DynamoDB table to enable TTL: - -```bash aws dynamodb update-time-to-live \ --region us-east-1 \ - --table-name delta_log \ --time-to-live-specification "Enabled=true, - AttributeName=commitTime" -``` - -#. Cleanup old AWS S3 temp files using S3 Lifecycle Expiration. - -In this `LogStore` implementation, a temp file is created containing a copy of -the metadata to be committed into the Delta log. Once that commit to the Delta -log is complete, and after the corresponding DynamoDB entry has been removed, it -is safe to delete this temp file. In practice, only the latest temp file will -ever be used during recovery of a failed commit. - -Here are two simple options for deleting these temp files. - -#. Delete manually using S3 CLI. - -This is the safest option. The following command will delete all but the latest temp file in your given `` and ``: - -```bash -aws s3 ls s3:////_delta_log/.tmp/ --recursive | awk 'NF>1{print $4}' | grep . | sort | head -n -1 | while read -r line ; do - echo "Removing ${line}" - aws s3 rm s3:////_delta_log/.tmp/${line} -done -``` - -#. Delete using an S3 Lifecycle Expiration Rule - -A more automated option is to use an [S3 Lifecycle Expiration rule](https://docs.aws.amazon.com/AmazonS3/latest/userguide/object-lifecycle-mgmt.html), with filter prefix pointing to the `/_delta_log/.tmp/` folder located in your table path, and an expiration value of 30 days. - - - It is important that you choose a sufficiently large expiration value. As - stated above, the latest temp file will be used during recovery of a failed - commit. If this temp file is deleted, then your DynamoDB table and S3 - `delta_table_path/_delta_log/.tmp/` folder will be out of sync. - - -There are a variety of ways to configuring a bucket lifecycle configuration, -described in AWS docs -[here](https://docs.aws.amazon.com/AmazonS3/latest/userguide/how-to-set-lifecycle-configuration-intro.html). - -One way to do this is using S3's `put-bucket-lifecycle-configuration` command. -See [S3 Lifecycle -Configuration](https://docs.aws.amazon.com/cli/latest/reference/s3api/put-bucket-lifecycle-configuration.html) -for details. An example rule and command invocation is given below: - - - -```json -// file://lifecycle.json -{ - "Rules": [ - { - "ID": "expire_tmp_files", - "Filter": { - "Prefix": "path/to/table/_delta_log/.tmp/" - }, - "Status": "Enabled", - "Expiration": { - "Days": 30 - } - } - ] -} -``` - - - -```bash aws s3api put-bucket-lifecycle-configuration \ --bucket my-bucket -\ --lifecycle-configuration file://lifecycle.json -``` - - - AWS S3 may have a limit on the number of rules per bucket. See - [PutBucketLifecycleConfiguration](https://docs.aws.amazon.com/AmazonS3/latest/API/API_PutBucketLifecycleConfiguration.html) - for details. - - -## Microsoft Azure storage - -Delta Lake has built-in support for the various Azure storage systems with -full transactional guarantees for concurrent reads and writes from multiple -clusters. - -Delta Lake relies on Hadoop `FileSystem` APIs to access Azure storage -services. Specifically, Delta Lake requires the implementation of -`FileSystem.rename()` to be atomic, which is only supported in newer Hadoop -versions ([Hadoop-15156](https://issues.apache.org/jira/browse/HADOOP-15156) and -[Hadoop-15086](https://issues.apache.org/jira/browse/HADOOP-15086))). For this -reason, you may need to build Spark with newer Hadoop versions and use them for -deploying your application. See [Specifying the Hadoop Version and Enabling -YARN](https://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version-and-enabling-yarn) -for building Spark with a specific Hadoop version and [\_](quick-start.md) for -setting up Spark with Delta Lake. - -Here is a list of requirements specific to each type of Azure storage system: - -### Azure Blob storage - -#### Requirements (Azure Blob storage) - -- A [shared - key](https://docs.microsoft.com/rest/api/storageservices/authorize-with-shared-key) - or [shared access signature - (SAS)](https://docs.microsoft.com/azure/storage/common/storage-dotnet-shared-access-signature-part-1) -- Delta Lake 0.2.0 or above -- Hadoop's Azure Blob Storage libraries for deployment with the following - versions: - - 2.9.1+ for Hadoop 2 - - 3.0.1+ for Hadoop 3 -- Apache Spark associated with the corresponding Delta Lake version (see - the Quick Start page of the relevant Delta version's documentation) and - [compiled with Hadoop - version](https://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version-and-enabling-yarn) - that is compatible with the chosen Hadoop libraries. - -For example, a possible combination that will work is Delta 0.7.0 or above, -along with Apache Spark 3.0 compiled and deployed with Hadoop 3.2. - -#### Configuration (Azure Blob storage) - -Here are the steps to configure Delta Lake on Azure Blob storage. - -#. Include `hadoop-azure` JAR in the classpath. See the requirements above for -version details. - -#. Set up credentials. - -You can set up your credentials in the [Spark configuration -property](https://spark.apache.org/docs/latest/configuration.html). - -We recommend that you use a SAS token. In Scala, you can use the following: - - - -```scala -spark.conf.set( - "fs.azure.sas...blob.core.windows.net", - "") -``` - - - -Or you can specify an account access key: - - - -```scala -spark.conf.set( - "fs.azure.account.key..blob.core.windows.net", - "") -``` - - - -#### Usage (Azure Blob storage) - - - -```scala -spark.range(5).write.format("delta").save("wasbs://@.blob.core.windows.net/") -spark.read.format("delta").load("wasbs://@.blob.core.windows.net/").show() -``` - - - -### Azure Data Lake Storage Gen1 - -#### Requirements (ADLS Gen1) - -- A [service - principal](https://docs.microsoft.com/azure/active-directory/develop/app-objects-and-service-principals) - for OAuth 2.0 access -- Delta Lake 0.2.0 or above -- Hadoop's Azure Data Lake Storage Gen1 libraries for deployment with the - following versions: - - 2.9.1+ for Hadoop 2 - - 3.0.1+ for Hadoop 3 -- Apache Spark associated with the corresponding Delta Lake version (see - the Quick Start page of the relevant Delta version's documentation) and - [compiled with Hadoop - version](https://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version-and-enabling-yarn) - that is compatible with the chosen Hadoop libraries. - -For example, a possible combination that will work is Delta 0.7.0 or above, -along with Apache Spark 3.0 compiled and deployed with Hadoop 3.2. - -#### Configuration (ADLS Gen1) - -Here are the steps to configure Delta Lake on Azure Data Lake Storage Gen1. - -#. Include `hadoop-azure-datalake` JAR in the classpath. See the requirements -above for version details. - -#. Set up Azure Data Lake Storage Gen1 credentials. - -You can set the following [Hadoop -configurations](https://spark.apache.org/docs/latest/configuration.html#custom-hadoophive-configuration) -with your credentials (in Scala): - - - -```scala -spark.conf.set("dfs.adls.oauth2.access.token.provider.type", "ClientCredential") -spark.conf.set("dfs.adls.oauth2.client.id", "") -spark.conf.set("dfs.adls.oauth2.credential", "") -spark.conf.set("dfs.adls.oauth2.refresh.url", "https://login.microsoftonline.com//oauth2/token") -``` - - - -#### Usage (ADLS Gen1) - - - -```scala -spark.range(5).write.format("delta").save("adl://.azuredatalakestore.net/") - -spark.read.format("delta").load("adl://.azuredatalakestore.net/").show() -``` - - - -### Azure Data Lake Storage Gen2 - -#### Requirements (ADLS Gen2) - -- Account created in [Azure Data Lake Storage - Gen2](<(https://docs.microsoft.com/en-us/azure/storage/common/storage-account-create)>) -- Service principal - [created](https://docs.microsoft.com/azure/azure-resource-manager/resource-group-create-service-principal-portal) - and [assigned the Storage Blob Data Contributor - role](https://docs.microsoft.com/azure/storage/common/storage-auth-aad-rbac-portal?toc=%2fazure%2fstorage%2fblobs%2ftoc.json) - for the storage account. - - - Note the storage-account-name, directory-id (also known as tenant-id), - application-id, and password of the principal. These will be used for - configuring Spark. - -- Delta Lake 0.7.0 or above -- Apache Spark 3.0 or above -- Apache Spark used must be built with Hadoop 3.2 or above. - -For example, a possible combination that will work is Delta 0.7.0 or above, -along with Apache Spark 3.0 compiled and deployed with Hadoop 3.2. - -#### Configuration (ADLS Gen2) - -Here are the steps to configure Delta Lake on Azure Data Lake Storage Gen1. - -#. Include the JAR of the Maven artifact `hadoop-azure-datalake` in the -classpath. See the [requirements](#azure-blob-storage) for version details. In -addition, you may also have to include JARs for Maven artifacts `hadoop-azure` -and `wildfly-openssl`. - -#. Set up Azure Data Lake Storage Gen2 credentials. - - - -```scala -spark.conf.set("fs.azure.account.auth.type..dfs.core.windows.net", "OAuth") -spark.conf.set("fs.azure.account.oauth.provider.type..dfs.core.windows.net", "org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider") -spark.conf.set("fs.azure.account.oauth2.client.id..dfs.core.windows.net", "") -spark.conf.set("fs.azure.account.oauth2.client.secret..dfs.core.windows.net","") -spark.conf.set("fs.azure.account.oauth2.client.endpoint..dfs.core.windows.net", "https://login.microsoftonline.com//oauth2/token") -``` - - - -where ``, ``, `` and -`` are details of the service principal we set as requirements -earlier. - -#. Initialize the file system if needed - - - -```scala -spark.conf.set("fs.azure.createRemoteFileSystemDuringInitialization", "true") -dbutils.fs.ls("abfss://@.dfs.core.windows.net/") -spark.conf.set("fs.azure.createRemoteFileSystemDuringInitialization", "false") -``` - - - -#### Usage (ADLS Gen2) - - - -```scala -spark.range(5).write.format("delta").save("abfss://@.dfs.core.windows.net/") - -spark.read.format("delta").load("abfss://@.dfs.core.windows.net/").show() -``` - - - -where `` is the file system name under the container. - -## HDFS - -Delta Lake has built-in support for HDFS with full transactional guarantees -on concurrent reads and writes from multiple clusters. See Hadoop and Spark -documentation for configuring credentials. - -## Google Cloud Storage - -You must configure Delta Lake to use the correct `LogStore` for concurrently -reading and writing from GCS. - -### Requirements (GCS) - -- For Delta Lake 1.1 and below, you must explicitly include the JAR of the - [Delta Contributions - (delta-contribs)](https://search.maven.org/search?q=g:io.delta%20AND%20a:delta-contribs*) - Maven artifact of the same version. -- JAR of the [GCS Connector - (gcs-connector)](https://search.maven.org/search?q=a:gcs-connector) Maven - artifact. -- Google Cloud Storage account and credentials - -### Configuration (GCS) - -#. For Delta Lake 1.2.0 and below, you must explicitly configure the LogStore -implementation for the scheme `gs`. - - - -```ini -spark.delta.logStore.gs.impl=io.delta.storage.GCSLogStore -``` - - - -#. Include the JAR for `gcs-connector` in the classpath. See the -[documentation](https://cloud.google.com/dataproc/docs/tutorials/gcs-connector-spark-tutorial) -for details on how to configure Spark with GCS. - -### Usage (GCS) - - - -```scala -spark.range(5, 10).write.format("delta").mode("append").save("gs:///") - -spark.read.format("delta").load("gs:///").show() -``` - - - -## Oracle Cloud Infrastructure - - - This support is new and experimental. - - -You have to configure Delta Lake to use the correct `LogStore` for -concurrently reading and writing. - -### Requirements (OCI) - -- JAR of the [Delta Contributions - (delta-contribs)](https://search.maven.org/search?q=g:io.delta%20AND%20a:delta-contribs*) - Maven artifact. -- JAR of the [OCI HDFS Connector - (oci-hdfs-connector)](https://search.maven.org/search?q=a:oci-hdfs-connector) - Maven artifact. -- [OCI account and Object Storage Access - Credentials](https://docs.oracle.com/en-us/iaas/Content/API/SDKDocs/hdfsconnector.htm). - -### Configuration (OCI) - -#. Configure LogStore implementation for the scheme `oci`. - - - -```ini -spark.delta.logStore.oci.impl=io.delta.storage.OracleCloudLogStore -``` - - - -#. Include the JARs for `delta-contribs` and `hadoop-oci-connector` in the -classpath. See [Using the HDFS Connector with -Spark](https://docs.oracle.com/en-us/iaas/Content/API/SDKDocs/hdfsconnectorspark.htm) -for details on how to configure Spark with OCI. - -#. Set the OCI Object Store credentials as explained in the -[documentation](https://docs.oracle.com/en-us/iaas/Content/API/SDKDocs/hdfsconnector.htm). - -### Usage (OCI) - - - -```scala -spark.range(5).write.format("delta").save("oci://@/") - -spark.read.format("delta").load("oci://@/").show() -``` - - - -## IBM Cloud Object Storage - - - This support is new and experimental. - - -You have to configure Delta Lake to use the correct `LogStore` for -concurrently reading and writing. - -### Requirements (IBM) - -- JAR of the [Delta Contributions - (delta-contribs)](https://search.maven.org/search?q=g:io.delta%20AND%20a:delta-contribs*) - Maven artifact. -- JAR of the [Stocator (Stocator)](https://search.maven.org/search?q=a:stocator) - Maven artifact, or build one that uses the IBM SDK following the [Stocator - README](https://github.com/CODAIT/stocator). -- [IBM COS - credentials](https://cloud.ibm.com/docs/services/cloud-object-storage/iam?topic=cloud-object-storage-service-credentials): - IAM or access keys - -### Configuration (IBM) - -#. Configure LogStore implementation for the scheme `cos`. - - - -```ini -spark.delta.logStore.cos.impl=io.delta.storage.IBMCOSLogStore -``` - - - -#. Include the JARs for `delta-contribs` and `Stocator` in the classpath. - -#. Configure `Stocator` with atomic write support by setting the following -properties in the Hadoop configuration. - - - -```ini fs.stocator.scheme.list=cos -fs.cos.impl=com.ibm.stocator.fs.ObjectStoreFileSystem -fs.stocator.cos.impl=com.ibm.stocator.fs.cos.COSAPIClient -fs.stocator.cos.scheme=cos fs.cos.atomic.write=true -``` - - - -#. Set up IBM COS credentials. The example below uses access keys with a service -named `service` (in Scala): - - - -```scala -sc.hadoopConfiguration.set("fs.cos.service.endpoint", "") -sc.hadoopConfiguration.set("fs.cos.service.access.key", "") -sc.hadoopConfiguration.set("fs.cos.service.secret.key", "") -``` - - - -### Usage (IBM) - - - -```scala -spark.range(5).write.format("delta").save("cos://.service/") -spark.read.format("delta").load("cos://.service/").show() -``` - - diff --git a/src/pages/latest/delta-streaming.mdx b/src/pages/latest/delta-streaming.mdx deleted file mode 100644 index db1cd6f..0000000 --- a/src/pages/latest/delta-streaming.mdx +++ /dev/null @@ -1,320 +0,0 @@ ---- -title: Streaming Reads and Writes -menu: docs ---- - -Delta Lake is deeply integrated with [Spark Structured Streaming](https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html) through `readStream` and `writeStream`. Delta Lake overcomes many of the limitations typically associated with streaming systems and files, including: - -- Maintaining "exactly-once" processing with more than one stream (or concurrent batch jobs) -- Efficiently discovering which files are new when using files as the source for a stream - -For many Delta Lake operations on tables, you enable integration with Apache Spark DataSourceV2 and Catalog APIs by setting configurations when you create a new `SparkSession`. See [Configure SparkSession](/latest/delta-batch/#configure-sparksession). - -## Delta table as a source - -When you load a Delta table as a stream source and use it in a streaming query, the query processes all of the data present in the table as well as any new data that arrives after the stream is started. - - - -```scala -spark.readStream.format("delta") - .load("/delta/events") - -import io.delta.implicits._ -spark.readStream.delta("/delta/events") -``` - - - -### Limit input rate - -The following options are available to control micro-batches: - -- `maxFilesPerTrigger`: How many new files to be considered in every micro-batch. The default is 1000. -- `maxBytesPerTrigger`: How much data gets processed in each micro-batch. This option sets a "soft max", meaning that a batch processes approximately this amount of data and may process more than the limit. If you use `Trigger.Once` for your streaming, this option is ignored. This is not set by default. - -If you use `maxBytesPerTrigger` in conjunction with `maxFilesPerTrigger`, the micro-batch processes data until either the `maxFilesPerTrigger` or `maxBytesPerTrigger` limit is reached. - - - In cases when the source table transactions are cleaned up due to the - `logRetentionDuration` [configuration](/latest/delta-batch/#data-retention) - and the stream lags in processing, Delta Lake processes the data corresponding - to the latest available transaction history of the source table but does not - fail the stream. This can result in data being dropped. - - -### Ignore updates and deletes - -Structured Streaming does not handle input that is not an append and throws an exception if any modifications occur on the table being used as a source. There are two main strategies for dealing with changes that cannot be automatically propagated downstream: - -- You can delete the output and checkpoint and restart the stream from the beginning. -- You can set either of these two options: - - `ignoreDeletes`: ignore transactions that delete data at partition boundaries. - - `ignoreChanges`: re-process updates if files had to be rewritten in the source table due to a data changing operation such as `UPDATE`, `MERGE INTO`, `DELETE` (within partitions), or `OVERWRITE`. Unchanged rows may still be emitted, therefore your downstream consumers should be able to handle duplicates. Deletes are not propagated downstream. `ignoreChanges` subsumes `ignoreDeletes`. Therefore if you use `ignoreChanges`, your stream will not be disrupted by either deletions or updates to the source table. - -#### Example - -For example, suppose you have a table `user_events` with `date`, `user_email`, and `action` columns that is partitioned by `date`. You stream out of the `user_events` table and you need to delete data from it due to GDPR. - -When you delete at partition boundaries (that is, the `WHERE` is on a partition column), the files are already segmented by value so the delete just drops those files from the metadata. Thus, if you just want to delete data from some partitions, you can use: - - - -```scala -spark.readStream.format("delta") .option("ignoreDeletes", "true").load("/delta/user_events") -``` - - - -However, if you have to delete data based on `user_email`, then you will need to use: - - - -```scala -spark.readStream.format("delta") .option("ignoreChanges", -"true").load("/delta/user_events") -``` - - - -If you update a `user_email` with the `UPDATE` statement, the file containing the `user_email` in question is rewritten. When you use `ignoreChanges`, the new record is propagated downstream with all other unchanged records that were in the same file. Your logic should be able to handle these incoming duplicate records. - -### Specify initial position - -You can use the following options to specify the starting point of the Delta Lake streaming source without processing the entire table. - -- `startingVersion`: The Delta Lake version to start from. All table changes starting from this version (inclusive) will be read by the streaming source. You can obtain the commit versions from the `version` column of the [DESCRIBE HISTORY](https://delta.io/docs/spark/utilities#delta-history) command output. - - - To return only the latest changes, specify `latest`. - -- `startingTimestamp`: The timestamp to start from. All table changes committed at or after the timestamp (inclusive) will be read by the streaming source. One of: - - - A timestamp string. For example, `"2019-01-01T00:00:00.000Z"`. - - A date string. For example, `"2019-01-01"`. - -You cannot set both options at the same time; you can use only one of them. They take effect only when starting a new streaming query. If a streaming query has started and the progress has been recorded in its checkpoint, these options are ignored. - - - Although you can start the streaming source from a specified version or - timestamp, the schema of the streaming source is always the latest schema of - the Delta table. You must ensure there is no incompatible schema change to the - Delta table after the specified version or timestamp. Otherwise, the streaming - source may return incorrect results when reading the data with an incorrect - schema. - - -#### Example - -For example, suppose you have a table `user_events`. If you want to read changes since version 5, use: - - - -```scala -spark.readStream.format("delta") - .option("startingVersion", "5") - .load("/delta/user_events") -``` - - - -If you want to read changes since 2018-10-18, use: - - - -```scala -spark.readStream.format("delta") - .option("startingTimestamp", "2018-10-18") - .load("/delta/user_events") -``` - - - -### Process initial snapshot without data being dropped - -When using a Delta table as a stream source, the query first processes all of the data present in the table. The Delta table at this version is called the initial snapshot. By default, the Delta table’s data files are processed based on which file was last modified. However, the last modification time does not necessarily represent the record event time order. - -In a stateful streaming query with a defined watermark, processing files by modification time can result in records being processed in the wrong order. This could lead to records dropping as late events by the watermark. - -You can avoid the data drop issue by enabling the following option: - -- `withEventTimeOrder`: Whether the initial snapshot should be processed with event time order. - -With event time order enabled, the event time range of initial snapshot data is divided into time buckets. Each micro batch processes a bucket by filtering data within the time range. The `maxFilesPerTrigger` and `maxBytesPerTrigger` configuration options are still applicable to control the microbatch size but only in an approximate way due to the nature of the processing. - -Notable information about this feature: - -- The data drop issue only happens when the initial Delta snapshot of a stateful streaming query is processed in the default order. -- You cannot change `withEventTimeOrder` once the stream query is started while the initial snapshot is still being processed. To restart with `withEventTimeOrder` changed, you need to delete the checkpoint. -- If you are running a stream query with withEventTimeOrder enabled, you cannot downgrade it to a DBR version which doesn’t support this feature until the initial snapshot processing is completed. If you need to downgrade, you can wait for the initial snapshot to finish, or delete the checkpoint and restart the query. -- This feature is not supported in the following uncommon scenarios: - - The event time column is a generated column and there are non-projection transformations between the Delta source and watermark. - - There is a watermark that has more than one Delta source in the stream query. -- With event