Expose register_listing_table

datafusion/tests/test_sql.py

@@ -21,8 +21,9 @@
 import pyarrow as pa
 import pyarrow.dataset as ds
 import pytest
+from datafusion.object_store import LocalFileSystem
 
-from datafusion import udf
+from datafusion import udf, col
 
 from . import generic as helpers
 
@@ -374,3 +375,58 @@ def test_simple_select(ctx, tmp_path, arr):
     result = batches[0].column(0)
 
     np.testing.assert_equal(result, arr)
+
+
+@pytest.mark.parametrize("file_sort_order", (None, [[col("int").sort(True, True)]]))
+@pytest.mark.parametrize("pass_schema", (True, False))
+def test_register_listing_table(ctx, tmp_path, pass_schema, file_sort_order):
+    dir_root = tmp_path / "dataset_parquet_partitioned"
+    dir_root.mkdir(exist_ok=False)
+    (dir_root / "grp=a/date_id=20201005").mkdir(exist_ok=False, parents=True)
+    (dir_root / "grp=a/date_id=20211005").mkdir(exist_ok=False, parents=True)
+    (dir_root / "grp=b/date_id=20201005").mkdir(exist_ok=False, parents=True)
+
+    table = pa.Table.from_arrays(
+        [
+            [1, 2, 3, 4, 5, 6, 7],
+            ["a", "b", "c", "d", "e", "f", "g"],
+            [1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7],
+        ],
+        names=["int", "str", "float"],
+    )
+    pa.parquet.write_table(
+        table.slice(0, 3), dir_root / "grp=a/date_id=20201005/file.parquet"
+    )
+    pa.parquet.write_table(
+        table.slice(3, 2), dir_root / "grp=a/date_id=20211005/file.parquet"
+    )
+    pa.parquet.write_table(
+        table.slice(5, 10), dir_root / "grp=b/date_id=20201005/file.parquet"
+    )
+
+    ctx.register_object_store("file://local", LocalFileSystem(), None)
+    ctx.register_listing_table(
+        "my_table",
+        f"file://{dir_root}/",
+        table_partition_cols=[("grp", "string"), ("date_id", "int")],
+        file_extension=".parquet",
+        schema=table.schema if pass_schema else None,
+        file_sort_order=file_sort_order,
+    )
+    assert ctx.tables() == {"my_table"}
+
+    result = ctx.sql(
+        "SELECT grp, COUNT(*) AS count FROM my_table GROUP BY grp"
+    ).collect()
+    result = pa.Table.from_batches(result)
+
+    rd = result.to_pydict()
+    assert dict(zip(rd["grp"], rd["count"])) == {"a": 5, "b": 2}
+
+    result = ctx.sql(
+        "SELECT grp, COUNT(*) AS count FROM my_table WHERE date_id=20201005 GROUP BY grp"
+    ).collect()
+    result = pa.Table.from_batches(result)
+
+    rd = result.to_pydict()
+    assert dict(zip(rd["grp"], rd["count"])) == {"a": 3, "b": 2}

docs/source/index.rst

@@ -75,6 +75,7 @@ Example
    Github and Issue Tracker <https://github.com/apache/arrow-datafusion-python>
    Rust's API Docs <https://docs.rs/datafusion/latest/datafusion/>
    Code of conduct <https://github.com/apache/arrow-datafusion/blob/main/CODE_OF_CONDUCT.md>
+   Examples <https://github.com/apache/arrow-datafusion-python/tree/main/examples>
 
 .. _toc.guide:
 .. toctree::
@@ -84,6 +85,7 @@ Example
 
    user-guide/introduction
    user-guide/basics
+   user-guide/configuration
    user-guide/common-operations/index
    user-guide/io/index
    user-guide/sql

docs/source/user-guide/configuration.rst

@@ -0,0 +1,51 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+.. or more contributor license agreements.  See the NOTICE file
+.. distributed with this work for additional information
+.. regarding copyright ownership.  The ASF licenses this file
+.. to you under the Apache License, Version 2.0 (the
+.. "License"); you may not use this file except in compliance
+.. with the License.  You may obtain a copy of the License at
+
+..   http://www.apache.org/licenses/LICENSE-2.0
+
+.. Unless required by applicable law or agreed to in writing,
+.. software distributed under the License is distributed on an
+.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+.. KIND, either express or implied.  See the License for the
+.. specific language governing permissions and limitations
+.. under the License.
+
+Configuration
+========
+
+Let's look at how we can configure DataFusion. When creating a :code:`SessionContext`, you can pass in
+a :code:`SessionConfig` and :code:`RuntimeConfig` object. These two cover a wide range of options.
+
+.. code-block:: python
+
+    from datafusion import RuntimeConfig, SessionConfig, SessionContext
+
+    # create a session context with default settings
+    ctx = SessionContext()
+    print(ctx)
+
+    # create a session context with explicit runtime and config settings
+    runtime = RuntimeConfig().with_disk_manager_os().with_fair_spill_pool(10000000)
+    config = (
+        SessionConfig()
+        .with_create_default_catalog_and_schema(True)
+        .with_default_catalog_and_schema("foo", "bar")
+        .with_target_partitions(8)
+        .with_information_schema(True)
+        .with_repartition_joins(False)
+        .with_repartition_aggregations(False)
+        .with_repartition_windows(False)
+        .with_parquet_pruning(False)
+        .set("datafusion.execution.parquet.pushdown_filters", "true")
+    )
+    ctx = SessionContext(config, runtime)
+    print(ctx)
+
+
+You can read more about available :code:`SessionConfig` options `here <https://arrow.apache.org/datafusion/user-guide/configs.html>`_,
+and about :code:`RuntimeConfig` options `here https://docs.rs/datafusion/latest/datafusion/execution/runtime_env/struct.RuntimeConfig.html`_.

