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processing, etc. + +The library is divided into three layers: the basic Kafka connectivity, the +streaming engine and the high level abstractions. + +The basic connectivity layer is a simple wrapper around the Confluent python +library, which is itself based on librdkafka. Besides some cosmetic changes, +this level provides a Fake in memory broker and consumer to make unit test quick +to run. + +The streaming engine provides an asynchronous processing interface to write +consumers. The consumer is written as a pipeline where each segment is an +asynchronous operation. The streaming engine implements the main consumer loop +and delegates the processing to the pipeline. + +On top of the streaming engine, the library provides high-level abstractions that +are common when writing Kafka consumers like: *map*, *reduce*, *filter* together +with some common messaging application patterns like the dead letter queue. + +Streaming Interface and Streaming Engine +---------------------------------------- + +A Kafka consumer is built as a pipeline where each segment processes messages in +an asynchronous way. The Streaming engine provides a message to a segment. The +segment is not supposed to execute small CPU work in a blocking way or do IO in a +non-blocking way. We generally use futures for this, and heavier CPU work in a +separate process. + +Arroyo provides an interface to implement to write a pipeline segment. +The segment interface is called *ProcessingStrategy* and is in +`this module `_. +(TODO: bring the docstrings to the docs and reference that). + +In most cases, when developing a consumer, the developer would not implement +that interface directly. A higher level abstraction would be used. + +.. figure:: _static/diagrams/arroyo_processing.png + +The main consumer loop is managed by the `stream engine `_. +These are the phases: + +* Poll from the Kafka consumer through the basic library. If a message is there + proceed or repeat. + +* Submit the message to the first *ProcessingStrategy*. This is supposed to deliver + work for the strategy to do. It is not supposed to be a blocking operation. The + strategy should return immediately. + +* Poll the strategy to execute work or to forward results to the following step + in the pipeline. Ideally all IO should be done in separate threads and heavy cpu + work should be done in separate processes so the *poll* method should check for + completed work, dispatch to the next step and return. In practice, work is executed + here in a blocking way if the overhead of offloading the work is too high. + +The *ProcessingStrategy* may decide not to take the message and instead apply back-pressure. +This is done by raising the *MessageRejected* exception. In this case, the streaming +engine pauses the consumer till the strategy is ready to accept the message. + +The *ProcessingStrategy* decides when it is time to commit a message. This is done +through a commit callback provided to the strategy when it is instantiated. + +The streaming engine orchestrates the life cycle of the *ProcessingStrategy*, thus +when it thinks it is time to shut the strategy down it would wait for all in-flight +work to be completed and then destroy the strategy. + +There are two scenarios where this can happen: + +* The consumer is being terminated. +* A rebalancing happened. A rebalancing revokes partitions and assigns new ones. + After a rebalancing is complete it is impossible to commit a message from a partition + that was revoked. In order to ensure the consumer behaves in a consistent way, + upon rebalancing, the streaming engine destroys the strategy and builds a new one. + This allows the strategy to complete all in-flight work before being terminated. + +High level strategies +----------------------- + +Most consumers follow the same few patterns, so Arroyo provides abstractions that +are based on the *ProcessingStrategy* but are simpler to implement for the common +use cases. + +Common examples are: + +* ``run task, run task in threads, run task with multiprocessing``. The run task + set of strategies are designed to be the most flexible and simple to use. They take + a function provided by the user and execute it on every message, passing the output + to the next step. The library includes synchronous and asynchronous versions depending + on the kind of concurrency required by the user. + +* ``filter, map and forward``. This type of consumer inspects a message, decides + whether to process it or discard it, transforms its content, and produces the result + on a new topic. In this case, Arroyo provides three implementations of the + *ProcessingStrategy*: *filter*, *transform*, and *produce*. The developer only needs + to wire them together and provide the map and filtering logic. + +* ``consume, apply side effects, produce``. This is a variation of the one above. + In this case, the transform operation can have side-effects like storing the content + of the message somewhere. + +* ``high throughput cpu intensive transform``. The python GIL does not allow CPU intensive + work to take advantage of parallelism. Arroyo provides an implementation of the *map* + pattern that batches messages and dispatches the work to separate processes via shared + memory. This is largely transparent to the developers. + +* ``map, reduce and store``. The reduce function is carried out by the *Collector*, which + batches messages and executes some logic with side-effects when