time order enabled, the performance of the Delta initial snapshot processing might be slower. -- Each micro batch scans the initial snapshot to filter data within the corresponding event time range. For faster filter action, it is advised to use a Delta source column as the event time so that data skipping can be applied (check \_ for when it’s applicable). Additionally, table partitioning along the event time column can further speed the processing. You can check Spark UI to see how many delta files are scanned for a specific micro batch. - -#### Example - -Suppose you have a table `user_events` with an `event_time` column. Your streaming query is an aggregation query. If you want to ensure no data drop during the initial snapshot processing, you can use: - - - -```scala -spark.readStream.format("delta") - .option("withEventTimeOrder", "true") - .load("/tmp/delta/user_events") - .withWatermark("event_time", "10 seconds") -``` - - - - - You can also enable this with Spark config on the cluster which will apply to - all streaming queries: `spark.databricks.delta.withEventTimeOrder.enabled` to - `true` - - -## Delta table as a sink - -You can also write data into a Delta table using Structured Streaming. The transaction log enables Delta Lake to guarantee exactly-once processing, even when there are other streams or batch queries running concurrently against the table. - - - The Delta Lake `VACUUM` function removes all files not managed by Delta Lake - but skips any directories that begin with _. You can safely store checkpoints - alongside other data and metadata for a Delta table using a directory - structure such as `table_name/_checkpoints`. - - -### Append mode - -By default, streams run in append mode, which adds new records to the table. - -You can use the path method: - - -```python -events.writeStream - .format("delta") \ - .outputMode("append") \ - .option("checkpointLocation", "/tmp/delta/_checkpoints/") \ - .start("/delta/events") -``` - -```scala -events.writeStream - .format("delta") - .outputMode("append") - .option("checkpointLocation", "/tmp/delta/events/_checkpoints/") - .start("/tmp/delta/events") - -import io.delta.implicits._ -events.writeStream - .outputMode("append") - .option("checkpointLocation", "/tmp/delta/events/_checkpoints/") - .delta("/tmp/delta/events") -``` - - - -or the toTable method in Spark 3.1 and higher (the Delta Lake library 8.3 and above), as follows. (In Spark versions before 3.1 (the Delta Lake library 8.2 and below), use the `table` method instead.) - - -```python -events.writeStream - .format("delta") \ - .outputMode("append") \ - .option("checkpointLocation", "/tmp/delta/events/_checkpoints/") \ - .toTable("events") -``` - -```scala -events.writeStream - .outputMode("append") - .option("checkpointLocation", "/tmp/delta/events/_checkpoints/") - .toTable("events") -``` - - - -### Complete mode - -You can also use Structured Streaming to replace the entire table with every batch. One example use case is to compute a summary using aggregation: - - -```python -(spark.readStream - .format("delta") - .load("/tmp/delta/events") - .groupBy("customerId") - .count() - .writeStream - .format("delta") - .outputMode("complete") - .option("checkpointLocation", "/tmp/delta/eventsByCustomer/_checkpoints/") - .start("/tmp/delta/eventsByCustomer") -) -``` - -```scala -spark.readStream - .format("delta") - .load("/tmp/delta/events") - .groupBy("customerId") - .count() - .writeStream - .format("delta") - .outputMode("complete") - .option("checkpointLocation", "/tmp/delta/eventsByCustomer/_checkpoints/") - .start("/tmp/delta/eventsByCustomer") -``` - - - -The preceding example continuously updates a table that contains the aggregate number of events by customer. - -For applications with more lenient latency requirements, you can save computing resources with one-time triggers. Use these to update summary aggregation tables on a given schedule, processing only new data that has arrived since the last update. - -## Idempotent table writes in `foreachBatch` - - - Available in Delta Lake 2.0.0 and above. - - -The command `foreachBatch` allows you to specify a function that is executed on the output of every micro-batch after arbitrary transformations in the streaming query. This allows implementating a `foreachBatch` function that can write the micro-batch output to one or more target Delta table destinations. However, `foreachBatch` does not make those writes idempotent as those write attempts lack the information of whether the batch is being re-executed or not. For example, rerunning a failed batch could result in duplicate data writes. - -To address this, Delta tables support the following `DataFrameWriter` options to make the writes idempotent: - -- `txnAppId`: A unique string that you can pass on each `DataFrame` write. For example, you can use the StreamingQuery ID as `txnAppId`. -- `txnVersion`: A monotonically increasing number that acts as transaction version. - -Delta table uses the combination of `txnAppId` and `txnVersion` to identify duplicate writes and ignore them. - -If a batch write is interrupted with a failure, rerunning the batch uses the same application and batch ID, which would help the runtime correctly identify duplicate writes and ignore them. Application ID (`txnAppId`) can be any user-generated unique string and does not have to be related to the stream ID. - - - If you delete the streaming checkpoint and restart the query with a new - checkpoint, you must provide a different `appId`; otherwise, writes from the - restarted query will be ignored because it will contain the same `txnAppId` - and the batch ID would start from 0. - - -The same `DataFrameWriter` options can be used to achieve the idempotent writes in non-Streaming job. For details [Idempotent writes](/latest/delta-batch/#idempotent-writes). - - - -```python -app_id = ... # A unique string that is used as an application ID. - -def writeToDeltaLakeTableIdempotent(batch_df, batch_id): - batch_df.write.format(...).option("txnVersion", batch_id).option("txnAppId", app_id).save(...) # location 1 - batch_df.write.format(...).option("txnVersion", batch_id).option("txnAppId", app_id).save(...) # location 2 -``` - -```scala -val appId = ... // A unique string that is used as an application ID. -streamingDF.writeStream.foreachBatch { (batchDF: DataFrame, batchId: Long) => - batchDF.write.format(...).option("txnVersion", batchId).option("txnAppId", appId).save(...) // location 1 - batchDF.write.format(...).option("txnVersion", batchId).option("txnAppId", appId).save(...) // location 2 -} -``` - - diff --git a/src/pages/latest/delta-update.mdx b/src/pages/latest/delta-update.mdx deleted file mode 100644 index 6ac9b04..0000000 --- a/src/pages/latest/delta-update.mdx +++ /dev/null @@ -1,812 +0,0 @@ ---- -title: Table deletes, updates, and merges -menu: docs ---- - -Delta Lake supports several statements to facilitate deleting data from and updating data in Delta tables. - -## Delete from a table - -You can remove data that matches a predicate from a Delta table. For instance, in a table named `people10m` or a path at `/tmp/delta/people-10m`, to delete all rows corresponding to people with a value in the `birthDate` column from before `1955`, you can run the following: - - - -```sql -DELETE FROM people10m WHERE birthDate < '1955-01-01' - -DELETE FROM delta.`/tmp/delta/people-10m` WHERE birthDate < '1955-01-01' -``` - -```python -from delta.tables import * -from pyspark.sql.functions import * - -deltaTable = DeltaTable.forPath(spark, '/tmp/delta/people-10m') - -# Declare the predicate by using a SQL-formatted string. -deltaTable.delete("birthDate < '1955-01-01'") - -# Declare the predicate by using Spark SQL functions. -deltaTable.delete(col('birthDate') < '1960-01-01') -``` - -```scala -import io.delta.tables._ - -val deltaTable = DeltaTable.forPath(spark, "/tmp/delta/people-10m") - -// Declare the predicate by using a SQL-formatted string. -deltaTable.delete("birthDate < '1955-01-01'") - -import org.apache.spark.sql.functions._ -import spark.implicits._ - -// Declare the predicate by using Spark SQL functions and implicits. -deltaTable.delete(col("birthDate") < "1955-01-01") -``` - -```java -import io.delta.tables.*; -import org.apache.spark.sql.functions; - -DeltaTable deltaTable = DeltaTable.forPath(spark, "/tmp/delta/people-10m"); - -// Declare the predicate by using a SQL-formatted string. -deltaTable.delete("birthDate < '1955-01-01'"); - -// Declare the predicate by using Spark SQL functions. -deltaTable.delete(functions.col("birthDate").lt(functions.lit("1955-01-01"))); -``` - - - -See [Configure SparkSession](/latest/delta-batch/#configure-sparksession) for the steps to enable support for SQL commands. - -See the [Delta API documentation](/latest/delta-apidoc) for details. - - - `delete` removes the data from the latest version of the Delta table but does - not remove it from the physical storage until the old versions are explicitly - vacuumed. See [vacuum](/latest/delta-utility/#remove-files-no-longer-referenced-by-a-delta-table) for details. - - - - When possible, provide predicates on the partition columns for a partitioned - Delta table as such predicates can significantly speed up the operation. - - -## Update a table - -You can update data that matches a predicate in a Delta table. For example, in a table named people10m or a path at `/tmp/delta/people-10m`, to change an abbreviation in the `gender` column from `M` or `F` to `Male` or `Female`, you can run the following: - - - -```sql -UPDATE people10m SET gender = 'Female' WHERE gender = 'F'; -UPDATE people10m SET gender = 'Male' WHERE gender = 'M'; - -UPDATE delta.`/tmp/delta/people-10m` SET gender = 'Female' WHERE gender = 'F'; -UPDATE delta.`/tmp/delta/people-10m` SET gender = 'Male' WHERE gender = 'M'; -``` - -```python -from delta.tables import * -from pyspark.sql.functions import * - -deltaTable = DeltaTable.forPath(spark, '/tmp/delta/people-10m') - -# Declare the predicate by using a SQL-formatted string. -deltaTable.update( - condition = "gender = 'F'", - set = { "gender": "'Female'" } -) - -# Declare the predicate by using Spark SQL functions. -deltaTable.update( - condition = col('gender') == 'M', - set = { 'gender': lit('Male') } -) -``` - -```scala -import io.delta.tables._ - -val deltaTable = DeltaTable.forPath(spark, "/tmp/delta/people-10m") - -// Declare the predicate by using a SQL-formatted string. -deltaTable.updateExpr( - "gender = 'F'", - Map("gender" -> "'Female'") - -import org.apache.spark.sql.functions._ -import spark.implicits._ - -// Declare the predicate by using Spark SQL functions and implicits. -deltaTable.update( - col("gender") === "M", - Map("gender" -> lit("Male"))); -``` - -```java -import io.delta.tables.*; -import org.apache.spark.sql.functions; -import java.util.HashMap; - -DeltaTable deltaTable = DeltaTable.forPath(spark, "/data/events/"); - -// Declare the predicate by using a SQL-formatted string. -deltaTable.updateExpr( - "gender = 'F'", - new HashMap() {{ - put("gender", "'Female'"); - }} -); - -// Declare the predicate by using Spark SQL functions. -deltaTable.update( - functions.col(gender).eq("M"), - new HashMap() {{ - put("gender", functions.lit("Male")); - }} -); -``` - - - -See the [Delta Lake APIs](/latest/delta-apidoc) for details. - - - Similar to delete, update operations can get a significant speedup with - predicates on partitions. - - -## Upsert into a table using merge - -You can upsert data from a source table, view, or DataFrame into a target Delta table using the `MERGE` SQL operation. Delta Lake supports inserts, updates and deletes in `MERGE`, and supports extended syntax beyond the SQL standards to facilitate advanced use cases. - -Suppose you have a source table named people10mupdates or a source path at `/tmp/delta/people-10m-updates` that contains new data for a target table named people10m or a target path at `/tmp/delta/people-10m`. Some of these new records may already be present in the target data. To merge the new data, you want to update rows where the person’s `id` is already present and insert the new rows where no matching `id` is present. You can run the following: - - - -```sql -MERGE INTO people10m -USING people10mupdates -ON people10m.id = people10mupdates.id -WHEN MATCHED THEN - UPDATE SET - id = people10mupdates.id, - firstName = people10mupdates.firstName, - middleName = people10mupdates.middleName, - lastName = people10mupdates.lastName, - gender = people10mupdates.gender, - birthDate = people10mupdates.birthDate, - ssn = people10mupdates.ssn, - salary = people10mupdates.salary -WHEN NOT MATCHED - THEN INSERT ( - id, - firstName, - middleName, - lastName, - gender, - birthDate, - ssn, - salary - ) - VALUES ( - people10mupdates.id, - people10mupdates.firstName, - people10mupdates.middleName, - people10mupdates.lastName, - people10mupdates.gender, - people10mupdates.birthDate, - people10mupdates.ssn, - people10mupdates.salary - ) -``` - -```python -from delta.tables import * - -deltaTablePeople = DeltaTable.forPath(spark, '/tmp/delta/people-10m') -deltaTablePeopleUpdates = DeltaTable.forPath(spark, '/tmp/delta/people-10m-updates') - -dfUpdates = deltaTablePeopleUpdates.toDF() - -deltaTablePeople.alias('people') \ - .merge( - dfUpdates.alias('updates'), - 'people.id = updates.id' - ) \ - .whenMatchedUpdate(set = - { - "id": "updates.id", - "firstName": "updates.firstName", - "middleName": "updates.middleName", - "lastName": "updates.lastName", - "gender": "updates.gender", - "birthDate": "updates.birthDate", - "ssn": "updates.ssn", - "salary": "updates.salary" - } - ) \ - .whenNotMatchedInsert(values = - { - "id": "updates.id", - "firstName": "updates.firstName", - "middleName": "updates.middleName", - "lastName": "updates.lastName", - "gender": "updates.gender", - "birthDate": "updates.birthDate", - "ssn": "updates.ssn", - "salary": "updates.salary" - } - ) \ - .execute() -``` - -```scala -import io.delta.tables._ -import org.apache.spark.sql.functions._ - -val deltaTablePeople = DeltaTable.forPath(spark, "/tmp/delta/people-10m") -val deltaTablePeopleUpdates = DeltaTable.forPath(spark, "tmp/delta/people-10m-updates") -val dfUpdates = deltaTablePeopleUpdates.toDF() - -deltaTablePeople - .as("people") - .merge( - dfUpdates.as("updates"), - "people.id = updates.id") - .whenMatched - .updateExpr( - Map( - "id" -> "updates.id", - "firstName" -> "updates.firstName", - "middleName" -> "updates.middleName", - "lastName" -> "updates.lastName", - "gender" -> "updates.gender", - "birthDate" -> "updates.birthDate", - "ssn" -> "updates.ssn", - "salary" -> "updates.salary" - )) - .whenNotMatched - .insertExpr( - Map( - "id" -> "updates.id", - "firstName" -> "updates.firstName", - "middleName" -> "updates.middleName", - "lastName" -> "updates.lastName", - "gender" -> "updates.gender", - "birthDate" -> "updates.birthDate", - "ssn" -> "updates.ssn", - "salary" -> "updates.salary" - )) - .execute() -``` - -```java -import io.delta.tables.*; -import org.apache.spark.sql.functions; -import java.util.HashMap; - -DeltaTable deltaTable = DeltaTable.forPath(spark, "/tmp/delta/people-10m") -Dataset dfUpdates = spark.read("delta").load("/tmp/delta/people-10m-updates") - -deltaTable - .as("people") - .merge( - dfUpdates.as("updates"), - "people.id = updates.id") - .whenMatched() - .updateExpr( - new HashMap() {{ - put("id", "updates.id"); - put("firstName", "updates.firstName"); - put("middleName", "updates.middleName"); - put("lastName", "updates.lastName"); - put("gender", "updates.gender"); - put("birthDate", "updates.birthDate"); - put("ssn", "updates.ssn"); - put("salary", "updates.salary"); - }}) - .whenNotMatched() - .insertExpr( - new HashMap() {{ - put("id", "updates.id"); - put("firstName", "updates.firstName"); - put("middleName", "updates.middleName"); - put("lastName", "updates.lastName"); - put("gender", "updates.gender"); - put("birthDate", "updates.birthDate"); - put("ssn", "updates.ssn"); - put("salary", "updates.salary"); - }}) - .execute(); -``` - - - -See the [Delta Lake APIs](/latest/delta-apidoc) for Scala, Java, and Python syntax details. - -Delta Lake merge operations typically require two passes over the source data. If your source data contains nondeterministic expressions, multiple passes on the source data can produce different rows causing incorrect results. Some common examples of nondeterministic expressions include the `current_date` and `current_timestamp` functions. If you cannot avoid using non-deterministic functions, consider saving the source data to storage, for example as a temporary Delta table. Caching the source data may not address this issue, as cache invalidation can cause the source data to be recomputed partially or completely (for example when a cluster loses some of it executors when scaling down). - -### Operation semantics - -Here is a detailed description of the `merge` programmatic operation. - -- There can be any number of `whenMatched` and `whenNotMatched` clauses. - -- `whenMatched` clauses are executed when a source row matches a target table row based on the match condition. These clauses have the following semantics. - - - `whenMatched` clauses can have at most one `update` and one `delete` action. The `update` action in `merge` only updates the specified columns (similar to the `update` [operation](#delta-update)) of the matched target row. The `delete` action deletes the matched row. - - - Each `whenMatched` clause can have an optional condition. If this clause condition exists, the `update` or `delete` action is executed for any matching source-target row pair only when the clause condition is true. - - - If there are multiple `whenMatched` clauses, then they are evaluated in order they are specified (that is, the order of the clauses matter). All `whenMatched` clauses, except the last one, must have conditions. - - - If multiple `whenMatched` clauses have conditions and none of the conditions are true for a matching source-target row pair, then the matched target row is left unchanged. - - - To update all the columns of the target Delta table with the corresponding columns of the source dataset, use `whenMatched(...).updateAll()`. This is equivalent to: - - - - ```scala - whenMatched(...).updateExpr(Map("col1" -> "source.col1", "col2" -> "source.col2", ...)) - ``` - - - - for all the columns of the target Delta table. Therefore, this action assumes that the source table has the same columns as those in the target table, otherwise the query throws an analysis error. - - - This behavior changes when automatic schema migration is enabled. See - [Automatic schema evolution](#automatic-schema-evolution) for details. - - -- `whenNotMatched` clauses are executed when a source row does not match any target row based on the match condition. These clauses have the following semantics. - - - `whenNotMatched` clauses can have only the `insert` action. The new row is generated based on the specified column and corresponding expressions. You do not need to specify all the columns in the target table. For unspecified target columns, `NULL` is inserted. - - - Each `whenNotMatched` clause can have an optional condition. If the clause condition is present, a source row is inserted only if that condition is true for that row. Otherwise, the source column is ignored. - - - If there are multiple `whenNotMatched` clauses, then they are evaluated in order they are specified (that is, the order of the clauses matter). All `whenNotMatched` clauses, except the last one, must have conditions. - - - To insert all the columns of the target Delta table with the corresponding columns of the source dataset, use `whenNotMatched(...).insertAll()`. This is equivalent to: - - - - ```scala - whenNotMatched(...).insertExpr(Map("col1" -> "source.col1", "col2" -> "source.col2", ...)) - ``` - - - - for all the columns of the target Delta table. Therefore, this action assumes that the source table has the same columns as those in the target table, otherwise the query throws an analysis error. - - - This behavior changes when automatic schema migration is enabled. See [Automatic schema evolution](#automatic-schema-evolution) for details. - - - - -- A merge operation can fail if multiple rows of the source dataset match and the merge attempts to update the same rows of the target Delta table. According to the `SQL` semantics of merge, such an update operation is ambiguous as it is unclear which source row should be used to update the matched target row. You can preprocess the source table to eliminate the possibility of multiple matches. See the [change data capture](#write-change-data-into-a-delta-table) example—it shows how to preprocess the change dataset (that is, the source dataset) to retain only the latest change for each key before applying that change into the target Delta table. - -- You can apply a SQL `MERGE` operation on a SQL VIEW only if the view has been defined as `CREATE VIEW viewName AS SELECT * FROM deltaTable`. - - - -### Schema validation - -`merge` automatically validates that the schema of the data generated by insert and update expressions are compatible with the schema of the table. It uses the following rules to determine whether the `merge` operation is compatible: - -- For `update` and `insert` actions, the specified target columns must exist in the target Delta table. - -- For `updateAll` and `insertAll` actions, the source dataset must have all the columns of the target Delta table. The source dataset can have extra columns and they are ignored. - - If you do not want the extra columns to be ignored and instead want to update the target table schema to include new columns, see [merge schema evolution](#merge-schema-evolution). - -- For all actions, if the data type generated by the expressions producing the target columns are different from the corresponding columns in the target Delta table, `merge` tries to cast them to the types in the table. - -### Automatic schema evolution - -By default, `updateAll` and `insertAll` assign all the columns in the target Delta table with columns of the same name from the source dataset. Any columns in the source dataset that don't match columns in the target table are ignored. However, in some use cases, it is desirable to automatically add source columns to the target Delta table. To automatically update the table schema during a `merge` operation with `updateAll` and `insertAll` (at least one of them), you can set the Spark session configuration `spark.databricks.delta.schema.autoMerge.enabled` to `true` before running the `merge` operation. - - - -- Schema evolution occurs only when there is either an `updateAll` (`UPDATE SET *`) or an `insertAll` (`INSERT *`) action, or both. - -- `update` and `insert` actions cannot explicitly refer to target columns that do not already exist in the target table (even it there are `updateAll` or `insertAll` as one of the clauses). See the examples below. - - - -Here are a few examples of the effects of `merge` operation with and without schema evolution. - -| Columns | Query (in Scala) | Behavior without schema evolution (default) | Behavior with schema evolution | -| ----------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| Target columns: `key`, `value`