src/context.rs

@@ -39,10 +39,14 @@ use crate::store::StorageContexts;
 use crate::udaf::PyAggregateUDF;
 use crate::udf::PyScalarUDF;
 use crate::utils::{get_tokio_runtime, wait_for_future};
-use datafusion::arrow::datatypes::{DataType, Schema};
+use datafusion::arrow::datatypes::{DataType, Schema, SchemaRef};
 use datafusion::arrow::pyarrow::PyArrowType;
 use datafusion::arrow::record_batch::RecordBatch;
 use datafusion::datasource::file_format::file_compression_type::FileCompressionType;
+use datafusion::datasource::file_format::parquet::ParquetFormat;
+use datafusion::datasource::listing::{
+    ListingOptions, ListingTable, ListingTableConfig, ListingTableUrl,
+};
 use datafusion::datasource::MemTable;
 use datafusion::datasource::TableProvider;
 use datafusion::execution::context::{SessionConfig, SessionContext, SessionState, TaskContext};
@@ -244,7 +248,7 @@ impl PySessionContext {
         })
     }
 
-    /// Register a an object store with the given name
+    /// Register an object store with the given name
     pub fn register_object_store(
         &mut self,
         scheme: &str,
@@ -278,6 +282,53 @@ impl PySessionContext {
         Ok(())
     }
 
+    #[allow(clippy::too_many_arguments)]
+    #[pyo3(signature = (name, path, table_partition_cols=vec![],
+    file_extension=".parquet",
+    schema=None,
+    file_sort_order=None))]
+    pub fn register_listing_table(
+        &mut self,
+        name: &str,
+        path: &str,
+        table_partition_cols: Vec<(String, String)>,
+        file_extension: &str,
+        schema: Option<PyArrowType<Schema>>,
+        file_sort_order: Option<Vec<Vec<PyExpr>>>,
+        py: Python,
+    ) -> PyResult<()> {
+        let options = ListingOptions::new(Arc::new(ParquetFormat::new()))
+            .with_file_extension(file_extension)
+            .with_table_partition_cols(convert_table_partition_cols(table_partition_cols)?)
+            .with_file_sort_order(
+                file_sort_order
+                    .unwrap_or_default()
+                    .into_iter()
+                    .map(|e| e.into_iter().map(|f| f.into()).collect())
+                    .collect(),
+            );
+        let table_path = ListingTableUrl::parse(path)?;
+        let resolved_schema: SchemaRef = match schema {
+            Some(s) => Arc::new(s.0),
+            None => {
+                let state = self.ctx.state();
+                let schema = options.infer_schema(&state, &table_path);
+                wait_for_future(py, schema).map_err(DataFusionError::from)?
+            }
+        };
+        let config = ListingTableConfig::new(table_path)
+            .with_listing_options(options)
+            .with_schema(resolved_schema);
+        let table = ListingTable::try_new(config)?;
+        self.register_table(
+            name,
+            &PyTable {
+                table: Arc::new(table),
+            },
+        )?;
+        Ok(())
+    }
+
     /// Returns a PyDataFrame whose plan corresponds to the SQL statement.
     pub fn sql(&mut self, query: &str, py: Python) -> PyResult<PyDataFrame> {
         let result = self.ctx.sql(query);
@@ -849,8 +900,9 @@ pub fn convert_table_partition_cols(
         .into_iter()
         .map(|(name, ty)| match ty.as_str() {
             "string" => Ok((name, DataType::Utf8)),
+            "int" => Ok((name, DataType::Int32)),
             _ => Err(DataFusionError::Common(format!(
-                "Unsupported data type '{ty}' for partition column"
+                "Unsupported data type '{ty}' for partition column. Supported types are 'string' and 'int'"
             ))),
         })
         .collect::<Result<Vec<_>, _>>()

src/dataframe.rs

@@ -424,10 +424,7 @@ impl PyDataFrame {
         let stream = wait_for_future(py, fut).map_err(py_datafusion_err)?;
 
         match stream {
-            Ok(batches) => Ok(batches
-                .into_iter()
-                .map(|batch_stream| PyRecordBatchStream::new(batch_stream))
-                .collect()),
+            Ok(batches) => Ok(batches.into_iter().map(PyRecordBatchStream::new).collect()),
             _ => Err(PyValueError::new_err(
                 "Unable to execute stream partitioned",
             )),