the batch is full. + This is a typical way to write messages on a storages in batches to reduce the + round trips. + +All strategies included with Arroyo are in `the strategies module `_. diff --git a/_sources/backpressure.rst.txt b/_sources/backpressure.rst.txt new file mode 100644 index 00000000..2dcc20df --- /dev/null +++ b/_sources/backpressure.rst.txt @@ -0,0 +1,43 @@ +Backpressure +============ + +.. py:currentmodule:: arroyo.processing.strategies + +Arroyo's own processing strategies internally apply backpressure by raising +:py:class:`~abstract.MessageRejected`. Most +consumers do not require additional work to deal with backpressure correctly. + +If you want to slow down the consumer based on some external signal or +condition, you can achieve that most effectively by raising the same exception +from within a callback passed to :py:class:`~run_task.RunTask` while the +consumer is supposed to be paused + +.. code-block:: Python + + class ConsumerStrategyFactory(ProcessingStrategyFactory[KafkaPayload]): + def __init__(self): + self.is_paused = False + + def create_with_partitions( + self, + commit: Commit, + partitions: Mapping[Partition, int], + ) -> ProcessingStrategy[KafkaPayload]: + def handle_message(message: Message[KafkaPayload]) -> Message[KafkaPayload]: + if self.is_paused: + raise MessageRejected() + + print(f"MSG: {message.payload}") + return message + + return RunTask(handle_message, CommitOffsets(commit)) + +It is not recommended to apply backpressure by just ``sleep()``-ing in +:py:class:`~abstract.ProcessingStrategy.submit` (or, in this example, +``handle_message``) for more than a few milliseconds. While this definitely +pauses the consumer, it will block the main thread for too long and and prevent +things like consumer rebalancing from occuring. + +A 0.01 second sleep is applied each time :py:class:`~abstract.MessageRejected` is +raised to prevent the main thread spinning at 100% CPU. However background thread +performance may be impacted during this time. diff --git a/_sources/dlqs.rst.txt b/_sources/dlqs.rst.txt new file mode 100644 index 00000000..62562a39 --- /dev/null +++ b/_sources/dlqs.rst.txt @@ -0,0 +1,21 @@ +================== +Dead letter queues +================== + +.. warning:: + Dead letter queues should be used with caution as they break some of the ordering guarantees + otherwise offered by Arroyo and Kafka consumer code. In particular, it must be safe for the + consumer to drop a message. If replaying or later re-processing of the DLQ'ed messages is done, + it is critical that ordering is not a requirement in the relevant downstream code. + +Arroyo provides support for routing invalid messages to dead letter queues in consumers. +Dead letter queues are critical in some applications because messages are ordered in Kafka +and a single invalid message can cause a consumer to crash and every subsequent message to +not be processed. + +The dead letter queue configuration is passed to the `StreamProcessor` and, if provided, any +`InvalidMessage` raise by a strategy will be produced to the dead letter queue. + + +.. automodule:: arroyo.dlq + :members: InvalidMessage, DlqLimit, DlqPolicy, DlqProducer, KafkaDlqProducer, NoopDlqProducer diff --git a/_sources/getstarted.rst.txt b/_sources/getstarted.rst.txt new file mode 100644 index 00000000..d0ce1308 --- /dev/null +++ b/_sources/getstarted.rst.txt @@ -0,0 +1,246 @@ +=========================== +Getting started with Arroyo +=========================== + +This tutorial shows how to create a Kafka consumer with Arroyo from scratch. + +Setup +===== + +This section explains how to setup Kafka, Zookeeper and install the library + +Kafka and Zookeeper +------------------- + +In order to run an arroyo Kafka consumer you will need a working Kafka broker. +If you already have one, you can skip this step. +If you do not have a running Kafka broker, this command will install and start +a Kafka docker container. (It requires Docker to be installed). + +.. code-block:: Bash + + docker network create arroyo + + docker run --rm \ + -v zookeeper_volume:/var/lib/zookeeper \ + --env ZOOKEEPER_CLIENT_PORT=2181 \ + --name=sentry_zookeeper \ + --network=arroyo \ + -p 2181:2181 \ + confluentinc/cp-zookeeper:6.2.0 + + docker run --rm \ + -v kafka_volume:/var/lib/kafka \ + --env KAFKA_ZOOKEEPER_CONNECT=sentry_zookeeper:2181 \ + --env KAFKA_LISTENERS=INTERNAL://0.0.0.0:9093,EXTERNAL://0.0.0.0:9092 \ + --env KAFKA_ADVERTISED_LISTENERS=INTERNAL://127.0.0.1:9093,EXTERNAL://127.0.0.1:9092 \ + --env KAFKA_LISTENER_SECURITY_PROTOCOL_MAP=INTERNAL:PLAINTEXT,EXTERNAL:PLAINTEXT \ + --env KAFKA_INTER_BROKER_LISTENER_NAME=INTERNAL \ + --env KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR=1 \ + --env CONFLUENT_SUPPORT_METRICS_ENABLE=false \ + --env KAFKA_LOG4J_LOGGERS=kafka.cluster=WARN,kafka.controller=WARN,kafka.coordinator=WARN,kafka.log=WARN,kafka.server=WARN,kafka.zookeeper=WARN,state.change.logger=WARN \ + --env KAFKA_LOG4J_ROOT_LOGLEVEL=WARN \ + --env KAFKA_TOOLS_LOG4J_LOGLEVEL=WARN \ + --name=sentry_kafka \ + --network=arroyo \ + -p 9092:9092 \ + confluentinc/cp-kafka:6.2.0 + +Now you should see Kafka and Zookeeper running with + +.. code-block:: Bash + + docker ps + +Install Kafkacat +---------------- + +This tool will be useful to produce onto and consume from topics. + +.. code-block:: Bash + + https://docs.confluent.io/platform/current/app-development/kafkacat-usage.html#kcat-formerly-kafkacat-utility + + +Development environment +----------------------- + +You