Source columns:` key`, `value`, `newValue` | `scala targetDeltaTable.alias("t") .merge( sourceDataFrame.alias("s"), "t.key = s.key") .whenMatched().updateAll() .whenNotMatched().insertAll() .execute()` | The table schema remains unchanged; only columns `key`, `value` are updated/inserted.

The table schema is changed to `(key, value, newValue)`. `updateAll` updates columns `value` and `newValue`, and `insertAll` inserts rows `(key, value, newValue)`. | The table schema remains unchanged; only columns `key`, `value` are updated/inserted.

The table schema is changed to `(key, value, newValue)`. `updateAll` updates columns `value` and `newValue`, and `insertAll` inserts rows `(key, value, newValue)`. | -| Target columns: `key`, `value`

Source columns:` key`, `newValue` | `targetDeltaTable.alias("t") .merge( sourceDataFrame.alias("s"), "t.key = s.key") .whenMatched().updateAll() .whenNotMatched().insertAll() .execute()` | `updateAll` and `insertAll` actions throw an error because the target column `oldValue` is not in the source. | The table schema is changed to `(key, oldValue, newValue)`. `updateAll` updates columns `key` and `newValue` leaving `oldValue` unchanged, and `insertAll` inserts rows `(key, NULL, newValue)` (that is, `oldValue` is inserted as `NULL`). | -| "Target columns: `key`, `value`

Source columns:` key`, `newValue`" | `targetDeltaTable.alias("t") .merge( sourceDataFrame.alias("s"), "t.key = s.key") .whenMatched().update(Map( "newValue" -> col("s.newValue"))) .whenNotMatched().insertAll() .execute()` | `update` throws an error because column `newValue` does not exist in the target table. | `update` still throws an error because column `newValue` does not exist in the target table. | -| Target columns: `key`, `value`

Source columns:` key`, `newValue` | `targetDeltaTable.alias("t") .merge( sourceDataFrame.alias("s"), "t.key = s.key") .whenMatched().updateAll() .whenNotMatched().insert(Map( "key" -> col("s.key"), "newValue" -> col("s.newValue"))) .execute()` | `insert` throws an error because column `newValue` does not exist in the target table. | `insert` still throws an error as column `newValue` does not exist in the target table. | - -## Special considerations for schemas that contain arrays of structs - -Delta `MERGE INTO` supports resolving struct fields by name and evolving schemas for arrays of structs. With schema evolution enabled, target table schemas will evolve for arrays of structs, which also works with any nested structs inside of arrays. - -Here are a few examples of the effects of merge operations with and without schema evolution for arrays of structs. - -| Source schema | Target schema | Behavior without schema evolution (default) | Behavior with schema evolutionBehavior with schema evolution | -| --------------------------------------------------------- | ---------------------------------------------- | --------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `array>` | `array>` | The table schema remains unchanged. Columns will be resolved by name and updated or inserted. | The table schema remains unchanged. Columns will be resolved by name and updated or inserted. | -| `array>` | `array>` | `update` and `insert` throw errors because `c` and `d` do not exist in the target table. | The table schema is changed to `array>`. `c` and `d` are inserted as `NULL` for existing entries in the target table. `update` and `insert` fill entries in the source table with `a` casted to string and `b` as `NULL`. | -| `array>>` | `array>>` | `update` and `insert` throw errors because `d` does not exist in the target table. | The target table schema is changed to `array>>`. `d` is inserted as `NULL` for existing entries in the target table. | - -### Performance tuning - -You can reduce the time taken by merge using the following approaches: - -- **Reduce the search space for matches**: By default, the `merge` operation searches the entire Delta table to find matches in the source table. One way to speed up `merge` is to reduce the search space by adding known constraints in the match condition. For example, suppose you have a table that is partitioned by `country` and `date` and you want to use `merge` to update information for the last day and a specific country. Adding the condition - - - - ```sql - events.date = current_date() AND events.country = 'USA' - ``` - - - - will make the query faster as it looks for matches only in the relevant partitions. Furthermore, it will also reduce the chances of conflicts with other concurrent operations. See [concurency control](/latest/concurrency-control) for more details. - -- **Compact files**: If the data is stored in many small files, reading the data to search for matches can become slow. You can compact small files into larger files to improve read throughput. See [best practices for compaction](/latest/best-practices/#compact-files) for details. - -- **Control the shuffle partitions for writes**: The `merge` operation shuffles data multiple times to compute and write the updated data. The number of tasks used to shuffle is controlled by the Spark session configuration `spark.sql.shuffle.partitions`. Setting this parameter not only controls the parallelism but also determines the number of output files. Increasing the value increases parallelism but also generates a larger number of smaller data files. - -- **Repartition output data before write**: For partitioned tables, `merge` can produce a much larger number of small files than the number of shuffle partitions. This is because every shuffle task can write multiple files in multiple partitions, and can become a performance bottleneck. In many cases, it helps to repartition the output data by the table's partition columns before writing it. You enable this by setting the Spark session configuration `spark.databricks.delta.merge.repartitionBeforeWrite.enabled` to `true`. - -## Merge examples - -Here are a few examples on how to use `merge` in different scenarios. - -### Data deduplication when writing into Delta tables - -A common ETL use case is to collect logs into Delta table by appending them to a table. However, often the sources can generate duplicate log records and downstream deduplication steps are needed to take care of them. With `merge`, you can avoid inserting the duplicate records. - - - -```sql -MERGE INTO logs -USING newDedupedLogs -ON logs.uniqueId = newDedupedLogs.uniqueId -WHEN NOT MATCHED - THEN INSERT * -``` - -```python -deltaTable.alias("logs").merge( - newDedupedLogs.alias("newDedupedLogs"), - "logs.uniqueId = newDedupedLogs.uniqueId") \ - .whenNotMatchedInsertAll() \ - .execute() -``` - -```scala -deltaTable - .as("logs") - .merge( - newDedupedLogs.as("newDedupedLogs"), - "logs.uniqueId = newDedupedLogs.uniqueId") - .whenNotMatched() - .insertAll() - .execute() -``` - -```java -deltaTable - .as("logs") - .merge( - newDedupedLogs.as("newDedupedLogs"), - "logs.uniqueId = newDedupedLogs.uniqueId") - .whenNotMatched() - .insertAll() - .execute(); -``` - - - - - The dataset containing the new logs needs to be deduplicated within itself. By - the SQL semantics of merge, it matches and deduplicates the new data with the - existing data in the table, but if there is duplicate data within the new - dataset, it is inserted. Hence, deduplicate the new data before merging into - the table. - - -If you know that you may get duplicate records only for a few days, you can optimized -your query further by partitioning the table by date, and then specifying the date -range of the target table to match on. - - - -```sql -MERGE INTO logs -USING newDedupedLogs -ON logs.uniqueId = newDedupedLogs.uniqueId AND logs.date > current_date() - INTERVAL 7 DAYS -WHEN NOT MATCHED AND newDedupedLogs.date > current_date() - INTERVAL 7 DAYS - THEN INSERT * -``` - -```python -deltaTable.alias("logs").merge( - newDedupedLogs.alias("newDedupedLogs"), - "logs.uniqueId = newDedupedLogs.uniqueId AND logs.date > current_date() - INTERVAL 7 DAYS") \ - .whenNotMatchedInsertAll("newDedupedLogs.date > current_date() - INTERVAL 7 DAYS") \ - .execute() -``` - -```scala -deltaTable.as("logs").merge( - newDedupedLogs.as("newDedupedLogs"), - "logs.uniqueId = newDedupedLogs.uniqueId AND logs.date > current_date() - INTERVAL 7 DAYS") - .whenNotMatched("newDedupedLogs.date > current_date() - INTERVAL 7 DAYS") - .insertAll() - .execute() -``` - -```java -deltaTable.as("logs").merge( - newDedupedLogs.as("newDedupedLogs"), - "logs.uniqueId = newDedupedLogs.uniqueId AND logs.date > current_date() - INTERVAL 7 DAYS") - .whenNotMatched("newDedupedLogs.date > current_date() - INTERVAL 7 DAYS") - .insertAll() - .execute(); -``` - - - -This is more efficient than the previous command as it looks for duplicates only in the -last 7 days of logs, not the entire table. Furthermore, you can use this insert-only merge with Structured Streaming to perform continuous deduplication of the logs. - -- In a streaming query, you can use merge operation in `foreachBatch` to continuously write any streaming data to a Delta table with deduplication. See the following [streaming example](#upsert-from-streaming-queries-using-foreachbatch) for more information on `foreachBatch`. - -- In another streaming query, you can continuously read deduplicated data from this Delta table. This is possible because an insert-only merge only appends new data to the Delta table. - -### Slowly changing data (SCD) Type 2 operation into Delta tables - -Another common operation is SCD Type 2, which maintains history of all changes made to each key in a dimensional table. Such operations require updating existing rows to mark previous values of keys as old, and the inserting the new rows as the latest values. Given a source table with updates and the target table with the dimensional data, SCD Type 2 can be expressed with `merge`. - -Here is a concrete example of maintaining the history of addresses for a customer along with the active date range of each address. When a customer's address needs to be updated, you have to mark the previous address as not the current one, update its active date range, and add the new address as the current one. - - - -```scala -val customersTable: DeltaTable = ... // table with schema (customerId, address, current, effectiveDate, endDate) - -val updatesDF: DataFrame = ... // DataFrame with schema (customerId, address, effectiveDate) - -// Rows to INSERT new addresses of existing customers -val newAddressesToInsert = updatesDF - .as("updates") - .join(customersTable.toDF.as("customers"), "customerid") - .where("customers.current = true AND updates.address <> customers.address") - -// Stage the update by unioning two sets of rows -// 1. Rows that will be inserted in the whenNotMatched clause -// 2. Rows that will either update the current addresses of existing customers or insert the new addresses of new customers -val stagedUpdates = newAddressesToInsert - .selectExpr("NULL as mergeKey", "updates.*") // Rows for 1. - .union( - updatesDF.selectExpr("updates.customerId as mergeKey", "*") // Rows for 2. - ) - -// Apply SCD Type 2 operation using merge -customersTable - .as("customers") - .merge( - stagedUpdates.as("staged_updates"), - "customers.customerId = mergeKey") - .whenMatched("customers.current = true AND customers.address <> staged_updates.address") - .updateExpr(Map( // Set current to false and endDate to source's effective date. - "current" -> "false", - "endDate" -> "staged_updates.effectiveDate")) - .whenNotMatched() - .insertExpr(Map( - "customerid" -> "staged_updates.customerId", - "address" -> "staged_updates.address", - "current" -> "true", - "effectiveDate" -> "staged_updates.effectiveDate", // Set current to true along with the new address and its effective date. - "endDate" -> "null")) - .execute() -``` - -```python -customersTable = ... # DeltaTable with schema (customerId, address, current, effectiveDate, endDate) - -updatesDF = ... # DataFrame with schema (customerId, address, effectiveDate) - -# Rows to INSERT new addresses of existing customers -newAddressesToInsert = updatesDF \ - .alias("updates") \ - .join(customersTable.toDF().alias("customers"), "customerid") \ - .where("customers.current = true AND updates.address <> customers.address") - -# Stage the update by unioning two sets of rows -# 1. Rows that will be inserted in the whenNotMatched clause -# 2. Rows that will either update the current addresses of existing customers or insert the new addresses of new customers -stagedUpdates = ( - newAddressesToInsert - .selectExpr("NULL as mergeKey", "updates.*") # Rows for 1 - .union(updatesDF.selectExpr("updates.customerId as mergeKey", "*")) # Rows for 2. -) - -# Apply SCD Type 2 operation using merge -customersTable.alias("customers").merge( - stagedUpdates.alias("staged_updates"), - "customers.customerId = mergeKey") \ -.whenMatchedUpdate( - condition = "customers.current = true AND customers.address <> staged_updates.address", - set = { # Set current to false and endDate to source's effective date. - "current": "false", - "endDate": "staged_updates.effectiveDate" - } -).whenNotMatchedInsert( - values = { - "customerid": "staged_updates.customerId", - "address": "staged_updates.address", - "current": "true", - "effectiveDate": "staged_updates.effectiveDate", # Set current to true along with the new address and its effective date. - "endDate": "null" - } -).execute() -``` - - - -### Write change data into a Delta table - -Similar to SCD, another common use case, often called change data capture (CDC), is to apply -all data changes generated from an external database into a Delta table. In other words, a set -of updates, deletes, and inserts applied to an external table needs to be applied to a Delta table. -You can do this using `merge` as follows. - - - -```scala -val deltaTable: DeltaTable = ... // DeltaTable with schema (key, value) - -// DataFrame with changes having following columns -// - key: key of the change -// - time: time of change for ordering between changes (can replaced by other ordering id) -// - newValue: updated or inserted value if key was not deleted -// - deleted: true if the key was deleted, false if the key was inserted or updated -val changesDF: DataFrame = ... - -// Find the latest change for each key based on the timestamp -// Note: For nested structs, max on struct is computed as -// max on first struct field, if equal fall back to second fields, and so on. -val latestChangeForEachKey = changesDF - .selectExpr("key", "struct(time, newValue, deleted) as otherCols" ) - .groupBy("key") - .agg(max("otherCols").as("latest")) - .selectExpr("key", "latest.*") - -deltaTable.as("t") - .merge( - latestChangeForEachKey.as("s"), - "s.key = t.key") - .whenMatched("s.deleted = true") - .delete() - .whenMatched() - .updateExpr(Map("key" -> "s.key", "value" -> "s.newValue")) - .whenNotMatched("s.deleted = false") - .insertExpr(Map("key" -> "s.key", "value" -> "s.newValue")) - .execute() -``` - -```python -deltaTable = ... # DeltaTable with schema (key, value) - -# DataFrame with changes having following columns -# - key: key of the change -# - time: time of change for ordering between changes (can replaced by other ordering id) -# - newValue: updated or inserted value if key was not deleted -# - deleted: true if the key was deleted, false if the key was inserted or updated -changesDF = spark.table("changes") - -# Find the latest change for each key based on the timestamp -# Note: For nested structs, max on struct is computed as -# max on first struct field, if equal fall back to second fields, and so on. -latestChangeForEachKey = changesDF \ - .selectExpr("key", "struct(time, newValue, deleted) as otherCols") \ - .groupBy("key") \ - .agg(max("otherCols").alias("latest")) \ - .select("key", "latest.*") \ - -deltaTable.alias("t").merge( - latestChangeForEachKey.alias("s"), - "s.key = t.key") \ - .whenMatchedDelete(condition = "s.deleted = true") \ - .whenMatchedUpdate(set = { - "key": "s.key", - "value": "s.newValue" - }) \ - .whenNotMatchedInsert( - condition = "s.deleted = false", - values = { - "key": "s.key", - "value": "s.newValue" - } - ).execute() -``` - - - -### Upsert from streaming queries using `foreachBatch` - -You can use a combination of `merge` and `foreachBatch` (see [foreachbatch](https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#foreachbatch) for more information) to write complex upserts from a streaming query into a Delta table. For example: - -- **Write streaming aggregates in Update Mode**: This is much more efficient than Complete Mode. - - - -```scala -import io.delta.tables.* - -val deltaTable = DeltaTable.forPath(spark, "/data/aggregates") - -// Function to upsert microBatchOutputDF into Delta table using merge -def upsertToDelta(microBatchOutputDF: DataFrame, batchId: Long) { - deltaTable.as("t") - .merge( - microBatchOutputDF.as("s"), - "s.key = t.key") - .whenMatched().updateAll() - .whenNotMatched().insertAll() - .execute() -} - -// Write the output of a streaming aggregation query into Delta table -streamingAggregatesDF.writeStream - .format("delta") - .foreachBatch(upsertToDelta _) - .outputMode("update") - .start() -``` - -```python -from delta.tables import * - -deltaTable = DeltaTable.forPath(spark, "/data/aggregates") - -# Function to upsert microBatchOutputDF into Delta table using merge -def upsertToDelta(microBatchOutputDF, batchId): - deltaTable.alias("t").merge( - microBatchOutputDF.alias("s"), - "s.key = t.key") \ - .whenMatchedUpdateAll() \ - .whenNotMatchedInsertAll() \ - .execute() -} - -# Write the output of a streaming aggregation query into Delta table -streamingAggregatesDF.writeStream \ - .format("delta") \ - .foreachBatch(upsertToDelta) \ - .outputMode("update") \ - .start() -``` - - - -- **Write a stream of database changes into a Delta table**: The [merge query for writing change data](#write-change-data-into-a-delta-table) can be used in `foreachBatch` to continuously apply a stream of changes to a Delta table. - -- **Write a stream data into Delta table with deduplication**: The [insert-only merge query for deduplication](#data-deduplication-when-writing-into-delta-tables) can be used in `foreachBatch` to continuously write data (with duplicates) to a Delta table with automatic deduplication. - - - -- Make sure that your `merge` statement inside `foreachBatch` is idempotent as restarts - of the streaming query can apply the operation on the same batch of data multiple times. -- When `merge` is used in `foreachBatch`, the input data rate of the streaming query - (reported through `StreamingQueryProgress` and visible in the notebook rate graph) may be reported - as a multiple of the actual rate at which data is generated at the source. This is because `merge` reads the input data multiple times causing the input metrics to be multiplied. If this is a bottleneck, you can cache the batch DataFrame before `merge` and then uncache it after `merge`. - - \ No newline at end of file diff --git a/src/pages/latest/delta-utility.mdx b/src/pages/latest/delta-utility.mdx deleted file mode 100644 index d576d78..0000000 --- a/src/pages/latest/delta-utility.mdx +++ /dev/null @@ -1,503 +0,0 @@ ---- -title: Utility Operations -width: full -menu: docs ---- - -Delta tables support a number of utility operations. - -For many Delta Lake operations, you enable integration with Apache Spark DataSourceV2 and Catalog APIs (since 3.0) by setting configurations when you create a new `SparkSession`. See [Configure SparkSession](/latest/delta-batch/#configure-sparksession). - -## Remove files no longer referenced by a Delta table - -You can remove files no longer referenced by a Delta table and are older than the retention -threshold by running the `vacuum` command on the table. `vacuum` is not triggered automatically. The -default retention threshold for the files is 7 days. To change this behavior, see [Data retention](/latest/delta-batch/#data-retention). - - - -- `vacuum` removes all files from directories not managed by Delta Lake, ignoring directories beginning with _. If you are storing additional metadata like Structured Streaming checkpoints within a Delta table directory, use a directory name such as `_checkpoints`. - -- `vacuum` deletes only data files, not log files. Log files are deleted automatically and asynchronously after checkpoint operations. The default retention period of log files is 30 days, configurable through the `delta.logRetentionDuration` property which you set with the `ALTER TABLE SET TBLPROPERTIES SQL` method. See [Table properties](/latest/delta-batch/#table-properties). - -- The ability to time travel back to a version older than the retention period is lost after running vacuum. - - - - - -```sql -VACUUM eventsTable -- vacuum files not required by versions older than the default retention period - -VACUUM '/data/events' -- vacuum files in path-based table - -VACUUM delta.`/data/events/` - -VACUUM delta.`/data/events/` RETAIN 100 HOURS -- vacuum files not required by versions more than 100 hours old - -VACUUM eventsTable DRY RUN -- do dry run to get the list of files to be deleted -```` - -```python -from delta.tables import * - -deltaTable = DeltaTable.forPath(spark, pathToTable) # path-based tables, or -deltaTable = DeltaTable.forName(spark, tableName) # Hive metastore-based tables - -deltaTable.vacuum() # vacuum files not required by versions older than the default retention period - -deltaTable.vacuum(100) # vacuum files not required by versions more than 100 hours old -```` - -```scala -import io.delta.tables._ - -val deltaTable = DeltaTable.forPath(spark, pathToTable) - -deltaTable.vacuum() // vacuum files not required by versions older than the default retention period - -deltaTable.vacuum(100) // vacuum files not required by versions more than 100 hours old -``` - -```java -import io.delta.tables.*; -import org.apache.spark.sql.functions; - -DeltaTable deltaTable = DeltaTable.forPath(spark, pathToTable); - -deltaTable.vacuum(); // vacuum files not required by versions older than the default retention period - -deltaTable.vacuum(100); // vacuum files not required by versions more than 100 hours old -``` - - - - -When using `VACUUM`, to configure Spark to delete files in parallel (based on the number of shuffle partitions) set the session configuration `"spark.databricks.delta.vacuum.parallelDelete.enabled"` to `"true"`. - - -See the [Delta Lake APIs](/latest/delta-apidoc) for Scala, Java, and Python syntax details. - - -It is recommended that you set a retention interval to be at least 7 days, because old snapshots and uncommitted files can still be in use by concurrent readers or writers to the table. If `VACUUM` cleans up active files, concurrent readers can fail or, worse, tables can be corrupted when `VACUUM` deletes files that have not yet been committed. You must choose an interval that is longer than the longest running concurrent transaction and the longest period that any stream can lag behind the most recent update to the table. - - -Delta Lake has a safety check to prevent you from running a dangerous `VACUUM` command. If you are certain that there are no operations being performed on this table that take longer than the retention interval you plan to specify, you can turn off this safety check by setting the Spark configuration property `spark.databricks.delta.retentionDurationCheck.enabled` to `false`. - -## Retrieve Delta table history - -You can retrieve information on the operations, user, timestamp, and so on for each write to a Delta table -by running the `history` command. The operations are returned in reverse chronological order. By default table history is retained for 30 days. - - -```sql -DESCRIBE HISTORY '/data/events/' -- get the full history of the table - -DESCRIBE HISTORY delta.`/data/events/` - -DESCRIBE HISTORY '/data/events/' LIMIT 1 -- get the last operation only - -DESCRIBE HISTORY eventsTable - -```` - -```python -from delta.tables import * - -deltaTable = DeltaTable.forPath(spark, pathToTable) - -fullHistoryDF = deltaTable.history() # get the full history of the table - -lastOperationDF = deltaTable.history(1) # get the last operation -```` - -```scala -import io.delta.tables._ - -val deltaTable = DeltaTable.forPath(spark, pathToTable) - -val fullHistoryDF = deltaTable.history() // get the full history of the table - -val lastOperationDF = deltaTable.history(1) // get the last operation -``` - -```java -import io.delta.tables.*; - -DeltaTable deltaTable = DeltaTable.forPath(spark, pathToTable); - -DataFrame fullHistoryDF = deltaTable.history(); // get the full history of the table - -DataFrame lastOperationDF = deltaTable.history(1); // fetch the last operation on the DeltaTable -``` - - - -See the [Delta Lake APIs](/latest/delta-apidoc) for Scala, Java, and Python syntax details. - -### History schema - -The output of the `history` operation has the following columns. - -| Column | Type | Description | -| ------------------- | --------- | -------------------------------------------------------------------------- | -| version | long | Table version generated by the operation. | -| timestamp | timestamp | When this version was committed. | -| userId | string | ID of the user that ran the operation. | -| userName | string | Name of the user that ran the operation. | -| operation | string | Name of the operation. | -| operationParameters | map | Parameters of the operation (for example, predicates.) | -| job | struct | Details of the job that ran the operation. | -| notebook | struct | Details of notebook from which the operation was run. | -| clusterId | string | ID of the cluster on which the operation ran. | -| readVersion | long | Version of the table that was read to perform the write operation. | -| isolationLevel | string | Isolation level used for this operation. | -| isBlindAppend | boolean | Whether this operation appended data. | -| operationMetrics | map | Metrics of the operation (for example, number of rows and files modified.) | -| userMetadata | string | User-defined commit metadata if it was specified | - -``` -+-------+-------------------+------+--------+---------+--------------------+----+--------+---------+-----------+--------------+-------------+--------------------+ -|version| timestamp|userId|userName|operation| operationParameters| job|notebook|clusterId|readVersion|isolationLevel|isBlindAppend| operationMetrics| -+-------+-------------------+------+--------+---------+--------------------+----+--------+---------+-----------+--------------+-------------+--------------------+ -| 5|2019-07-29 14:07:47| null| null| DELETE|[predicate -> ["(...|null| null| null| 4| Serializable| false|[numTotalRows -> ...| -| 4|2019-07-29 14:07:41| null| null| UPDATE|[predicate -> (id...|null| null| null| 3| Serializable| false|[numTotalRows -> ...| -| 3|2019-07-29 14:07:29| null| null| DELETE|[predicate -> ["(...|null| null| null| 2| Serializable| false|[numTotalRows -> ...| -| 2|2019-07-29 14:06:56| null| null| UPDATE|[predicate -> (id...|null| null| null| 1| Serializable| false|[numTotalRows -> ...| -| 1|2019-07-29 14:04:31| null| null| DELETE|[predicate -> ["(...|null| null| null| 0| Serializable| false|[numTotalRows -> ...| -| 0|2019-07-29 14:01:40| null| null| WRITE|[mode -> ErrorIfE...|null| null| null| null| Serializable| true|[numFiles -> 2, n...| -+-------+-------------------+------+--------+---------+--------------------+----+--------+---------+-----------+--------------+-------------+--------------------+ -``` - - - -- Some of the columns may be nulls because the corresponding information may not be available in your environment. - -- Columns added in the future will always be added after the last column - - - -### Operation metrics keys - -The `history` operation returns a collection of operations metrics in the `operationMetrics` column map. - -The following table lists the map key definitions by operation. - -| Operation | Metric Name | Description | -| ----------------------------------------------------------------- | ---------------------- | ------------------------------------------------------------------------------ | -| `WRITE`, `CREATE TABLE AS SELECT`, `REPLACE TABLE AS SELECT`, `COPY INTO` | | | -| | numFiles | Number of files written. | -| | numOutputBytes | Size in bytes of the written contents. | -| | numOutputRows | Number of rows written. | -| `STREAMING UPDATE ` | | | -| | numAddedFiles | Number of files added. | -| | numRemovedFiles | Number of files removed. | -| | numOutputRows | Number of rows written. | -| | numOutputBytes | Size of write in bytes. | -| `DELETE` | | | -| | numAddedFiles | Number of files added. Not provided when partitions of the table are deleted. | -| | numRemovedFiles | Number of files removed. | -| | numDeletedRows | Number of rows removed. Not provided when partitions of the table are deleted. | -| | numCopiedRows | Number of rows copied in the process of deleting files. | -| | executionTimeMs | Time taken to execute the entire operation. | -| | scanTimeMs | Time taken to scan the files for matches. | -| | rewriteTimeMs | Time taken to rewrite the matched files. | -| `TRUNCATE` | | | -| | numRemovedFiles | Number of files removed. | -| | executionTimeMs | Time taken to execute the entire operation. | -| `MERGE` | | | -| | numSourceRows | Number of rows in the source DataFrame. | -| | numTargetRowsInserted | Number of rows inserted into the target table. | -| | numTargetRowsUpdated | Number of rows updated in the target table. | -| | numTargetRowsDeleted | Number of rows deleted in the target table. | -| | numTargetRowsCopied | Number of target rows copied. | -| | numOutputRows | Total number of rows written out. | -| | numTargetFilesAdded | Number of files added to the sink(target). | -| | numTargetFilesRemoved | Number of files removed from the sink(target). | -| | executionTimeMs | Time taken to execute the entire operation. | -| | scanTimeMs | Time taken to scan the files for matches. | -| | rewriteTimeMs | Time taken to rewrite the matched files. | -| `UPDATE` | | | -| | numAddedFiles | Number of files added. | -| | numRemovedFiles | Number of files removed. | -| | numUpdatedRows | Number of rows updated. | -| | numCopiedRows | Number of rows just copied over in the process of updating files. | -| | executionTimeMs | Time taken to execute the entire operation. | -| | scanTimeMs | Time taken to scan the files for matches. | -| | rewriteTimeMs | Time taken to rewrite the matched files. | -| `FSCK` | | | -| | numRemovedFiles | Number of files removed. | -| `CONVERT` | | | -| | numConvertedFiles | Number of Parquet files that have been converted. | -| `OPTIMIZE` | | | -| | numAddedFiles | Number of files added. | -| | numRemovedFiles | Number of files optimized. | -| | numAddedBytes | Number of bytes added after the table was optimized. | -| | numRemovedBytes | Number of bytes removed. | -| | minFileSize | Size of the smallest file after the table was optimized. | -| | p25FileSize | Size of the 25th percentile file after the table was optimized. | -| | p50FileSize | Median file size after the table was optimized. | -| | p75FileSize | Size of the 75th percentile file after the table was optimized. | -| | maxFileSize | Size of the largest file after the table was optimized. | -| `RESTORE` | | | -| | tableSizeAfterRestore | Table size in bytes after restore. | -| | numOfFilesAfterRestore | Number of files in the table after restore. | -| | numRemovedFiles | Number of files removed by the restore operation. | -| | numRestoredFiles | Number of files that were added as a result of the restore. | -| | removedFilesSize | Size in bytes of files removed by the restore. | -| | restoredFilesSize | Size in bytes of files added by the restore. | - - -## Retrieve Delta table details - -You can retrieve detailed information about a Delta table (for example, number of files, data size) using `DESCRIBE DETAIL`. - - - -```sql -DESCRIBE DETAIL '/data/events/' - -DESCRIBE DETAIL eventsTable -``` - -```python -from delta.tables import * - -deltaTable = DeltaTable.forPath(spark, pathToTable) - -detailDF = deltaTable.detail() -``` - -```scala -import io.delta.tables._ - -val deltaTable = DeltaTable.forPath(spark, pathToTable) - -val detailDF = deltaTable.detail() -``` - -```java -import io.delta.tables.*; - -DeltaTable deltaTable = DeltaTable.forPath(spark, pathToTable); - -DataFrame detailDF = deltaTable.detail(); -``` - - - -See the [Delta Lake APIs](/latest/delta-apidoc) for Scala, Java, and Python syntax details. - -### Detail schema - -The output of this operation has only one row with the following schema. - -| Column | Type | Description | -| ---------------- | ----------------- | --------------------------------------------------------------------------------------- | -| format | string | Format of the table, that is, `delta`. | -| id | string | Unique ID of the table. | -| name | string | Name of the table as defined in the metastore. | -| description | string | Description of the table. | -| location | string | Location of the table. | -| createdAt | timestamp | When the table was created. | -| lastModified | timestamp | When the table was last modified. | -| partitionColumns | array of strings | Names of the partition columns if the table is partitioned. | -| numFiles | long | Number of the files in the latest version of the table. | -| sizeInBytes | int | The size of the latest snapshot of the table in bytes. | -| properties | string-string map | All the properties set for this table. | -| minReaderVersion | int | Minimum version of readers (according to the log protocol) that can read the table. | -| minWriterVersion | int | Minimum version of writers (according to the log protocol) that can write to the table. | - -```text -+------+--------------------+------------------+-----------+--------------------+--------------------+-------------------+----------------+--------+-----------+----------+----------------+----------------+ -|format| id| name|description| location| createdAt| lastModified|partitionColumns|numFiles|sizeInBytes|properties|minReaderVersion|minWriterVersion| -+------+--------------------+------------------+-----------+--------------------+--------------------+-------------------+----------------+--------+-----------+----------+----------------+----------------+ -| delta|d31f82d2-a69f-42e...|default.deltatable| null|file:/Users/tuor/...|2020-06-05 12:20:...|2020-06-05 12:20:20| []| 10| 12345| []| 1| 2| -+------+--------------------+------------------+-----------+--------------------+--------------------+-------------------+----------------+--------+-----------+----------+----------------+----------------+ -``` - - - -## Generate a manifest file - -You can a generate manifest file for a Delta table that can be used by other processing engines (that is, other than Apache Spark) to read the Delta table. For example, to generate a manifest file that can be used by Presto and Athena to read a Delta table, you run the following: - - - -```sql -GENERATE symlink_format_manifest FOR TABLE delta.`` -``` - -```python -deltaTable = DeltaTable.forPath() -deltaTable.generate("symlink_format_manifest") -``` - -```scala -val deltaTable = DeltaTable.forPath() -deltaTable.generate("symlink_format_manifest") -``` - -```java -DeltaTable deltaTable = DeltaTable.forPath(); -deltaTable.generate("symlink_format_manifest"); -``` - -See [Configure SparkSession](/latest/delta-batch/#configure-sparksession) for the steps to enable support for SQL commands in Apache Spark. - - - -## Convert a Parquet table to a Delta table - -Convert a Parquet table to a Delta table in-place. This command lists all the files in the directory, creates a Delta Lake transaction log that tracks these files, and automatically infers the data schema by reading the footers of all Parquet files. If your data is partitioned, you must specify the schema of the partition columns as a DDL-formatted string (that is, ` , , ...`). - -By default, this command will collect per-file statistics (e.g. minimum and maximum values for each column). These statistics will be used at query time to provide faster queries. You can disable this statistics collection in the SQL API using `NO STATISTICS`. - - - -If a Parquet table was created by Structured Streaming, the listing of files can be avoided by using the `_spark_metadata` sub-directory as the source of truth for files contained in the table setting the SQL configuration `spark.databricks.delta.convert.useMetadataLog` to `true`. - - - - -```sql --- Convert unpartitioned Parquet table at path '' -CONVERT TO DELTA parquet.`` - --- Convert unpartitioned Parquet table and disable statistics collection -CONVERT TO DELTA parquet.`` NO STATISTICS - --- Convert partitioned Parquet table at path '' and partitioned by integer columns named 'part' and 'part2' -CONVERT TO DELTA parquet.`` PARTITIONED BY (part int, part2 int) - --- Convert partitioned Parquet table and disable statistics collection -CONVERT TO DELTA parquet.`` NO STATISTICS PARTITIONED BY (part int, part2 int) -```` - -```python -from delta.tables import * - -# Convert unpartitioned Parquet table at path '' -deltaTable = DeltaTable.convertToDelta(spark, "parquet.``") - -# Convert partitioned parquet table at path '' and partitioned by integer column named 'part' -partitionedDeltaTable = DeltaTable.convertToDelta(spark, "parquet.``", "part int") -```` - -```scala -import io.delta.tables._ - -// Convert unpartitioned Parquet table at path '' -val deltaTable = DeltaTable.convertToDelta(spark, "parquet.``") - -// Convert partitioned Parquet table at path '' and partitioned by integer columns named 'part' and 'part2' -val partitionedDeltaTable = DeltaTable.convertToDelta(spark, "parquet.``", "part int, part2 int") -``` - -```java -import io.delta.tables.*; - -// Convert unpartitioned Parquet table at path '' -DeltaTable deltaTable = DeltaTable.convertToDelta(spark, "parquet.``"); - -// Convert partitioned Parquet table at path '' and partitioned by integer columns named 'part' and 'part2' -DeltaTable deltaTable = DeltaTable.convertToDelta(spark, "parquet.``", "part int, part2 int"); -``` - - - - - -Any file not tracked by Delta Lake is invisible and can be deleted when you run `vacuum`. You should avoid updating or appending data files during the conversion process. After the table is converted, make sure all writes go through Delta Lake. - - - -## Convert a Delta table to a Parquet table - -You can easily convert a Delta table back to a Parquet table using the following steps: - -1. If you have performed Delta Lake operations that can change the data files (for example, `delete` or `merge`), run [vacuum](#remove-files-no-longer-referenced-by-a-delta-table) with retention of 0 hours to delete all data files that do not belong to the latest version of the table. -2. Delete the `_delta_log` directory in the table directory. - -## Restore a Delta table to an earlier state - -You can restore a Delta table to its earlier state by using the `RESTORE` command. A Delta table internally maintains historic versions of the table that enable it to be restored to an earlier state. A version corresponding to the earlier state or a timestamp of when the earlier state was created are supported as options by the RE`STORE command. - - - -- You can restore an already restored table. -- Restoring a table to an older version where the data files were deleted manually or by `vacuum` will fail. Restoring to this version partially is still possible if `spark.sql.files.ignoreMissingFiles` is set to `true`. -- The timestamp format for restoring to an earlier state is `yyyy-MM-dd HH:mm:ss`. Providing only a date(`yyyy-MM-dd`) string is also supported. - - - - - -```sql -RESTORE TABLE db.target_table TO VERSION AS OF -RESTORE TABLE delta.`/data/target/` TO TIMESTAMP AS OF -``` - -```python -from delta.tables import * - -deltaTable = DeltaTable.forPath(spark, ) # path-based tables, or -deltaTable = DeltaTable.forName(spark, ) # Hive metastore-based tables - -deltaTable.restoreToVersion(0) # restore table to oldest version - -deltaTable.restoreToTimestamp('2019-02-14') # restore to a specific timestamp -``` - -```scala -import io.delta.tables._ - -val deltaTable = DeltaTable.forPath(spark, ) -val deltaTable = DeltaTable.forName(spark, ) - -deltaTable.restoreToVersion(0) // restore table to oldest version - -deltaTable.restoreToTimestamp("2019-02-14") // restore to a specific timestamp -``` - -```java -import io.delta.tables.*; - -DeltaTable deltaTable = DeltaTable.forPath(spark, ); -DeltaTable deltaTable = DeltaTable.forName(spark, ); - -deltaTable.restoreToVersion(0) // restore table to oldest version - -deltaTable.restoreToTimestamp("2019-02-14") // restore to a specific timestamp -``` - - - - - - -Restore is considered a data-changing operation. Delta Lake log entries added by the RESTORE command contain dataChange set to true. If there is a downstream application, such as a Structured streaming job that processes the updates to a Delta Lake table, the data change log entries added by the restore operation are considered as new data updates, and processing them may result in duplicate data. - -For example: - -| Table Version | Operation | Delta Log updates | Records in data change log updates | -| - | - | - | - | -| 0 | `INSERT` | `AddFile(/path/to/file-1, dataChange = true)` | (name = Viktor, age = 29, (name = George, age = 55) | -| 1 | `INSERT` | `AddFile(/path/to/file-2, dataChange = true)` | (name = George, age = 39) | -| 2 | `OPTIMIZE` | `AddFile(/path/to/file-3, dataChange = false)`, `RemoveFile(/path/to/file-1)`, `RemoveFile(/path/to/file-2)` | (No records as Optimize compaction does not change the data in the table) | -| 3 | `RESTORE(version=1)` | `RemoveFile(/path/to/file-3)`, `AddFile(/path/to/file-1, dataChange = true)`, `AddFile(/path/to/file-2, dataChange = true)` | (name = Viktor, age = 29), (name = George, age = 55), (name = George, age = 39) | - -In the preceding example, the `RESTORE` command results in updates that were already seen when reading the Delta table version 0 and 1. If a streaming query was reading this table, then these files will be considered as newly added data and will be processed again. - - - -### Restore Metrics - -`RESTORE` reports the following metrics as a single row DataFrame once the operation is complete: - -- `table_size_after_restore`: The size of the table after restoring. -- `num_of_files_after_restore`: The number of files in the table after restoring. -- `num_removed_files`: Number of files removed (logically deleted) from the table. -- `num_restored_files`: Number of files restored due to rolling back. -- `removed_files_size`: Total size in bytes of the files that are removed from the table. -- `restored_files_size`: Total size in bytes of the files that are restored. \ No newline at end of file diff --git a/src/pages/latest/getting-started.mdx b/src/pages/latest/getting-started.mdx deleted file mode 100644 index 55c0a86..0000000 --- a/src/pages/latest/getting-started.mdx +++ /dev/null @@ -1,43 +0,0 @@ ---- -title: Getting Started with Delta Lake Spark -description: Learn how to start using Delta Lake Spark ---- - -Lorem ipsum dolor sit amet. - - - -```jsx -export const MyComponent = (props) => { - const { title, children } = props; - - return ( -
-