will need to install the library. Most likely in a python venv. So first, create a python virtual +environment. Then you can install arroyo with this. + +.. code-block:: Bash + + pip install sentry-arroyo + +Create two Kafka topics +----------------------- + +Our example will consume from one topic and produce the same messages on another topic. So we need +two topics. + +.. code-block:: Bash + + docker exec sentry_kafka kafka-topics \ + --create \ + --topic source-topic \ + --bootstrap-server 127.0.0.1:9092 + + docker exec sentry_kafka kafka-topics \ + --create \ + --topic dest-topic \ + --bootstrap-server 127.0.0.1:9092 + +Now you should be ready to develop with Arroyo. + +Create a basic consumer +======================= + +Arroyo provides two level of abstractions when writing a consumer: the basic consumer/producer library +and the Streaming library. The first is just a thin wrapper around a librdkafka consumer/producer that +adds some features around offset management. The second provides a more abstract streaming interface +that hides details like rebalancing and the consumer lifecycle. + +Creating a basic consumer +------------------------- + +This initializes a basic consumer and consumes a message. + +.. code-block:: Python + + from arroyo.backends.kafka.configuration import ( + build_kafka_consumer_configuration, + ) + from arroyo.backends.kafka.consumer import KafkaConsumer + from arroyo.types import Topic + + TOPIC = Topic("source-topic") + + consumer = KafkaConsumer( + build_kafka_consumer_configuration( + default_config={}, + bootstrap_servers=["127.0.0.1:9092"], + auto_offset_reset="latest", + group_id="test-group", + ) + ) + + consumer.subscribe([TOPIC]) + + while True: + msg = consumer.poll(timeout=1.0) + if msg is not None: + print(f"MSG: {msg.payload}") + +Start this script and use kcat to produce a message: + +.. code-block:: Bash + + echo "MESSAGE" | kcat -P -b 127.0.0.1:9092 -t source-topic + +In a while the message should appear on the console: + +.. code-block:: Bash + + MSG: KafkaPayload(key=None, value=b'MESSAGE', headers=[]) + + +Create a streaming consumer +--------------------------- + +Add a `ProcessingStrategy` and `ProcessingStrategyFactory`. +Here we are using the `RunTask` strategy which runs a custom function over each message. + +.. code-block:: Python + + from typing import Mapping + + from arroyo.backends.kafka import KafkaPayload + from arroyo.processing.strategies import ( + CommitOffsets, + ProcessingStrategy, + ProcessingStrategyFactory, + RunTask, + ) + from arroyo.types import Commit, Message, Partition, Topic + + + def handle_message(message: Message[KafkaPayload]) -> Message[KafkaPayload]: + print(f"MSG: {message.payload}") + return message.payload + + class ConsumerStrategyFactory(ProcessingStrategyFactory[KafkaPayload]): + """ + The factory manages the lifecycle of the `ProcessingStrategy`. + A strategy is created every time new partitions are assigned to the + consumer, while it is destroyed when partitions are revoked or the + consumer is closed + """ + def create_with_partitions( + self, + commit: Commit, + partitions: Mapping[Partition, int], + ) -> ProcessingStrategy[KafkaPayload]: + return RunTask(handle_message, CommitOffsets(commit)) + +The code above is orchestrated by the Arroyo runtime called `StreamProcessor`. + +.. code-block:: Python + + from arroyo.processing import StreamProcessor + from arroyo.commit import ONCE_PER_SECOND + + processor = StreamProcessor( + consumer=consumer, + topic=TOPIC, + processor_factory=ConsumerStrategyFactory(), + commit_policy=ONCE_PER_SECOND, + ) + + processor.run() + +The main consumer loop is managed by the `StreamProcessor` no need to periodically poll the +consumer. The `ConsumerStrategy` works by inversion of control. + +Add some useful logic +--------------------- + +Now we will chain the `Produce` strategy to produce messages on a second topic after the message is logged + +.. code-block:: Python + + from arroyo.backends.kafka import KafkaProducer + from arroyo.backends.kafka.configuration import build_kafka_configuration + from arroyo.processing.strategies import Produce + + class ConsumerStrategyFactory(ProcessingStrategyFactory[KafkaPayload]): + """ + The factory manages the lifecycle of the `ProcessingStrategy`. + A strategy is created every time new partitions are assigned to the + consumer, while it is destroyed when partitions are revoked or the + consumer is closed + """ + def create_with_partitions( + self, + commit: Commit, + partitions: Mapping[Partition, int], + ) -> ProcessingStrategy[KafkaPayload]: + producer = KafkaProducer( + build_kafka_configuration( + default_config={}, + bootstrap_servers=BOOTSTRAP_SERVERS, + ) + ) + + return RunTask( + handle_message, + Produce(producer, Topic("dest-topic"), CommitOffsets(commit)) + ) + +The message is first passed to the `RunTask` strategy which simply logs the message and submits +the output to the next step. The `Produce` strategy produces the message asynchronously. Once +the message is produced, the `CommitOffsets` strategy commits the offset of the message. + +Further examples +================ + +Find some complete `examples of usage `_. diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 00000000..fb702775 --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,16 @@ +.. include:: intro.rst + +Contents: +--------- + +.. toctree:: + :maxdepth: 2 + + what_for + getstarted + architecture + strategies/index + offsets + dlqs + backpressure + metrics diff --git