{title}

-

{children}

-
- ); -}; -``` - -```tsx -import type { ReactNode } from "react"; - -interface MyComponentProps { - title: string; - children?: ReactNode; -} - -export const MyComponent = (props): ReactElement => { - const { title, children } = props; - - return ( -
-

{title}

-

{children}

-
- ); -}; -``` - - diff --git a/src/pages/latest/index.mdx b/src/pages/latest/index.mdx deleted file mode 100644 index b3b6501..0000000 --- a/src/pages/latest/index.mdx +++ /dev/null @@ -1,38 +0,0 @@ ---- -title: Delta Lake Spark -description: Learn about Delta Lake features and resources to learn about Delta Lake. ---- - -[Delta Lake](https://delta.io) is an [open source -project](https://github.com/delta-io/delta) that enables building a [Lakehouse -architecture](https://databricks.com/blog/2020/01/30/what-is-a-data-lakehouse.html) -on top of [data lakes](https://databricks.com/discover/data-lakes/introduction). - -Delta Lake provides [ACID transactions](concurrency-control.md), scalable -metadata handling, and unifies [streaming](delta-streaming.md) and -[batch](delta-batch.md) data processing on top of existing data lakes, such as -S3, ADLS, GCS, and HDFS. - -For a quick overview and benefits of Delta Lake, watch this YouTube video (3 -minutes). - -Specifically, Delta Lake offers: - -- [ACID transactions](concurrency-control.md) on Spark: Serializable isolation - levels ensure that readers never see inconsistent data. -- Scalable metadata handling: Leverages Spark distributed processing power to - handle all the metadata for petabyte-scale tables with billions of files at - ease. -- [Streaming](delta-streaming.md) and [batch](delta-batch.md) unification: A - table in Delta Lake is a batch table as well as a streaming source and sink. - Streaming data ingest, batch historic backfill, interactive queries all just - work out of the box. -- Schema enforcement: Automatically handles schema variations to prevent - insertion of bad records during ingestion. -- [Time travel](delta-batch.md#deltatimetravel): Data versioning enables - rollbacks, full historical audit trails, and reproducible machine learning - experiments. -- [Upserts](delta-update.md#delta-merge) and - [deletes](delta-update.md#delta-delete): Supports merge, update and delete - operations to enable complex use cases like change-data-capture, - slowly-changing-dimension (SCD) operations, streaming upserts, and so on. diff --git a/src/pages/latest/integrations.mdx b/src/pages/latest/integrations.mdx deleted file mode 100644 index 119c1ff..0000000 --- a/src/pages/latest/integrations.mdx +++ /dev/null @@ -1,23 +0,0 @@ ---- -title: Access Delta tables from external data processing engines -description: Docs for accessesing Delta tables from external data processing engines ---- - -You can access Delta tables from Apache Spark and [other data processing systems](https://delta.io/integrations/). Here is the list of integrations that enable you to access Delta tables from external data processing engines. - -## Presto to Delta Lake integration - -Since Presto [version 0.269](https://prestodb.io/docs/0.269/release/release-0.269.html#delta-lake-connector-changes), Presto natively supports reading Delta Lake tables. For details on using the native Delta Lake connector, see [Delta Lake Connector - Presto](https://prestodb.io/docs/current/connector/deltalake.html). For Presto versions [lower than 0.269](https://prestodb.io/docs/0.269/release/release-0.269.html#delta-lake-connector-changes), you can use the manifest-based approach detailed in [Presto, Trino, and Athena to Delta Lake integration using manifests](https://docs.delta.io/latest/presto-integration.html). - -## Trino to Delta Lake integration - -Since Trino version 373, Trino natively supports reading and writing the Delta Lake tables. For details on using the native Delta Lake connector, see Delta Lake Connector - Trino. For Trino versions lower than version 373, you can use the manifest-based approach detailed in Presto, Trino, and Athena to Delta Lake integration using manifests. - -## Other integrations - -- [Presto, Trino, and Athena to Delta Lake integration using manifests](/latest/presto-integration.html) -- [Redshift Spectrum to Delta Lake integration](/latest/redshift-spectrum-integration.html) -- [Delta Lake to Snowflake integration](/snowflake-integration.html) -- [Apache Hive to Delta Lake integration](/latest/hive-integration.html) -- [Delta Lake to Apache Flink integration](/latest/flink-integration.html) -- [Delta Standalone](/latest/delta-standalone.html) diff --git a/src/pages/latest/optimizations-oss.mdx b/src/pages/latest/optimizations-oss.mdx deleted file mode 100644 index 49c17b7..0000000 --- a/src/pages/latest/optimizations-oss.mdx +++ /dev/null @@ -1,166 +0,0 @@ ---- -title: Optimizations -description: Optimizations for running analyses on Delta Lakes faster ---- - -Delta Lake provides optimizations that accelerate data lake operations. - -## Optimize performance with file management - -To improve query speed, Delta Lake supports the ability to optimize the layout of data in storage. There are various ways to optimize the layout. - -### Compaction (bin-packing) - - -Note - -This feature is available in Delta Lake 1.2.0 and above. - - -Delta Lake can improve the speed of read queries from a table by coalescing small files into larger ones. - - - -```sql -OPTIMIZE '/path/to/delta/table' -- Optimizes the path-based Delta Lake table - -OPTIMIZE delta_table_name; - -OPTIMIZE delta.`/path/to/delta/table`; - --- If you have a large amount of data and only want to optimize a subset of it, you can specify an optional partition predicate using `WHERE`: -OPTIMIZE delta_table_name WHERE date >= '2017-01-01' -``` - -```python -from delta.tables import * - -deltaTable = DeltaTable.forPath(spark, pathToTable) # For path-based tables -# For Hive metastore-based tables: deltaTable = DeltaTable.forName(spark, tableName) - -deltaTable.optimize().executeCompaction() - -# If you have a large amount of data and only want to optimize a subset of it, you can specify an optional partition predicate using `where` -deltaTable.optimize().where("date='2021-11-18'").executeCompaction() -``` - -```scala -import io.delta.tables._ - -val deltaTable = DeltaTable.forPath(spark, pathToTable) // For path-based tables -// For Hive metastore-based tables: val deltaTable = DeltaTable.forName(spark, tableName) - -deltaTable.optimize().executeCompaction() - -// If you have a large amount of data and only want to optimize a subset of it, you can specify an optional partition predicate using `where` -deltaTable.optimize().where("date='2021-11-18'").executeCompaction() -``` - - - -For Scala, Java, and Python API syntax details, see the [Delta Lake APIs](/latest/delta-apidoc.html). - - -Note - -* Bin-packing optimization is _idempotent_, meaning that if it is run twice on the same dataset, the second run has no effect. - -* Bin-packing aims to produce evenly-balanced data files with respect to their size on disk, but not necessarily number of tuples per file. However, the two measures are most often correlated. - -* Python and Scala APIs for executing `OPTIMIZE` operation are available from Delta Lake 2.0 and above. - -* Set Spark session configuration `spark.databricks.delta.optimize.repartition.enabled=true` to use `repartition(1)` instead of `coalesce(1)` for better performance when compacting many small files. - - -Readers of Delta tables use snapshot isolation, which means that they are not interrupted when `OPTIMIZE` removes unnecessary files from the transaction log. `OPTIMIZE` makes no data related changes to the table, so a read before and after an `OPTIMIZE` has the same results. Performing `OPTIMIZE` on a table that is a streaming source does not affect any current or future streams that treat this table as a source. `OPTIMIZE` returns the file statistics (min, max, total, and so on) for the files removed and the files added by the operation. Optimize stats also contains the number of batches, and partitions optimized. - -## Data skipping - - -Note - -This feature is available in Delta Lake 1.2.0 and above. - - -Data skipping information is collected automatically when you write data into a Delta Lake table. Delta Lake takes advantage of this information (minimum and maximum values for each column) at query time to provide faster queries. You do not need to configure data skipping; the feature is activated whenever applicable. However, its effectiveness depends on the layout of your data. For best results, apply [Z-Ordering](/latest/optimizations-oss.html#-z-ordering-multi-dimensional-clustering). - -Collecting statistics on a column containing long values such as string or binary is an expensive operation. To avoid collecting statistics on such columns you can configure the [table property](/latest/delta-batch.html#-table-properties) `delta.dataSkippingNumIndexedCols`. This property indicates the position index of a column in the table’s schema. All columns with a position index less than the `delta.dataSkippingNumIndexedCols` property will have statistics collected. For the purposes of collecting statistics, each field within a nested column is considered as an individual column. To avoid collecting statistics on columns containing long values, either set the `delta.dataSkippingNumIndexedCols` property so that the long value columns are after this index in the table’s schema, or move columns containing long strings to an index position greater than the `delta.dataSkippingNumIndexedCols` property by using `[ALTER TABLE ALTER COLUMN](/latest/sql-ref-syntax-ddl-alter-table.html#alter-or-change-column)`. - -## Z-Ordering (multi-dimensional clustering) - - -Note - -This feature is available in Delta Lake 2.0.0 and above. - - -Z-Ordering is a [technique](https://en.wikipedia.org/wiki/Z-order_curve) to colocate related information in the same set of files. This co-locality is automatically used by Delta Lake in data-skipping algorithms. This behavior dramatically reduces the amount of data that Delta Lake on Apache Spark needs to read. To Z-Order data, you specify the columns to order on in the `ZORDER BY` clause: - - - -```sql -OPTIMIZE events ZORDER BY (eventType) - --- If you have a large amount of data and only want to optimize a subset of it, you can specify an optional partition predicate by using "where". -OPTIMIZE events WHERE date = '2021-11-18' ZORDER BY (eventType) -``` - -```python -from delta.tables import * - -deltaTable = DeltaTable.forPath(spark, pathToTable) # path-based table -# For Hive metastore-based tables: deltaTable = DeltaTable.forName(spark, tableName) - -deltaTable.optimize().executeZOrderBy(eventType) - -# If you have a large amount of data and only want to optimize a subset of it, you can specify an optional partition predicate using `where` -deltaTable.optimize().where("date='2021-11-18'").executeZOrderBy(eventType) -``` - -```scala -import io.delta.tables._ - -val deltaTable = DeltaTable.forPath(spark, pathToTable) // path-based table -// For Hive metastore-based tables: val deltaTable = DeltaTable.forName(spark, tableName) - -deltaTable.optimize().executeZOrderBy(eventType) - -// If you have a large amount of data and only want to optimize a subset of it, you can specify an optional partition predicate by using "where". -deltaTable.optimize().where("date='2021-11-18'").executeZOrderBy(eventType) -``` - - - -For Scala, Java, and Python API syntax details, see the [Delta Lake APIs](/latest/delta-apidoc.html). - -If you expect a column to be commonly used in query predicates and if that column has high cardinality (that is, a large number of distinct values), then use `ZORDER BY`. - -You can specify multiple columns for `ZORDER BY` as a comma-separated list. However, the effectiveness of the locality drops with each extra column. Z-Ordering on columns that do not have statistics collected on them would be ineffective and a waste of resources. This is because data skipping requires column-local stats such as min, max, and count. You can configure statistics collection on certain columns by reordering columns in the schema, or you can increase the number of columns to collect statistics on. See [Data skipping](https://docs.delta.io/latest/optimizations-oss.html#-data-skipping). - - -Note - -* Z-Ordering is _not idempotent_. Everytime the Z-Ordering is executed it will try to create a new clustering of data in all files (new and existing files that were part of previous Z-Ordering) in a partition. - -* Z-Ordering aims to produce evenly-balanced data files with respect to the number of tuples, but not necessarily data size on disk. The two measures are most often correlated, but there can be situations when that is not the case, leading to skew in optimize task times. - -* For example, if you `ZORDER BY` _date_ and your most recent records are all much wider (for example longer arrays or string values) than the ones in the past, it is expected that the `OPTIMIZE` job’s task durations will be skewed, as well as the resulting file sizes. This is, however, only a problem for the `OPTIMIZE` command itself; it should not have any negative impact on subsequent queries. - - -## Multi-part checkpointing - - -Note - -This feature is available in Delta Lake 2.0.0 and above. This feature is in experimental support mode. - - -Delta Lake table periodically and automatically compacts all the incremental updates to the Delta log into a Parquet file. This “checkpointing” allows read queries to quickly reconstruct the current state of the table (that is, which files to process, what is the current schema) without reading too many files having incremental updates. - -Delta Lake protocol allows [splitting the checkpoint](https://github.com/delta-io/delta/blob/master/PROTOCOL.md#checkpoints) into multiple Parquet files. This parallelizes and speeds up writing the checkpoint. In Delta Lake, by default each checkpoint is written as a single Parquet file. To to use this feature, set the SQL configuration `spark.databricks.delta.checkpoint.partSize=`, where `n` is the limit of number of actions (such as `AddFile`) at which Delta Lake on Apache Spark will start parallelizing the checkpoint and attempt to write a maximum of this many actions per checkpoint file. - - -Note - -This feature requires no reader side configuration changes. The existing reader already supports reading a checkpoint with multiple files. - diff --git a/src/pages/latest/porting.mdx b/src/pages/latest/porting.mdx deleted file mode 100644 index c885101..0000000 --- a/src/pages/latest/porting.mdx +++ /dev/null @@ -1,258 +0,0 @@ ---- -title: Migration Guide -description: Learn how to migrate existing workloads to Delta Lake on Databricks. -menu: docs ---- - -## Migrate workloads to Delta Lake - -When you migrate workloads to Delta Lake, you should be aware of the -following simplifications and differences compared with the data sources -provided by Apache Spark and Apache Hive. - -Delta Lake handles the following operations automatically, which you should -never perform manually: - -- **Add and remove partitions**: Delta Lake automatically tracks the set of - partitions present in a table and updates the list as data is added or - removed. As a result, there is no need to run `ALTER TABLE [ADD|DROP] PARTITION` or `MSCK`. - -- **Load a single partition**: As an optimization, you may sometimes directly - load the partition of data you are interested in. For example, - `spark.read.parquet("/data/date=2017-01-01")`. This is unnecessary with - Delta Lake, since it can quickly read the list of files from the - transaction log to find the relevant ones. If you are interested in a single - partition, specify it using a `WHERE` clause. For example, - `spark.read.delta("/data").where("date = '2017-01-01'")`. For large tables - with many files in the partition, this can be much faster than loading a - single partition (with direct partition path, or with `WHERE`) from a Parquet - table because listing the files in the directory is often slower than reading - the list of files from the transaction log. - -When you port an existing application to Delta Lake, you should avoid the -following operations, which bypass the transaction log: - -- **Manually modify data**: Delta Lake uses the transaction log to atomically - commit changes to the table. Because the log is the source of truth, files - that are written out but not added to the transaction log are not read by - Spark. Similarly, even if you manually delete a file, a pointer to the file is - still present in the transaction log. Instead of manually modifying files - stored in a Delta table, always use the commands that are described in this - guide. - -- **External readers**: Directly reading the data stored in Delta Lake. For - information on how to read Delta tables, see [Access Delta tables from external data processing engines](/latest/integrations). - -### Example - -Suppose you have Parquet data stored in a directory named `/data-pipeline`, and -you want to create a Delta table named `events`. - -The [first example](#save-as-delta-table) shows how to: - -- Read the Parquet data from its original location, `/data-pipeline`, into a - DataFrame. -- Save the DataFrame's contents in Delta format in a separate location, - `/tmp/delta/data-pipeline/`. -- Create the `events` table based on that separate location, - `/tmp/delta/data-pipeline/`. - -The [second example](#convert-to-delta-table) shows how to use `CONVERT TO TABLE` to convert data from Parquet to Delta format without changing its -original location, `/data-pipeline/`. - -#### Save as Delta table - -1. Read the Parquet data into a DataFrame and then save the DataFrame's contents - to a new directory in `delta` format: - - - -```python -data = spark.read.parquet("/data-pipeline") -data.write.format("delta").save("/tmp/delta/data-pipeline/") -``` - - - -2. Create a Delta table named `events` that refers to the files in the new - directory: - - - -```python -spark.sql("CREATE TABLE events USING DELTA LOCATION '/tmp/delta/data-pipeline/'") -``` - - - -#### Convert to Delta table - -You have two options for converting a Parquet table to a Delta table: - -- Convert files to Delta Lake format and then create a Delta table: - - - -```sql -CONVERT TO DELTA parquet.`/data-pipeline/` -CREATE TABLE events USING DELTA LOCATION '/data-pipeline/' -``` - - - -- Create a Parquet table and then convert it to a Delta table: - - - -```sql -CREATE TABLE events USING PARQUET OPTIONS (path '/data-pipeline/') -CONVERT TO DELTA events -``` - - - -For details, see [Convert a Parquet table to a Delta table](delta-utility.md#convert-to-delta). - -## Migrate Delta Lake workloads to newer versions - -This section discusses any changes that may be required in the user code when -migrating from older to newer versions of Delta Lake. - -### Delta Lake 1.2.1, 2.0.0, or 2.1.0 to Delta Lake 2.0.1, 2.1.1 or above - -Delta Lake 1.2.1, 2.0.0 and 2.1.0 have a bug in their DynamoDB-based S3 multi-cluster configuration implementations where an incorrect timestamp value was written to DynamoDB. This caused [DynamoDB’s TTL](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/TTL.html) feature to cleanup completed items before it was safe to do so. This has been fixed in Delta Lake versions 2.0.1 and 2.1.1, and the TTL attribute has been renamed from `commitTime` to `expireTime`. - -If you already have TTL enabled on your DynamoDB table using the old attribute, you need to disable TTL for that attribute and then enable it for the new one. You may need to wait an hour between these two operations, as TTL settings changes may take some time to propagate. See the DynamoDB docs [here](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/time-to-live-ttl-before-you-start.html). If you don’t do this, DyanmoDB’s TTL feature will not remove any new and expired entries. There is no risk of data loss. - -```bash -# Disable TTL on old attribute -aws dynamodb update-time-to-live \ - --region \ - --table-name \ - --time-to-live-specification "Enabled=false, AttributeName=commitTime" - -# Enable TTL on new attribute -aws dynamodb update-time-to-live \ - --region \ - --table-name \ - --time-to-live-specification "Enabled=true, AttributeName=expireTime" -``` - -### Delta Lake 2.0 or below to Delta Lake 2.1 or above - -When calling `CONVERT TO DELTA` on a catalog table Delta Lake 2.1 infers the data schema from the catalog. In version 2.0 and below, Delta Lake infers the data schema from the data. This means in Delta 2.1 data columns that are not defined in the original catalog table will not be present in the converted Delta table. This behavior can be disabled by setting the Spark session configuration `spark.databricks.delta.convert.useCatalogSchema=false`. - -### Delta Lake 1.2 or below to Delta Lake 2.0 or above - -Delta Lake 2.0.0 introduced a behavior change for [DROP CONSTRAINT](/latest/delta-constraints#check-constraint). In version 1.2 and below, no error was thrown when trying to drop a non-existent constraint. In version 2.0.0 and above, the behavior is changed to throw a constraint not exists error. To avoid the error, use `IF EXISTS` construct (for example, `ALTER TABLE` events `DROP CONSTRAINT IF EXISTS constraint_name`). There is no change in behavior in dropping an existing constraint. - -Delta Lake 2.0.0 introduced support for [Dynamic Partition Overwrites](/latest/delta-batch#overwrite). In version 1.2 and below, enabling dynamic partition overwrite mode in either the Spark session configuration or a DataFrameWriter option was a no-op, and writes in `overwrite` mode replaced all existing data in every partition of the table. In version 2.0.0 and above, when dynamic partition overwrite mode is enabled, Delta Lake replaces all existing data in each logical partition for which the write will commit new data. - -### Delta Lake 1.1 or below to Delta Lake 1.2 or above - -The [LogStore](https://docs.delta.io/latest/api/java/index.html) related code is -extracted out from the `delta-core` Maven module into a new module -`delta-storage` as part of the issue -[#951](https://github.com/delta-io/delta/issues/951) for better code -manageability. This results in an additional JAR `delta-storage-.jar` -dependency for `delta-core`. By default, the additional JAR is downloaded as -part of the `delta-core-_.jar` dependency. In clusters -where there is _no internet connectivity_, `delta-storage-.jar` cannot -be downloaded. It is advised to download the `delta-storage-.jar` -manually and place it in the Java classpath. - -### Delta Lake 1.0 or below to Delta Lake 1.1 or above - -If the name of a partition column in a Delta table contains invalid characters -(` ,;{}()\n\t=`), you cannot read it in Delta Lake 1.1 and above, due to -[SPARK-36271](https://issues.apache.org/jira/browse/SPARK-36271). However, this -should be rare as you cannot create such tables by using Delta Lake 0.6 and -above. If you still have such legacy tables, you can overwrite your tables with -new valid column names by using Delta Lake 1.0 and below before upgrading to Delta Lake to 1.1 and above, such as the following: - - - -```python -spark.read \ - .format("delta") \ - .load("/the/delta/table/path") \ - .withColumnRenamed("column name", "column-name") \ - .write \ - .format("delta") \ - .mode("overwrite") \ - .option("overwriteSchema", "true") \ - .save("/the/delta/table/path") -``` - -```scala -spark.read - .format("delta") - .load("/the/delta/table/path") - .withColumnRenamed("column name", "column-name") - .write - .format("delta") - .mode("overwrite") - .option("overwriteSchema", "true") - .save("/the/delta/table/path") -``` - - - -### Delta Lake 0.6 or below to Delta Lake 0.7 or above - -If you are using `DeltaTable` APIs in Scala, Java, or Python to -[update](/latest/delta-update) or [run utility operations](/latest/delta-utility) on them, -then you may have to add the following configurations when creating the -`SparkSession` used to perform those operations. - - - -```python -from pyspark.sql import SparkSession - -spark = SparkSession \ - .builder \ - .appName("...") \ - .master("...") \ - .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \ - .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") \ - .getOrCreate() -``` - -```scala -import org.apache.spark.sql.SparkSession - -val spark = SparkSession - .builder() - .appName("...") - .master("...") - .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") - .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") - .getOrCreate() -``` - -```java -import org.apache.spark.sql.SparkSession; - -SparkSession spark = SparkSession - .builder() - .appName("...") - .master("...") - .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") - .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") - .getOrCreate(); -``` - - - -Alternatively, you can add additional configurations when submitting you Spark -application using `spark-submit` or when starting `spark-shell`/`pyspark` by -specifying them as command line parameters. - -```bash -spark-submit --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog" ... -``` - -```bash -pyspark --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog" ... -``` diff --git a/src/pages/latest/quick-start.mdx b/src/pages/latest/quick-start.mdx deleted file mode 100644 index 757adbb..0000000 --- a/src/pages/latest/quick-start.mdx +++ /dev/null @@ -1,481 +0,0 @@ ---- -title: Quickstart -description: Learn how to get started quickly with Delta Lake. -menu: docs ---- -import { version } from "config/version"; - -This guide helps you quickly explore the main features of Delta Lake. It -provides code snippets that show how to read from and write to Delta tables from -interactive, batch, and streaming queries. - -## Set up Apache Spark with Delta Lake - -Follow these instructions to set up Delta Lake with Spark. You can run the -steps in this guide on your local machine in the following two ways: - -1. Run interactively: Start the Spark shell (Scala or Python) with Delta Lake - and run the code snippets interactively in the shell. - -2. Run as a project: Set up a Maven or SBT project (Scala or Java) with Delta Lake, copy the code snippets into a source file, and run the project. - Alternatively, you can use the [examples provided in the Github - repository](https://github.com/delta-io/delta/tree/master/examples). - - - For all of the following instructions, make sure to install the correct - version of Spark or PySpark that is compatible with Delta Lake { version.latest }. - See the [release compatibility matrix](releases-oss.md) for details. - - -### Prerequisite: set up Java - -As mentioned in the official Apache Spark installation instructions [here](https://spark.apache.org/docs/latest/index.html#downloading), make sure you have a valid Java version installed (8, 11, or 17) and that Java is configured correctly on your system using either the system PATH or JAVA_HOME environmental variable. - -Windows users should follow the instructions in this blog, making sure to use the correct version of Apache Spark that is compatible with Delta Lake 2.2.0. - -### Set up interactive shell - -To use Delta Lake interactively within the Spark Scala or Python shell, you -need a local installation of Apache Spark. Depending on whether you want to -use Python or Scala, you can set up either PySpark or the Spark shell, -respectively. - -#### Spark SQL Shell - -Download the [compatible version](/latest/releases) of Apache Spark by following instructions from [Downloading Spark](https://spark.apache.org/downloads.html), either using pip or by downloading and extracting the archive and running spark-sql in the extracted directory. - - - -```bash -bin/spark-sql --packages io.delta:delta-core_2.12:2.2.0 --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog" -``` - - - -#### PySpark Shell - -1. Install the PySpark version that is [compatible](/latest/releases) with the Delta Lake version by running the following: - - - -```bash -pip install pyspark==2.2.0 -``` - - - -2. Run PySpark with the Delta Lake package and additional configurations: - - - -```bash -pyspark --packages io.delta:delta-core_2.12:2.2.0 --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog" -``` - - - -#### Spark Scala Shell - -Download the [compatible version](releases-oss.md) of Apache Spark by -following instructions from [Downloading -Spark](https://spark.apache.org/downloads.html), either using `pip` or by -downloading and extracting the archive and running `spark-shell` in the -extracted directory. - -```bash -bin/spark-shell --packages io.delta:delta-core_2.12:2.2.0 --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog" -``` - -### Set up project - -If you want to build a project using Delta Lake binaries from Maven Central -Repository, you can use the following Maven coordinates. - -#### Maven - -You include Delta Lake in your Maven project by adding it as a dependency in -your POM file. Delta Lake compiled with Scala 2.12. - -```xml - - io.delta - delta-core_2.12 - 2.2.0 - -``` - -#### SBT - -You include Delta Lake in your SBT project by adding the following line to -your `build.sbt` file: - -```scala -libraryDependencies += "io.delta" %% "delta-core" % "2.2.0" -``` - -#### Python - -To set up a Python project (for example, for unit testing), you can install - -Delta Lake using `pip install delta-spark` and then configure the -SparkSession with the `configure_spark_with_delta_pip()` utility function in -Delta Lake. - -```python -import pyspark -from delta import * - -builder = pyspark.sql.SparkSession.builder.appName("MyApp") \ - .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \ - .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") - -spark = configure_spark_with_delta_pip(builder).getOrCreate() -``` - -## Create a table - -To create a Delta table, write a DataFrame out in the `delta` format. You can -use existing Spark SQL code and change the format from `parquet`, `csv`, `json`, -and so on, to `delta`. - - - -```SQL -CREATE TABLE delta.`/tmp/delta-table` USING DELTA AS SELECT col1 as id FROM VALUES 0,1,2,3,4; -``` - -```python -data = spark.range(0, 5) -data.write.format("delta").save("/tmp/delta-table") -``` - -```scala -val data = spark.range(0, 5) -data.write.format("delta").save("/tmp/delta-table") -``` - -```java -import org.apache.spark.sql.SparkSession; -import org.apache.spark.sql.Dataset; -import org.apache.spark.sql.Row; - -SparkSession spark = ... // create SparkSession - -Dataset data = spark.range(0, 5); -data.write().format("delta").save("/tmp/delta-table"); -``` - - - -These operations create a new Delta table using the schema that was _inferred_ -from your DataFrame. For the full set of options available when you create a new -Delta table, see [Create a table](/latest/delta-batch/#create-a-table) and -[Write to a table](/latest/delta-batch/#write-to-a-table). - - - -This quickstart uses local paths for Delta table locations. For -configuring HDFS or cloud storage for Delta tables, see [delta storage](/latest/delta-storage). - - - -## Read data - -You read data in your Delta table by specifying the path to the files: -`"/tmp/delta-table"`: - - - -```SQL -SELECT * FROM delta.`/tmp/delta-table`; -``` - -```python -df = spark.read.format("delta").load("/tmp/delta-table") -df.show() -``` - -```scala -val df = spark.read.format("delta").load("/tmp/delta-table") -df.show() -``` - -```java -Dataset df = spark.read().format("delta").load("/tmp/delta-table"); -df.show(); -``` - - - -## Update table data - -Delta Lake supports several operations to modify tables using standard -DataFrame APIs. This example runs a batch job to overwrite the data in the -table: - -### Overwrite - - - -```SQL -INSERT OVERWRITE delta.`/tmp/delta-table` SELECT col1 as id FROM VALUES 5,6,7,8,9; -``` - -```python -data = spark.range(5, 10) -data.write.format("delta").mode("overwrite").save("/tmp/delta-table") -``` - -```scala -val data = spark.range(5, 10) -data.write.format("delta").mode("overwrite").save("/tmp/delta-table") -df.show() -``` - -```java -Dataset data = spark.range(5, 10); -data.write().format("delta").mode("overwrite").save("/tmp/delta-table"); -``` - - - -If you read this table again, you should see only the values `5-9` you have -added because you overwrote the previous data. - -### Conditional update without overwrite - -Delta Lake provides programmatic APIs to conditional update, delete, and -merge (upsert) data into tables. Here are a few examples. - - - -```SQL --- Update every even value by adding 100 to it -UPDATE delta.`/tmp/delta-table` SET id = id + 100 WHERE id % 2 == 0; - --- Delete very even value -DELETE FROM delta.`/tmp/delta-table` WHERE id % 2 == 0; - --- Upsert (merge) new data -CREATE TEMP VIEW newData AS SELECT col1 AS id FROM VALUES 1,3,5,7,9,11,13,15,17,19; - -MERGE INTO delta.`/tmp/delta-table` AS oldData -USING newData -ON oldData.id = newData.id -WHEN MATCHED - THEN UPDATE SET id = newData.id -WHEN NOT MATCHED - THEN INSERT (id) VALUES (newData.id); - -SELECT * FROM delta.`/tmp/delta-table`; -``` - -```python -from delta.tables import * -from pyspark.sql.functions import * - -deltaTable = DeltaTable.forPath(spark, "/tmp/delta-table") - -# Update every even value by adding 100 to it -deltaTable.update( - condition = expr("id % 2 == 0"), - set = { "id": expr("id + 100") }) - -# Delete every even value -deltaTable.delete(condition = expr("id % 2 == 0")) - -# Upsert (merge) new data -newData = spark.range(0, 20) - -deltaTable.alias("oldData") \ - .merge( - newData.alias("newData"), - "oldData.id = newData.id") \ - .whenMatchedUpdate(set = { "id": col("newData.id") }) \ - .whenNotMatchedInsert(values = { "id": col("newData.id") }) \ - .execute() - -deltaTable.toDF().show() -``` - -```scala -import io.delta.tables._ -import org.apache.spark.sql.functions._ - -val deltaTable = DeltaTable.forPath("/tmp/delta-table") - -// Update every even value by adding 100 to it -deltaTable.update( - condition = expr("id % 2 == 0"), - set = Map("id" -> expr("id + 100"))) - -// Delete every even value -deltaTable.delete(condition = expr("id % 2 == 0")) - -// Upsert (merge) new data -val newData = spark.range(0, 20).toDF - -deltaTable.as("oldData") - .merge( - newData.as("newData"), - "oldData.id = newData.id") - .whenMatched - .update(Map("id" -> col("newData.id"))) - .whenNotMatched - .insert(Map("id" -> col("newData.id"))) - .execute() - -deltaTable.toDF.show() -``` - -```java -import io.delta.tables.*; -import org.apache.spark.sql.functions; -import java.util.HashMap; - -DeltaTable deltaTable = DeltaTable.forPath("/tmp/delta-table"); - -// Update every even value by adding 100 to it -deltaTable.update( - functions.expr("id % 2 == 0"), - new HashMap() {{ - put("id", functions.expr("id + 100")); - }} -); - -// Delete every even value -deltaTable.delete(condition = functions.expr("id % 2 == 0")); - -// Upsert (merge) new data -Dataset newData = spark.range(0, 20).toDF(); - -deltaTable.as("oldData") - .merge( - newData.as("newData"), - "oldData.id = newData.id") - .whenMatched() - .update( - new HashMap() {{ - put("id", functions.col("newData.id")); - }}) - .whenNotMatched() - .insertExpr( - new HashMap() {{ - put("id", functions.col("newData.id")); - }}) - .execute(); - -deltaTable.toDF().show(); -``` - - - -You should see that some of the existing rows have been updated and new rows -have been inserted. - -For more information on these operations, see [Table delets, updates, and merges](/latestl/delta-update). - -## Read older versions of data using time travel - -You can query previous snapshots of your Delta table by using time travel. If -you want to access the data that you overwrote, you can query a snapshot of the -table before you overwrote the first set of data using the `versionAsOf` option. - - - -```SQL -SELECT * FROM delta.`/tmp/delta-table` VERSION AS OF 0; -``` - -```python -df = spark.read.format("delta").option("versionAsOf", 0).load("/tmp/delta-table") -df.show() -``` - -```scala -val df = spark.read.format("delta").option("versionAsOf", 0).load("/tmp/delta-table") -df.show() -``` - -```java -Dataset df = spark.read().format("delta").option("versionAsOf", 0).load("/tmp/delta-table"); -df.show(); -``` - - - -You should see the first set of data, from before you overwrote it. Time travel -takes advantage of the power of the Delta Lake transaction log to access data -that is no longer in the table. Removing the version 0 option (or specifying -version 1) would let you see the newer data again. For more information, see -[Query an older snapshot of a table (time travel)](/latest/delta-batch/#deltatimetravel). - -## Write a stream of data to a table - -You can also write to a Delta table using Structured Streaming. The Delta Lake -transaction log guarantees exactly-once processing, even when there are other -streams or batch queries running concurrently against the table. By default, -streams run in append mode, which adds new records to the table: - - -```python -streamingDf = spark.readStream.format("rate").load() -stream = streamingDf.selectExpr("value as id").writeStream.format("delta").option("checkpointLocation", "/tmp/checkpoint").start("/tmp/delta-table") -``` - -```scala -val streamingDf = spark.readStream.format("rate").load() -val stream = streamingDf.select($"value" as "id").writeStream.format("delta").option("checkpointLocation", "/tmp/checkpoint").start("/tmp/delta-table") -``` - -```java -import org.apache.spark.sql.streaming.StreamingQuery; - -Dataset streamingDf = spark.readStream().format("rate").load(); -StreamingQuery stream = streamingDf.selectExpr("value as id").writeStream().format("delta").option("checkpointLocation", "/tmp/checkpoint").start("/tmp/delta-table"); -``` - - - -While the stream is running, you can read the table using the earlier commands. - - - -If you're running this in a shell, you may see the streaming task -progress, which make it hard to type commands in that shell. It may be useful to -start another shell in a new terminal for querying the table. - - - -You can stop the stream by running `stream.stop()` in the same terminal that -started the stream. - -For more information about Delta Lake integration with Structured Streaming, -see [Table streaming reads and writes](/latest/delta-streaming). See also the [Structured Streaming Programming -Guide](https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html) -on the Apache Spark website. - -## Read a stream of changes from a table - -While the stream is writing to the Delta table, you can also read from that -table as streaming source. For example, you can start another streaming query -that prints all the changes made to the Delta table. You can specify which -version Structured Streaming should start from by providing the -`startingVersion` or `startingTimestamp` option to get changes from that point -onwards. See [Structured Streaming](/latest/delta-streaming/#specify-initial-position) -for details. - - -```python -stream2 = spark.readStream.format("delta").load("/tmp/delta-table").writeStream.format("console").start() -``` - -```scala -val stream2 = spark.readStream.format("delta").load("/tmp/delta-table").writeStream.format("console").start() -``` - -```java -StreamingQuery stream2 = spark.readStream().format("delta").load("/tmp/delta-table").writeStream().format("console").start(); -``` - - diff --git a/src/pages/latest/releases.mdx b/src/pages/latest/releases.mdx deleted file mode 100644 index d4a9cb9..0000000 --- a/src/pages/latest/releases.mdx +++ /dev/null @@ -1,24 +0,0 @@ ---- -title: Releases -description: Learn about Delta Lake releases. ---- - -This page has release information. - -## Release notes - -The [GitHub releases page](https://github.com/delta-io/delta/releases/) describes features of each release. - -## Compatibility with Apache Spark - -The following table lists Delta Lake versions and their compatible Apache Spark versions. - -| Delta Lake version | Apache Spark version | -| ------------------ | -------------------- | -| 2.1.x | 3.3.x | -| 2.0.x | 3.2.x | -| 1.2.x | 3.2.x | -| 1.1.x | 3.2.x | -| 1.0.x | 3.1.x | -| 0.7.x and 0.8.x | 3.0.x | -| Below 0.7.0 | 2.4.2 - 2.4._latest_ | diff --git a/src/pages/latest/table-properties.mdx b/src/pages/latest/table-properties.mdx deleted file mode 100644 index 336c8d4..0000000 --- a/src/pages/latest/table-properties.mdx +++ /dev/null @@ -1,20 +0,0 @@ ---- -title: Delta table properties reference -description: Reference for the different Delta table properties ---- - -Delta Lake reserves Delta table properties starting with `delta`. These properties may have specific meanings, and affect behaviors when these properties are set. Available Delta table properties include: - -| Property | Description | Data Type | Default | -|-|-|-|-| -| `delta.appendOnly` | `true` for this Delta table to be append-only. If append-only, existing records cannot be deleted, and existing values cannot be updated. See [Table properties](/latest/delta-batch.html#-table-properties). | `Boolean` | `false`| -|`delta.checkpoint.writeStatsAsJson` | `true` for Delta Lake to write file statistics in checkpoints in JSON format for the stats column. | `Boolean` | `true`| -|`delta.checkpoint.writeStatsAsStruct` | `true` for Delta Lake to write file statistics to checkpoints in struct format for the stats_parsed column and to write partition values as a struct for partitionValues_parsed. | `Boolean`| (none)| -|`delta.compatibility.symlinkFormatManifest.enabled`|`true` for Delta Lake to configure the Delta table so that all write operations on the table automatically update the manifests. See Step 3: [Update manifests](https://docs.delta.io/latest/presto-integration.html#-step-3-update-manifests).|`Boolean`|`false`| -|`delta.dataSkippingNumIndexedCols`|The number of columns for Delta Lake to collect statistics about for data skipping. A value of -1 means to collect statistics for all columns. Updating this property does not automatically collect statistics again; instead, it redefines the statistics schema of the Delta table. For example, it changes the behavior of future statistics collection (such as during appends and optimizations) as well as data skipping (such as ignoring column statistics beyond this number, even when such statistics exist).|`Int`|32| -|`delta.deletedFileRetentionDuration`|The shortest duration for Delta Lake to keep logically deleted data files before deleting them physically. This is to prevent failures in stale readers after compactions or partition overwrites. This value should be large enough to ensure that: 1. It is larger than the longest possible duration of a job if you run VACUUM when there are concurrent readers or writers accessing the Delta table. 2. If you run a streaming query that reads from the table, that the query does not stop for longer than this value. Otherwise, the query may not be able to restart, as it must still read old files. See [Data retention](/latest/delta-batch.html#-data-retention).|`CalendarInterval`|`interval 1 week`| -|`delta.enableChangeDataFeed`|`true` to enable change data feed. See [Enable change data feed](/latest/delta-change-data-feed.html#-enable-change-data-feed).|`Boolean`|`false`| -|`delta.logRetentionDuration`|How long the history for a Delta table is kept. Each time a checkpoint is written, Delta Lake automatically cleans up log entries older than the retention interval. If you set this property to a large enough value, many log entries are retained. This should not impact performance as operations against the log are constant time. Operations on history are parallel but will become more expensive as the log size increases. See [Data retention](/latest/delta-batch.html#-data-retention).|`CalendarInterval`|`interval 30 days`| -|`delta.minReaderVersion`|The minimum required protocol reader version for a reader that allows to read from this Delta table. See [Table protocol versioning](/latest/versioning.html).|`Int`|`1`| -|`delta.minWriterVersion`|The minimum required protocol writer version for a writer that allows to write to this Delta table. See [Table protocol versioning](/latest/versioning.html).|`Int`|`2`| -|`delta.setTransactionRetentionDuration`|The shortest duration within which new snapshots will retain transaction identifiers (for example, SetTransactions). When a new snapshot sees a transaction identifier older than or equal to the duration specified by this property, the snapshot considers it expired and ignores it. The SetTransaction identifier is used when making the writes idempotent. See [Idempotent table writes in foreachBatch](https://docs.delta.io/latest/delta-streaming.html#-idempotent-table-writes-in-foreachbatch) for details.