a/_sources/intro.rst.txt b/_sources/intro.rst.txt new file mode 100644 index 00000000..72b0d4e2 --- /dev/null +++ b/_sources/intro.rst.txt @@ -0,0 +1,15 @@ +.. image:: _static/arroyo-banner.png + :width: 583 + :height: 95 + +Arroyo is a library to build streaming applications that consume from +and produce to Kafka. + +It relies on the `confluent_kafka` python library, which itself relies +on `librdkafka`. + +Arroyo provides mainly three functionalities: + +* A set of abstractions inspired to common messaging applications patterns. +* Some abstractions to simplify offset management and rebalancing. +* An in memory broker abstraction to simplify writing unit tests. diff --git a/_sources/metrics.rst.txt b/_sources/metrics.rst.txt new file mode 100644 index 00000000..c005c740 --- /dev/null +++ b/_sources/metrics.rst.txt @@ -0,0 +1,63 @@ +================== +Metrics +================== + + +Arroyo consumers and strategies attempt to auto instrument some metrics that most people find useful +to understand the behavior and performance of their consumers. These metrics are typically sampled or +buffered as appropriate and flushed periodically (often once per second). + +In order to use these metrics, you must configure a metrics backend that conforms to the metrics protocol +before creating your consumer. + +This can be done like so: + +.. code:: python + + from arroyo.utils.metrics import Metrics, MetricName + + class MyMetrics(Metrics): + def increment( + self, name: MetricName, value: Union[int, float] = 1, tags: Optional[Tags] = None + ) -> None: + # Increment a counter by the given value. + record_incr(name, value, tags) + + def gauge( + self, name: MetricName, value: Union[int, float], tags: Optional[Tags] = None + ) -> None: + # Sets a gauge metric to the given value. + record_gauge(name, value, tags) + + def timing( + self, name: MetricName, value: Union[int, float], tags: Optional[Tags] = None + ) -> None: + # Emit a timing metric with the given value. + record_timing(name, value, tags) + + metrics_backend = MyMetrics() + + configure_metrics(metrics_backend) + + +Available Metrics +==================== + +.. literalinclude:: ../../arroyo/utils/metric_defs.py + +For convenience Arroyo includes a machine readable version which can be loaded like: + +.. code:: python + + import importlib.resources + import json + + with importlib.resources.files("arroyo.utils").joinpath("metricDefs.json").open() as f: + metric_defs = json.load(f) + +API +======= + +.. automodule:: arroyo.utils.metrics + :members: + :undoc-members: diff --git a/_sources/offsets.rst.txt b/_sources/offsets.rst.txt new file mode 100644 index 00000000..6316e653 --- /dev/null +++ b/_sources/offsets.rst.txt @@ -0,0 +1,37 @@ +================== +Committing offsets +================== + +Arroyo does not auto commit offsets. It is up to you to manually commit offsets when processing for that +message is completed. + +The commit callback will be passed to processing strategy via `ProcessingStrategyFactory.create_with_partitions`. +You should pass this to the strategy and have your strategy call this commit function once the rest of the message +processing has been done. + +The offset to be committed in Kafka is always the next offset to be consumed from, i.e. message's offset + 1. +In Arroyo, this means you should commit `Message.next_offset` and never `Message.offset` when done processing +that message. Arroyo exposes `Message.position_to_commit` to make this easier. + +It is not safe to commit every offset in a high throughput consumer as this will add a lot of load to the system. +Commits should generally be throttled. `CommitPolicy` is the Arroyo way of specifying commit frequency. A `CommitPolicy` +must be passed to the stream processor, which allows specifying a minimum commit frequency (or messages between commits). +Commit throttling can be skipped when needed (i.e. during consumer shutdown) by passing `force=True` to the commit callback. +If you are not sure how often to commit, `ONCE_PER_SECOND` is a reasonable option. + +The easiest way is to use the `CommitOffsets` strategy as the last step in a chain of processing strategies to commit offsets. + +.. code-block:: Python + + class MyConsumerFactoryFactory(ProcessingStrategyFactory[KafkaPayload]): + def create_with_partitions( + self, + commit: Commit, + partitions: Mapping[Partition, int], + ) -> ProcessingStrategy[KafkaPayload]: + def my_processing_function(message: Message[KafkaPayload]) -> None: + # do something (synchronous) with the message + do_something() + + + return RunTask(my_processing_function, CommitOffsets(commit)) diff --git a/_sources/strategies/batching.rst.txt b/_sources/strategies/batching.rst.txt new file mode 100644 index 00000000..540d7d8a --- /dev/null +++ b/_sources/strategies/batching.rst.txt @@ -0,0 +1,10 @@ +Batch and Unbatch +----------------------------- + +Accumulate messages into a batch and pass to the next step. +The batch and unbatch strategies are based on reduce and unfold. +Use reduce/unfold instead if you want to provide custom +accumulator/generator functions. + +.. automodule:: arroyo.processing.strategies.batching + :members: diff --git a/_sources/strategies/commit_offsets.rst.txt b/_sources/strategies/commit_offsets.rst.txt new file mode 100644 index 00000000..64d3504c --- /dev/null +++ b/_sources/strategies/commit_offsets.rst.txt @@ -0,0 +1,8 @@ +Commit offsets +----------------------------- + +Should be used as the last strategy in the chain, to ensure +that offsets are