|`CalendarInterval`|(none)| \ No newline at end of file diff --git a/src/pages/latest/versioning.mdx b/src/pages/latest/versioning.mdx deleted file mode 100644 index e6167b0..0000000 --- a/src/pages/latest/versioning.mdx +++ /dev/null @@ -1,98 +0,0 @@ ---- -title: Table Protocol Versioning -description: Learn how Delta table protocols are versioned. -orphan: 1 -menu: docs ---- - - - -The transaction log for a Delta table contains protocol versioning information -that supports Delta Lake evolution. Delta Lake tracks minimum [reader and writer -versions](/latest/delta-utility/#detail-schema) separately. - -Delta Lake guarantees _backward compatibility_. A higher protocol version of -the -Delta Lake reader is always able to read data that was written by a lower protocol -version. - -Delta Lake will occasionally break _forward compatibility_. Lower protocol -versions of Delta Lake may not be able to read and write data that was -written by a higher protocol version of Delta Lake. If you try to read and -write to a table with a protocol version of Delta Lake that is too low, -you'll get an error telling you that you need to upgrade. - -When creating a table, Delta Lake chooses the minimum required protocol version -based on table characteristics such as the schema or table properties. You can -also set the default protocol versions by setting the SQL configurations: - -- `spark.databricks.delta.properties.defaults.minWriterVersion = 2` (default) -- `spark.databricks.delta.properties.defaults.minReaderVersion = 1` (default) - -To upgrade a table to a newer protocol version, use the -`DeltaTable.upgradeTableProtocol` method: - - -Protocol version upgrades are irreversible, and upgrading the protocol version may break the existing Delta Lake table readers, writers, or both. Therefore, we recommend you upgrade specific tables only when needed, such as to opt-in to new features in Delta Lake. You should also check to make sure that all of your current and future production tools support Delta Lake tables with the new protocol version. - - - -```sql --- Upgrades the reader protocol version to 1 and the writer protocol version to 3. -ALTER TABLE SET TBLPROPERTIES('delta.minReaderVersion' = '1', 'delta.minWriterVersion' = '3') -``` - -```python -from delta.tables import DeltaTable -delta = DeltaTable.forPath(spark, "path_to_table") # or DeltaTable.forName -delta.upgradeTableProtocol(1, 3) # upgrades to readerVersion=1, writerVersion=3 -``` - -```scala -import io.delta.tables.DeltaTable -val delta = DeltaTable.forPath(spark, "path_to_table") // or DeltaTable.forName -delta.upgradeTableProtocol(1, 3) // Upgrades to readerVersion=1, writerVersion=3 -``` - - - -## Features by protocol version - -| Feature | `minWriterVersion` | `minReaderVersion` | Introduced in | Documentation | -| ------------------- | ------------------ | ------------------ | ------------------------------------------------------------------------- | -------------------------------------------- | -| Basic functionality | 2 | 1 | -- | [Delta Lake guide](/latest) | -| `CHECK` constraints | 3 | 1 | [Delta Lake 0.8.0](https://github.com/delta-io/delta/releases/tag/v0.8.0) | [CHECK constraint](/latest/delta-constraints/#check-constraint) | -| Generated columns | 4 | 1 | [Delta Lake 1.0.0](https://github.com/delta-io/delta/releases/tag/v1.0.0) | [Use generated columns](/latest/delta-batch/#use-generated-columns) | -| Column mapping | 5 | 2 | [Delta Lake 1.2.0](https://github.com/delta-io/delta/releases/tag/v1.2.0) | [Column mapping](#column-mapping) | -| Change data feed | 4 | 1 | [Delta Lake 2.0.0](https://github.com/delta-io/delta/releases/tag/v2.0.0) | [Change data feed](/latest/delta-change-data-feed) - - -See [Requirements for Readers](https://github.com/delta-io/delta/blob/master/PROTOCOL.md#requirements-for-readers) and [Writer Version Requirements](https://github.com/delta-io/delta/blob/master/PROTOCOL.md#writer-version-requirements) in the [delta-io/delta](https://github.com/delta-io/delta) repo on the GitHub website. - -### Column mapping - -[Column mapping feature](https://github.com/delta-io/delta/blob/master/PROTOCOL.md#column-mapping) allows Delta table columns and the underlying Parquet file columns to use different names. This enables Delta schema evolution operations such as [RENAME COLUMN](/latest/delta-batch/#rename-columns) on a Delta table without the need to rewrite the underlying Parquet files. It also allows users to name Delta table columns by using [characters that are not allowed](/latest/delta-batch/#use-special-characters-in-column-names) by Parquet, such as spaces, so that users can directly ingest CSV or JSON data into Delta without the need to rename columns due to previous character constraints. - -Column mapping requires upgrading the Delta Lake table protocol. - - -Protocol version upgrades are irreversible, and upgrading the protocol version may break the existing Delta Lake table readers, writers, or both. Therefore, we recommend you upgrade specific tables only when needed, such as to opt-in to new features in Delta Lake. You should also check to make sure that all of your current and future production tools support Delta Lake tables with the new protocol version. - - - - - -```sql -ALTER TABLE SET TBLPROPERTIES ( - 'delta.minReaderVersion' = '2', - 'delta.minWriterVersion' = '5', - 'delta.columnMapping.mode' = 'name' -) -``` - - - -## Known limitations -- In Delta Lake 2.0.0, [Spark Structured Streaming](https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html) and [Change data feed](/latest/delta-change-data-feed) reads are explicitly blocked on a column mapping enabled table. - -- The Delta table protocol specifies two modes of column mapping, by name and by id. 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-This folder contains instructions and materials to get new users started with Delta Lake and work through the quickstart materials using a self-contained Docker image. - -> Note: The basic prerequisite for following along using Delta Lake Docker image is having Docker installed on your machine. Please follow the steps from the Docker website to install Docker locally. Based on your local machine operating system, please choose the appropriate option listed on the [Get Docker](https://docs.docker.com/get-docker/) page. - -Follow the steps below to build an Apache SparkTM image with Delta Lake installed, run a container, and follow the quickstart in an interactive notebook or shell with any of the options like Python, PySpark, Scala Spark or even Rust. - -1. [Working with Docker](#working-with-docker) - 1. [Build the image](#build-the-image) - 2. [Docker Hub](#docker-hub) -2. [Choose an interface](#choose-an-interface) - -> Note: Python version available in this Docker image is 3.9.2 and is available as `python3`. - -## Working with Docker - -### Build the Image - -1. Clone this repo -2. Navigate to the cloned folder -3. Navigate to the `quickstart_docker` folder -4. Open a bash shell (if on windows use git bash, WSL, or any shell configured for bash commands) -5. Execute the following from the `static/quickstart_docker` folder - - ```bash - docker build -t delta_quickstart -f Dockerfile_delta_quickstart . - ``` - -#### Build Entry Point - -Your entry point for this locally built docker file is - -```bash -docker run --name delta_quickstart --rm -it --entrypoint bash delta_quickstart -``` - -### Docker Hub - -You can also download the image from DockerHub at [Delta Lake DockerHub](https://go.delta.io/dockerhub) - -Note, there are different versions of the Delta Lake docker - -| Tag | Platform | Python | Rust | Delta-Spark | Spark | JupyterLab | Pandas | ROAPI | -| ----------------- | -------- | ------ | ------ | ----------- | ----- | ---------- | ------ | ----- | -| 0.8.1_2.3.0 | amd64 | 0.8.1 | latest | 2.3.0 | 3.3.2 | 3.6.3 | 1.5.3 | 0.9.0 | -| 0.8.1_2.3.0_arm64 | arm64 | 0.8.1 | latest | 2.3.0 | 3.3.2 | 3.6.3 | 1.5.3 | 0.9.0 | -| 1.0.0_3.0.0 | amd64 | 0.12.0 | latest | 3.0.0 | 3.5.0 | 3.6.3 | 1.5.3 | 0.9.0 | -| 1.0.0_3.0.0_arm64 | arm64 | 0.12.0 | latest | 3.0.0 | 3.5.0 | 3.6.3 | 1.5.3 | 0.9.0 | -| latest | amd64 | 0.12.0 | latest | 3.0.0 | 3.5.0 | 3.6.3 | 1.5.3 | 0.9.0 | -| latest | arm64 | 0.12.0 | latest | 3.0.0 | 3.5.0 | 3.6.3 | 1.5.3 | 0.9.0 | - -> Note, the arm64 version is built for ARM64 platforms like Mac M1 - -Download the appropriate tag, e.g.: - -- `docker pull deltaio/delta-docker:latest` for the standard Linux docker -- `docker pull deltaio/delta-docker:latest_arm64` for running this optimally on your Mac M1 - -#### Image Entry Point - -Your entry point for the Docker Hub image is: - -```bash -# Running locally on Mac M1 -docker run --name delta_quickstart --rm -it --entrypoint bash deltaio/delta-docker:latest_arm64 -``` - -```bash -# Running on Linux VM -docker run --name delta_quickstart --rm -it --entrypoint bash deltaio/delta-docker:latest -``` - -Once the image has been built or you have downloaded the correct image, you can then move on to running the quickstart in a notebook or shell. - -## Choose the Delta Package version - -In the following instructions, the variable `${DELTA_PACKAGE_VERSION}` refers to the Delta Lake Package version. - -The current version is `delta-spark_2.12:3.0.0` which corresponds to Apache Spark 3.5.x release line. - -## Choose an Interface - -- [delta-rs Python](#delta-rs-python) -- [JupyterLab Notebook](#jupyterlab-notebook) -- [PySpark Shell](#pyspark-shell) -- [Scala Shell](#scala-shell) -- [Delta Rust API](#delta-rust-api) -- [ROAPI](#optional-roapi) - -### delta-rs Python - -1. Open a bash shell (if on windows use git bash, WSL, or any shell configured for bash commands) - -1. Run a container from the image with a bash entrypoint ([build](#build-entry-point) | [DockerHub](#image-entry-point)) - -1. Launch a _python_ interactive shell session with `python3` - - ```bash - python3 - ``` - - > Note: The Delta Rust Python bindings are already installed in this docker. To do this manually in your own environment, run the command: `pip3 install deltalake==0.12.0` - -1. Run some basic commands in the shell to write to and read from Delta Lake with Pandas - - ```python - import pandas as pd - from deltalake.writer import write_deltalake - from deltalake import DeltaTable - - # Create a Pandas DataFrame - df = pd.DataFrame({"data": range(5)}) - - # Write to the Delta Lake table - write_deltalake("/tmp/deltars_table", df) - - # Append new data - df = pd.DataFrame({"data": range(6, 11)}) - write_deltalake("/tmp/deltars_table", df, mode="append") - - # Read the Delta Lake table - dt = DeltaTable("/tmp/deltars_table") - - # Show the Delta Lake table - dt.to_pandas() - ``` - - ```python - ## Output - data - 0 0 - 1 1 - 2 2 - ... - 8 9 - 9 10 - ``` - -1. Review the files - - ```python - # List files for the Delta Lake table - dt.files() - ``` - - ```python - ## Output - ['0-6944fddf-60e3-4eab-811d-1398e9f64073-0.parquet', '1-66c7ee6e-6aab-4c74-866d-a82790102652-0.parquet'] - ``` - -1. Review history - - ```python - # Review history - dt.history() - ``` - - ```python - ## Output - [{'timestamp': 1698002214493, 'operation': 'WRITE', 'operationParameters': {'mode': 'Append', 'partitionBy': '[]'}, 'clientVersion': 'delta-rs.0.17.0', 'version': 1}, {'timestamp': 1698002207527, 'operation': 'CREATE TABLE', 'operationParameters': {'mode': 'ErrorIfExists', 'protocol': '{"minReaderVersion":1,"minWriterVersion":1}', 'location': 'file:///tmp/deltars_table', 'metadata': '{"configuration":{},"created_time":1698002207525,"description":null,"format":{"options":{},"provider":"parquet"},"id":"bf749aab-22b6-484b-bd73-dc1680ee4384","name":null,"partition_columns":[],"schema":{"fields":[{"metadata":{},"name":"data","nullable":true,"type":"long"}],"type":"struct"}}'}, 'clientVersion': 'delta-rs.0.17.0', 'version': 0}] - ``` - -1. Time Travel (load older version of table) - - ```python - # Load initial version of table - dt.load_version(0) - - # Show table - dt.to_pandas() - ``` - - ```python - ## Output - data - 0 0 - 1 1 - 2 2 - 3 3 - 4 4 - ``` - -1. Follow the delta-rs Python documentation [here](https://delta-io.github.io/delta-rs/python/usage.html#) - -1. To verify that you have a Delta Lake table, you can list the contents within the folder of your Delta Lake table. For example, in the previous code, you saved the table in `/tmp/deltars-table`. Once you close your `python3` process, run a list command in your Docker shell and you should get something similar to below. - - ```bash - $ ls -lsgA /tmp/deltars_table - ``` - - ```bash - total 12 - 4 -rw-r--r-- 1 NBuser 1689 Oct 22 19:16 0-6944fddf-60e3-4eab-811d-1398e9f64073-0.parquet - 4 -rw-r--r-- 1 NBuser 1691 Oct 22 19:16 1-66c7ee6e-6aab-4c74-866d-a82790102652-0.parquet - 4 drwxr-xr-x 2 NBuser 4096 Oct 22 19:16 _delta_log - ``` - -1. [Optional] Skip ahead to try out the [Delta Rust API](#delta-rust-api) and [ROAPI](#optional-roapi) - -### JupyterLab Notebook - -1. Open a bash shell (if on windows use git bash, WSL, or any shell configured for bash commands) - -2. Run a container from the image with a JuypterLab entrypoint - - ```bash - # Build entry point - docker run --name delta_quickstart --rm -it -p 8888-8889:8888-8889 delta_quickstart - ``` - - ```bash - # Image entry point (M1) - docker run --name delta_quickstart --rm -it -p 8888-8889:8888-8889 -entrypoint bash deltaio/delta-docker:latest_arm64 - ``` - -3. Running the above command gives a JupyterLab notebook URL, copy that URL and launch a browser to follow along the notebook and run each cell. - - > **Note that you may also launch the pyspark or scala shells after launching a terminal in JupyterLab** - -### PySpark Shell - -1. Open a bash shell (if on windows use git bash, WSL, or any shell configured for bash commands) - -2. Run a container from the image with a bash entrypoint ([build](#build-entry-point) | [DockerHub](#image-entry-point)) - -3. Launch a pyspark interactive shell session - - ```bash - - $SPARK_HOME/bin/pyspark --packages io.delta:${DELTA_PACKAGE_VERSION} \ - --conf spark.driver.extraJavaOptions="-Divy.cache.dir=/tmp -Divy.home=/tmp" \ - --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" \ - --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog" - ``` - - > Note: `DELTA_PACKAGE_VERSION` is set in `./startup.sh` - -4. Run some basic commands in the shell - - ```python - # Create a Spark DataFrame - data = spark.range(0, 5) - - # Write to a Delta Lake table - (data - .write - .format("delta") - .save("/tmp/delta-table") - ) - - # Read from the Delta Lake table - df = (spark - .read - .format("delta") - .load("/tmp/delta-table") - .orderBy("id") - ) - - # Show the Delta Lake table - df.show() - ``` - - ```python - ## Output - +---+ - | id| - +---+ - | 0| - | 1| - | 2| - | 3| - | 4| - +---+ - ``` - -5. Continue with the quickstart [here](https://docs.delta.io/latest/quick-start.html#create-a-table&language-python) - -6. To verify that you have a Delta Lake table, you can list the contents within the folder of your Delta Lake table. For example, in the previous code, you saved the table in `/tmp/delta-table`. Once you close your `pyspark` process, run a list command in your Docker shell and you should get something similar to below. - - ```bash - $ ls -lsgA /tmp/delta-table - ``` - - ```bash - total 52 - 4 drwxr-xr-x 2 NBuser 4096 Oct 22 19:23 _delta_log - 4 -rw-r--r-- 1 NBuser 296 Oct 22 19:23 part-00000-dc0fd6b3-9c0f-442f-a6db-708301b27bd2-c000.snappy.parquet - 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:23 .part-00000-dc0fd6b3-9c0f-442f-a6db-708301b27bd2-c000.snappy.parquet.crc - 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:23 part-00001-d379441e-1ee4-4e78-8616-1d9635df1c7b-c000.snappy.parquet - 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:23 .part-00001-d379441e-1ee4-4e78-8616-1d9635df1c7b-c000.snappy.parquet.crc - 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:23 part-00003-c08dcac4-5ea9-4329-b85d-9110493e8757-c000.snappy.parquet - 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:23 .part-00003-c08dcac4-5ea9-4329-b85d-9110493e8757-c000.snappy.parquet.crc - 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:23 part-00005-5db8dd16-2ab1-4d76-9b4d-457c5641b1c8-c000.snappy.parquet - 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:23 .part-00005-5db8dd16-2ab1-4d76-9b4d-457c5641b1c8-c000.snappy.parquet.crc - 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:23 part-00007-cad760e0-3c26-4d22-bed6-7d75a9459a0f-c000.snappy.parquet - 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:23 .part-00007-cad760e0-3c26-4d22-bed6-7d75a9459a0f-c000.snappy.parquet.crc - 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:23 part-00009-b58e8445-07b7-4e2a-9abf-6fea8d0c3e3f-c000.snappy.parquet - 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:23 .part-00009-b58e8445-07b7-4e2a-9abf-6fea8d0c3e3f-c000.snappy.parquet.crc - ``` - -### Scala Shell - -1. Open a bash shell (if on windows use git bash, WSL, or any shell configured for bash commands) - -2. Run a container from the image with a bash entrypoint ([build](#build-entry-point) | [DockerHub](#image-entry-point)) - -3. Launch a scala interactive shell session - - ```bash - $SPARK_HOME/bin/spark-shell --packages io.delta:${DELTA_PACKAGE_VERSION} \ - --conf spark.driver.extraJavaOptions="-Divy.cache.dir=/tmp -Divy.home=/tmp" \ - --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" \ - --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog" - ``` - -4. Run some basic commands in the shell - - > note: if you've already written to the Delta table in the python shell example, use `.mode("overwrite")` to overwrite the current delta table. You can always time-travel to rewind. - - ```scala - // Create a Spark DataFrame - val data = spark.range(0, 5) - - // Write to a Delta Lake table - - (data - .write - .format("delta") - .save("/tmp/delta-table") - ) - - // Read from the Delta Lake table - val df = (spark - .read - .format("delta") - .load("/tmp/delta-table") - .orderBy("id") - ) - - // Show the Delta Lake table - df.show() - ``` - - ```scala - ## Output - +---+ - | id| - +---+ - | 0| - | 1| - | 2| - | 3| - | 4| - +---+ - ``` - -5. Follow the quickstart [here](https://docs.delta.io/latest/quick-start.html#create-a-table&language-scala) - -6. To verify that you have a Delta Lake table, you can list the contents within the folder of your Delta Lake table. For example, in the previous code, you saved the table in `/tmp/delta-table`. Once you close your Scala Spark process [`spark-shell`], run a list command in your Docker shell and you should get something similar to below. - - ```bash - $ ls -lsgA /tmp/delta-table - ``` - - ```bash - total 52 - 4 drwxr-xr-x 2 NBuser 4096 Oct 22 19:28 _delta_log - 4 -rw-r--r-- 1 NBuser 296 Oct 22 19:28 part-00000-f1f417f7-df64-4c7c-96f2-6a452ae2b49e-c000.snappy.parquet - 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:28 .part-00000-f1f417f7-df64-4c7c-96f2-6a452ae2b49e-c000.snappy.parquet.crc - 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:28 part-00001-b28acb6f-f08a-460f-a24e-4d9c1affee86-c000.snappy.parquet - 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:28 .part-00001-b28acb6f-f08a-460f-a24e-4d9c1affee86-c000.snappy.parquet.crc - 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:28 part-00003-29079c58-d1ad-4604-9c04-0f00bf09546d-c000.snappy.parquet - 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:28 .part-00003-29079c58-d1ad-4604-9c04-0f00bf09546d-c000.snappy.parquet.crc - 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:28 part-00005-04424aa7-48e1-4212-bd57-52552c713154-c000.snappy.parquet - 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:28 .part-00005-04424aa7-48e1-4212-bd57-52552c713154-c000.snappy.parquet.crc - 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:28 part-00007-e7a54a4f-bee4-4371-a35d-d284e28eb9f8-c000.snappy.parquet - 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:28 .part-00007-e7a54a4f-bee4-4371-a35d-d284e28eb9f8-c000.snappy.parquet.crc - 4 -rw-r--r-- 1 NBuser 478 Oct 22 19:28 part-00009-086e6cd9-e8c6-4f16-9658-b15baf22905d-c000.snappy.parquet - 4 -rw-r--r-- 1 NBuser 12 Oct 22 19:28 .part-00009-086e6cd9-e8c6-4f16-9658-b15baf22905d-c000.snappy.parquet.crc - ``` - - - -### Delta Rust API - -> Note: Use a docker volume in case of running into limits "no room left on device" -> `docker volume create rustbuild` > `docker run --name delta_quickstart -v rustbuild:/tmp --rm -it --entrypoint bash deltaio/delta-docker:3.0.0` - -1. Open a bash shell (if on windows use git bash, WSL, or any shell configured for bash commands) - -2. Run a container from the image with a bash entrypoint ([build](#build-entry-point) | [DockerHub](#image-entry-point)) - -3. Execute `examples/read_delta_table.rs` to review the Delta Lake table metadata and files of the `covid19_nyt` Delta Lake table. - - ```bash - cd rs - cargo run --example read_delta_table - ``` - - > You can also use a different location to build and run the examples - - ```bash - cd rs - CARGO_TARGET_DIR=/tmp cargo run --example read_delta_table - ``` - - > If using [Delta Lake DockerHub](https://go.delta.io/dockerhub), sometimes the Rust environment hasn't been configured. To resolve this, run the command `source "$HOME/.cargo/env"` - - ```bash - === Delta table metadata === - DeltaTable(/opt/spark/work-dir/rs/data/COVID-19_NYT) - version: 0 - metadata: GUID=7245fd1d-8a6d-4988-af72-92a95b646511, name=None, description=None, partitionColumns=[], createdTime=Some(1619121484605), configuration={} - min_version: read=1, write=2 - files count: 8 - - - === Delta table files === - [Path { raw: "part-00000-a496f40c-e091-413a-85f9-b1b69d4b3b4e-c000.snappy.parquet" }, Path { raw: "part-00001-9d9d980b-c500-4f0b-bb96-771a515fbccc-c000.snappy.parquet" }, Path { raw: "part-00002-8826af84-73bd-49a6-a4b9-e39ffed9c15a-c000.snappy.parquet" }, Path { raw: "part-00003-539aff30-2349-4b0d-9726-c18630c6ad90-c000.snappy.parquet" }, Path { raw: "part-00004-1bb9c3e3-c5b0-4d60-8420-23261f58a5eb-c000.snappy.parquet" }, Path { raw: "part-00005-4d47f8ff-94db-4d32-806c-781a1cf123d2-c000.snappy.parquet" }, Path { raw: "part-00006-d0ec7722-b30c-4e1c-92cd-b4fe8d3bb954-c000.snappy.parquet" }, Path { raw: "part-00007-4582392f-9fc2-41b0-ba97-a74b3afc8239-c000.snappy.parquet" }] - ``` - -4. Execute `examples/read_delta_datafusion.rs` to query the `covid19_nyt` Delta Lake table using `datafusion` - - ```bash - cargo run --example read_delta_datafusion - ``` - - ```bash - === Datafusion query === - [RecordBatch { schema: Schema { fields: [Field { name: "cases", data_type: Int32, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: None }, Field { name: "county", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: None }, Field { name: "date", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: None }], metadata: {} }, columns: [PrimitiveArray - [ - 1, - 1, - 1, - 1, - 1, - ], StringArray - [ - "Snohomish", - "Snohomish", - "Snohomish", - "Cook", - "Snohomish", - ], StringArray - [ - "2020-01-21", - "2020-01-22", - "2020-01-23", - "2020-01-24", - "2020-01-24", - ]], row_count: 5 }] - ``` - -

- - -### [Optional] ROAPI - -You can query your Delta Lake table with [Apache Arrow](https://github.com/apache/arrow) and [Datafusion](https://github.com/apache/arrow-datafusion) using [ROAPI](https://roapi.github.io/docs/config/dataset-formats/delta.html) which are pre-installed in this docker. - -> Note: If you need to do this in your own environment, run the command `pip3 install roapi==0.9.0` - -1. Open a bash shell (if on windows use git bash, WSL, or any shell configured for bash commands) - -2. Run a container from the image with a bash entrypoint ([build](#build-entry-point) | [DockerHub](#image-entry-point)) - -3. Start the `roapi` API using the following command. Notes: - - - the API calls are pushed to the `nohup.out` file. - - if you haven't created the `deltars_table` in your container create it via the [delta-rs Python](#delta-rs-python) option above. Alternatively you may omit the following from the command: - `--table 'deltars_table=/tmp/deltars_table/,format=delta'` as well as any steps that call the `deltars_table` - - ```bash - nohup roapi --addr-http 0.0.0.0:8080 --table 'deltars_table=/tmp/deltars_table/,format=delta' --table 'covid19_nyt=/opt/spark/work-dir/rs/data/COVID-19_NYT,format=delta' & - ``` - -4. Check the schema of the two Delta Lake tables - - ```bash - curl localhost:8080/api/schema - ``` - - ```bash - { - "covid19_nyt":{"fields":[ - {"name":"date","data_type":"Utf8","nullable":true,"dict_id":0,"dict_is_ordered":false}, - {"name":"county","data_type":"Utf8","nullable":true,"dict_id":0,"dict_is_ordered":false}, - {"name":"state","data_type":"Utf8","nullable":true,"dict_id":0,"dict_is_ordered":false}, - {"name":"fips","data_type":"Int32","nullable":true,"dict_id":0,"dict_is_ordered":false}, - {"name":"cases","data_type":"Int32","nullable":true,"dict_id":0,"dict_is_ordered":false}, - {"name":"deaths","data_type":"Int32","nullable":true,"dict_id":0,"dict_is_ordered":false} - ]}, - "deltars_table":{"fields":[ - {"name":"0","data_type":"Int64","nullable":true,"dict_id":0,"dict_is_ordered":false} - ]} - } - ``` - -5. Query the `deltars_table` - - ```bash - curl -X POST -d "SELECT * FROM deltars_table" localhost:8080/api/sql - ``` - - ```bash - # output - [{"0":0},{"0":1},{"0":2},{"0":3},{"0":4},{"0":6},{"0":7},{"0":8},{"0":9},{"0":10}] - ``` - -6. Query the `covid19_nyt` table - - ```bash - curl -X POST -d "SELECT cases, county, date FROM covid19_nyt ORDER BY cases DESC LIMIT 5" localhost:8080/api/sql - ``` - - ```bash - [ - {"cases":1208672,"county":"Los Angeles","date":"2021-03-11"}, - {"cases":1207361,"county":"Los Angeles","date":"2021-03-10"}, - {"cases":1205924,"county":"Los Angeles","date":"2021-03-09"}, - {"cases":1204665,"county":"Los Angeles","date":"2021-03-08"}, - {"cases":1203799,"county":"Los Angeles","date":"2021-03-07"} - ] - ```