only committed once all processing is complete. + +.. automodule:: arroyo.processing.strategies.commit + :members: diff --git a/_sources/strategies/filter.rst.txt b/_sources/strategies/filter.rst.txt new file mode 100644 index 00000000..22cba221 --- /dev/null +++ b/_sources/strategies/filter.rst.txt @@ -0,0 +1,5 @@ +Filter +----------------------------- + +.. automodule:: arroyo.processing.strategies.filter + :members: diff --git a/_sources/strategies/healthcheck.rst.txt b/_sources/strategies/healthcheck.rst.txt new file mode 100644 index 00000000..89fe7a3c --- /dev/null +++ b/_sources/strategies/healthcheck.rst.txt @@ -0,0 +1,74 @@ +Healthchecks +============ + +If your code blocks for too long in the main thread, the consumer can turn +unhealthy. + +Kafka has a setting called ``max.poll.interval.ms`` for this that tells Kafka +to kick the consumer out of the broker after this many milliseconds of not polling. + +You can pass this option into :py:class:`arroyo.backends.kafka.consumer.KafkaConsumer` like so: + +.. code-block:: Python + + consumer = KafkaConsumer( + { + "max.poll.interval.ms": 300000, # default 5 minutes + } + ) + +However, this will not shut down the consumer, it will just keep running doing +nothing (because it is blocked in the main thread). You want a pod-level +healthcheck as well. + +Arroyo supports touching a file repeatedly from the main thread to indicate +health. Start your pipeline with the +:py:class:`arroyo.processing.strategies.healthcheck.Healthcheck` strategy. + +.. code-block:: Python + + def handle_message(message: Message[KafkaPayload]) -> Message[KafkaPayload]: + ... + return message + + class ConsumerStrategyFactory(ProcessingStrategyFactory[KafkaPayload]): + def __init__(self): + self.is_paused = False + + def create_with_partitions( + self, + commit: Commit, + partitions: Mapping[Partition, int], + ) -> ProcessingStrategy[KafkaPayload]: + step = RunTask(handle_message, CommitOffsets(commit)) + return Healthcheck("/tmp/health.txt", step) + +The Kubernetes `liveness +`_ +command would look like: + +.. code-block:: YAML + + apiVersion: v1 + kind: Pod + metadata: + labels: + test: liveness + name: liveness-exec + spec: + containers: + - name: liveness + image: registry.k8s.io/busybox + args: + - bin/my_arroyo_consumer + livenessProbe: + exec: + command: + - rm + - /tmp/health.txt + initialDelaySeconds: 5 + periodSeconds: 320 # should be higher than max.poll.interval.ms + + +.. automodule:: arroyo.processing.strategies.healthcheck + :members: diff --git a/_sources/strategies/index.rst.txt b/_sources/strategies/index.rst.txt new file mode 100644 index 00000000..60d75e1a --- /dev/null +++ b/_sources/strategies/index.rst.txt @@ -0,0 +1,39 @@ +Processing Strategies +===================== + +The processing strategies are the components to be wired together to +build a consumer. + +Strategy interface +------------------------- + +We normally don't recommend writing your own strategy, and encourage you to use +built-in ones such as "reduce" or "run task" to plug in your application logic. +Nevertheless, all arroyo strategies are written against the following interface: + +.. automodule:: arroyo.processing.strategies.abstract + :members: + :undoc-members: + :show-inheritance: + +Messages +------------ + +.. automodule:: arroyo.types + :members: + :undoc-members: + +.. toctree:: + :hidden: + + filter + reduce + unfold + batching + run_task + run_task_in_threads + run_task_with_multiprocessing + produce + commit_offsets + noop + healthcheck diff --git a/_sources/strategies/noop.rst.txt b/_sources/strategies/noop.rst.txt new file mode 100644 index 00000000..5a2dc622 --- /dev/null +++ b/_sources/strategies/noop.rst.txt @@ -0,0 +1,5 @@ +Noop +----------------------------- + +.. automodule:: arroyo.processing.strategies.noop + :members: diff --git a/_sources/strategies/produce.rst.txt b/_sources/strategies/produce.rst.txt new file mode 100644 index 00000000..d40c3ba8 --- /dev/null +++ b/_sources/strategies/produce.rst.txt @@ -0,0 +1,5 @@ +Produce +----------------------------- + +.. automodule:: arroyo.processing.strategies.produce + :members: diff --git a/_sources/strategies/reduce.rst.txt b/_sources/strategies/reduce.rst.txt new file mode 100644 index 00000000..41be2365 --- /dev/null +++ b/_sources/strategies/reduce.rst.txt @@ -0,0 +1,7 @@ +Reduce (Fold) +----------------------------- + +Accumulate messages based on a custom accumulator function + +.. automodule:: arroyo.processing.strategies.reduce + :members: diff --git a/_sources/strategies/run_task.rst.txt b/_sources/strategies/run_task.rst.txt new file mode 100644 index 00000000..bce67b48 --- /dev/null +++ b/_sources/strategies/run_task.rst.txt @@ -0,0 +1,5 @@ +Run Task +----------------------------- + +.. automodule:: arroyo.processing.strategies.run_task + :members: diff --git a/_sources/strategies/run_task_in_threads.rst.txt b/_sources/strategies/run_task_in_threads.rst.txt new file mode 100644 index 00000000..84ea0c08 --- /dev/null +++ b/_sources/strategies/run_task_in_threads.rst.txt @@ -0,0 +1,5 @@ +Run Task in Threads +----------------------------- + +.. automodule:: arroyo.processing.strategies.run_task_in_threads + :members: diff --git a/_sources/strategies/run_task_with_multiprocessing.rst.txt b/_sources/strategies/run_task_with_multiprocessing.rst.txt new file mode 100644 index 00000000..1855dbbd --- /dev/null +++ b/_sources/strategies/run_task_with_multiprocessing.rst.txt @@ -0,0 +1,5 @@ +Run Task with Multiprocessing +----------------------------- + +.. automodule:: arroyo.processing.strategies.run_task_with_multiprocessing + :members: diff --git a/_sources/strategies/unfold.rst.txt b/_sources/strategies/unfold.rst.txt new file mode 100644 index 00000000..113dc3b7 --- /dev/null +++ b/_sources/strategies/unfold.rst.txt @@ -0,0 +1,7 @@ +Unfold +----------------------------- + +Generates a sequence of messages from a single message based on a custom generator function + +.. automodule:: arroyo.processing.strategies.unfold + :members: diff --git a/_sources/what_for.rst.txt b/_sources/what_for.rst.txt new file mode 100644 index 00000000..7e2d04fd --- /dev/null +++ b/_sources/what_for.rst.txt @@ -0,0 +1,276 @@ +What is Arroyo for? +=================== + +Arroyo is a library for writing high-throughput, testable kafka +consumers and producers. This document attempts to outline the +intricacies of writing such consumers. + +Goals +----- + +1. Make it easy to build consumers that provide delivery guarantees +2. Make it easy to write high-throughput kafka consumers +3. Make it easy to write reliable, high throughput kafka producers +4. Abstract away rebalancing such that users of the library do not have + to worry about it +5. Make it possible to test kafka consumer logic without having to + reproduce the entire kafka environment +6. Provide a way for the application logic to signal backpressure + +Why Simple Doesn’t Cut It +------------------------- + +When visualizing event-driven architecture, Kafka is viewed as an +abstract queue with groups of producers pushing to it, and consumers +consuming from it (as in the diagram below). + +.. mermaid:: + + graph TD + Producer --> Kafka_Topic + Kafka_Topic --> Consumer + Consumer --> Destination + +A more accurate model is that kafka is like a log file which is +persistent and there are offsets of the file that different consumers +have read or not read. + +The most simple kafka consumer looks something like this: + +.. code:: python + + from confluent_kafka import Consumer + + conf = { + "bootstrap.servers": "localhost:9092", + "group.id": "my_group", + "auto.offset.reset": "latest", + } + + consumer = Consumer(conf) + consumer.subscribe(["my_topic"]) + + while True: + message = consumer.poll() + send_to_destination(process_message(message)) + +This simple consumer would not satisfy the goals mentioned at the top of +this page. The following subsections will explain why + +Providing delivery guarantees +----------------------------- + +By default, a consumer in the ``confluent_kafka`` library will +auto-commit on poll. To understand what it means to commit to a kafka +topic, see the Appendix. This can lead to the following issue: + +.. code:: python + + # get message from kafka, commit immediately + message = consumer.poll() + # ❗❗❗ throws exception due to a network issue + send_to_destination(process_message(message)) + # this message is now lost and we're on to the next one + +This can be fixed by only committing after we know that the message has +reached its destination in the following way: + +.. code:: python + + # add this value to the config: + "enable.auto.commit": "false" + # ------- + message = consumer.poll(timeout=0) + send_to_destination(process_message(message)) + consumer.commit(message.offset()) + +High Throughput +--------------- + +The previous section has allowed us to not commit messages that are not +processed however committing every message severely hurts throughput. +Every call to commit is a network operation, it also makes the broker +persist and replicate the information. If we can reduce the number of +commit calls, our throughput can be much higher. And so we commit in +batches + +.. code:: python + + # this code is purely descriptive. + # We have to commit to each partition separately + # but that code is not helpful for this example + message = consumer.poll(timeout=0) + batch.append(process_message(message)) + if len(batch) == batch_size: + consumer.commit(offsets=[m.offset() for m in batch]) + +This will get us faster throughput however we are currently hand-waving +away how we send the message to its destination + +Reliable High Throughput Batched Producers +------------------------------------------ + +Producing to Kafka reliably and at high throughput is not a simple +operation. Here is how a simple Kafka Producer looks in code: + +.. code:: python + + from confluent_kafka import Producer + + conf = { + "bootstrap.servers": "localhost:9092", + } + producer = Producer(conf) + def send_to_destination(message): + # ❗ This does not do what it says + # it writes to a buffer + producer.produce("destination_topic", message) + # this will actually block until the messages are produced + # calling this after produce every time is very expensive, + # how often we flush has high impacts on the producer throughput + producer.flush() + + +At a high level, the producer is actually buffering the messages +produced to the topic + +.. image:: _static/diagrams/kafka_producer.png + +A kafka producer writes to an internal buffer. This batches the IO +(good) but you don’t know when it will ever make it to the +destination + +In order to allow for reliability of transmission, the +``confluent_kafka`` library provides `a callback to +produce `__ +like so + +.. code:: python + + def delivery_callback(error, message): + # do something here to make sure your message is in the state + # you want it to be + + producer.produce("destination_topic", message, on_delivery=delivery_callback) + +Dealing With Rebalancing +------------------------ + +What is Rebalancing +~~~~~~~~~~~~~~~~~~~ + +A kafka topic is divided into n partitions, each partition can be +consumed by exactly one consumer per `consumer +group `__. A consumer can +consume multiple partitions + +.. figure:: _static/diagrams/consumer_groups.png + +When Rebalancing Can Happen +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Rebalancing can happen due to: + +- An addition or removal of a consumer to a consumer group + + - (Every deploy does this) + +- A rebalance being kicked off manually +- A consumer pod dies and now its partition needs to be re-assigned +- Whenever the broker decides it’s a good idea (it can happen at any + time) +- TODO: More things? + +How Rebalancing Affects a Consumer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Rebalancing is annoying to handle for a consumer that processes batches, +imagine the following scenario: + +.. mermaid:: + + sequenceDiagram + Broker->>Consumer: message + activate Consumer + note right of Consumer: start building batch + Broker->>Consumer: message + Broker->>Consumer: Revoke Partition + deactivate Consumer + Consumer->>Broker: commit batch + note left of Broker: Received commit from revoked Consumer! + +Once a partition is revoked for a consumer, it cannot commit to it. This +is bad news for the batch that the consumer has built up. Each consumer +has different requirements but a decision has to be made as to whether +to flush the batch or to discard its work and let the next consumer +assigned to this partition pick it up. The rebalancing behavior can be +customized by providing an ``on_revoke`` callback to the consumer when +subscribing. + +.. code:: python + + from confluent_kafka import Consumer + + conf = { + "bootstrap.servers": "localhost:9092", + "group.id": "my_group", + "auto.offset.reset": "latest", + } + + def flush_current_batch(consumer, partitions): + # flush the current batch + pass + + consumer = Consumer(conf) + consumer.subscribe(["my_topic"], on_revoke=flush_current_batch) + +librdkafka’s Callback Hell +-------------------------- + +librdkafka uses callbacks as a core mechanic for control flow. A few +such examples have been mentioned in this document already. What is not +clear however, is that **callbacks are only called when ``poll`` is +called** + +This means that: + +- this line could possibly do a lot of work: + +.. code:: python + + # any scheduled callbacks will run within this call + message = consumer.poll() + +- No callbacks will be invoked until the consumer or producer call + ``poll`` again (for their respective callbacks) +- ``poll`` has to be called periodically on a consumer otherwise the + broker will kick the consumer out of the consumer group + + - The handling of that revocation won’t happen until ``poll`` is + called + +Conclusion +---------- + +There are many intricacies and gotchas to writing high performant, +reliable kafka consumers. This document does not outline all of them but +all of what is outlined here should be kept in mind when designing any +kafka consumer library. + +Appendix +-------- + +Committing to a Kafka Topic +--------------------------- + +A consumer comitting to a topic signals to the broker that this message +has been processed. When ``poll`` is called next by that consumer, it +will return the next message. + +`API +Doc `__ + +What is a Kafka Consumer Group +------------------------------ + +https://www.educba.com/kafka-consumer-group/ diff --git a/_static/arroyo-banner.png b/_static/arroyo-banner.png new file mode 100644 index 00000000..90482cb6 Binary files /dev/null and b/_static/arroyo-banner.png differ diff --git a/_static/arroyo-logo.png b/_static/arroyo-logo.png new file mode 100644 index 00000000..8c795208 Binary files /dev/null and b/_static/arroyo-logo.png differ diff --git a/_static/basic.css b/_static/basic.css new file mode 100644 index 00000000..cfc60b86 --- /dev/null +++ b/_static/basic.css @@ -0,0 +1,921 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + 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00000000..a738cff4 --- /dev/null +++ b/_static/custom.css @@ -0,0 +1,10 @@ +/* fix mermaid diagrams in dark mode of shibuya theme we use */ + +html.dark .mermaid path { + stroke: #fff !important; +} + +html.dark .mermaid .marker { + stroke: #fff !important; + fill: #fff !important; +} diff --git a/_static/diagrams/arroyo_processing.png b/_static/diagrams/arroyo_processing.png new file mode 100644 index 00000000..21004dde Binary files /dev/null and b/_static/diagrams/arroyo_processing.png differ diff --git a/_static/diagrams/consumer_groups.png b/_static/diagrams/consumer_groups.png new file mode 100644 index 00000000..09aa7440 Binary files /dev/null and b/_static/diagrams/consumer_groups.png differ diff --git a/_static/diagrams/kafka_producer.png b/_static/diagrams/kafka_producer.png new file mode 100644 index 00000000..53a90d9d Binary files /dev/null and b/_static/diagrams/kafka_producer.png differ diff --git a/_static/doctools.js b/_static/doctools.js new file mode 100644 index 00000000..d06a71d7 --- /dev/null +++ b/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/_static/documentation_options.js b/_static/documentation_options.js new file mode 100644 index 00000000..b57ae3b8 --- /dev/null +++ b/_static/documentation_options.js @@ -0,0 +1,14 @@ +var DOCUMENTATION_OPTIONS = { + URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), + VERSION: '', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/_static/file.png b/_static/file.png new file mode 100644 index 00000000..a858a410 Binary files /dev/null and b/_static/file.png differ diff --git a/_static/language_data.js b/_static/language_data.js new file mode 100644 index 00000000..250f5665 --- /dev/null +++ b/_static/language_data.js @@ -0,0 +1,199 @@ +/* + * language_data.js + * ~~~~~~~~~~~~~~~~ + * + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer and splitter. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, is available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/_static/minus.png b/_static/minus.png new file mode 100644 index 00000000..d96755fd Binary files /dev/null and b/_static/minus.png differ diff --git a/_static/plus.png b/_static/plus.png new file mode 100644 index 00000000..7107cec9 Binary files /dev/null and b/_static/plus.png differ diff --git a/_static/print.css b/_static/print.css new file mode 100644 index 00000000..bd88769f --- /dev/null +++ b/_static/print.css @@ -0,0 +1,42 @@ +.sy-head { + position: static; + border-bottom: 1px solid var(--sy-c-divider); +} + +.sy-head-inner { + padding: 0; 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.sr{color:var(--syntax-regexp)}.highlight .s1,.highlight .ss{color:var(--syntax-string)}.highlight .bp,.highlight .vc,.highlight .vg,.highlight .vi{color:var(--syntax-variable)} diff --git a/_static/searchtools.js b/_static/searchtools.js new file mode 100644 index 00000000..97d56a74 --- /dev/null +++ b/_static/searchtools.js @@ -0,0 +1,566 @@ +/* + * searchtools.js + * ~~~~~~~~~~~~~~~~ + * + * Sphinx JavaScript utilities for the full-text search. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = docUrlRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = docUrlRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? 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1/0.95)}}}:root{--tomato-9-contrast:#fff;--red-9-contrast:#fff;--ruby-9-contrast:#fff;--crimson-9-contrast:#fff;--pink-9-contrast:#fff;--plum-9-contrast:#fff;--purple-9-contrast:#fff;--violet-9-contrast:#fff;--iris-9-contrast:#fff;--indigo-9-contrast:#fff;--blue-9-contrast:#fff;--cyan-9-contrast:#fff;--teal-9-contrast:#fff;--jade-9-contrast:#fff;--green-9-contrast:#fff;--grass-9-contrast:#fff;--orange-9-contrast:#fff;--brown-9-contrast:#fff;--sky-9-contrast:#1c2024;--mint-9-contrast:#1a211e;--lime-9-contrast:#1d211c;--yellow-9-contrast:#21201c;--amber-9-contrast:#21201c;--gold-9-contrast:#fff;--bronze-9-contrast:#fff;--gray-9-contrast:#fff}.light,.light-theme,:root{--gray-surface:#ffffffcc;--mauve-surface:#ffffffcc;--slate-surface:#ffffffcc;--sage-surface:#ffffffcc;--olive-surface:#ffffffcc;--sand-surface:#ffffffcc;--tomato-surface:#fff6f5cc;--red-surface:#fff5f5cc;--ruby-surface:#fff5f6cc;--crimson-surface:#fef5f8cc;--pink-surface:#fef5facc;--plum-surface:#fdf5fdcc;--purple-surface:#faf5fecc;--violet-surface:#f9f6ffcc;--iris-surface:#f6f6ffcc;--indigo-surface:#f5f8ffcc;--blue-surface:#f1f9ffcc;--cyan-surface:#eff9facc;--teal-surface:#f0faf8cc;--jade-surface:#f1faf5cc;--green-surface:#f1faf4cc;--grass-surface:#f3faf3cc;--brown-surface:#fbf8f4cc;--bronze-surface:#fdf5f3cc;--gold-surface:#f9f8efcc;--sky-surface:#eef9fdcc;--mint-surface:#effaf8cc;--lime-surface:#f6f9f0cc;--yellow-surface:#fefbe4cc;--amber-surface:#fefae4cc;--orange-surface:#fff5e9cc;@supports (color:color(display-p3 1 1 1)){@media (color-gamut:p3){&{--gray-surface:color(display-p3 1 1 1/0.8);--mauve-surface:color(display-p3 1 1 1/0.8);--slate-surface:color(display-p3 1 1 1/0.8);--sage-surface:color(display-p3 1 1 1/0.8);--olive-surface:color(display-p3 1 1 1/0.8);--sand-surface:color(display-p3 1 1 1/0.8);--tomato-surface:color(display-p3 0.9922 0.9647 0.9608/0.8);--red-surface:color(display-p3 0.9961 0.9647 0.9647/0.8);--ruby-surface:color(display-p3 0.9961 0.9647 0.9647/0.8);--crimson-surface:color(display-p3 0.9922 0.9608 0.9725/0.8);--pink-surface:color(display-p3 0.9922 0.9608 0.9804/0.8);--plum-surface:color(display-p3 0.9843 0.9647 0.9843/0.8);--purple-surface:color(display-p3 0.9804 0.9647 0.9922/0.8);--violet-surface:color(display-p3 0.9725 0.9647 0.9961/0.8);--iris-surface:color(display-p3 0.9647 0.9647 0.9961/0.8);--indigo-surface:color(display-p3 0.9647 0.9725 0.9961/0.8);--blue-surface:color(display-p3 0.9529 0.9765 0.9961/0.8);--cyan-surface:color(display-p3 0.9412 0.9765 0.9804/0.8);--teal-surface:color(display-p3 0.9451 0.9804 0.9725/0.8);--jade-surface:color(display-p3 0.9529 0.9804 0.9608/0.8);--green-surface:color(display-p3 0.9569 0.9804 0.9608/0.8);--grass-surface:color(display-p3 0.9569 0.9804 0.9569/0.8);--brown-surface:color(display-p3 0.9843 0.9725 0.9569/0.8);--bronze-surface:color(display-p3 0.9843 0.9608 0.9529/0.8);--gold-surface:color(display-p3 0.9765 0.9725 0.9412/0.8);--sky-surface:color(display-p3 0.9412 0.9765 0.9843/0.8);--mint-surface:color(display-p3 0.9451 0.9804 0.9725/0.8);--lime-surface:color(display-p3 0.9725 0.9765 0.9412/0.8);--yellow-surface:color(display-p3 0.9961 0.9922 0.902/0.8);--amber-surface:color(display-p3 0.9922 0.9843 0.902/0.8);--orange-surface:color(display-p3 0.9961 0.9608 0.9176/0.8)}}}}.dark,.dark-theme{--gray-surface:#21212180;--mauve-surface:#22212380;--slate-surface:#1f212380;--sage-surface:#1e201f80;--olive-surface:#1f201e80;--sand-surface:#21212080;--tomato-surface:#2d191580;--red-surface:#2f151780;--ruby-surface:#2b191d80;--crimson-surface:#2f151f80;--pink-surface:#31132980;--plum-surface:#2f152f80;--purple-surface:#2b173580;--violet-surface:#25193980;--iris-surface:#1d1b3980;--indigo-surface:#171d3b80;--blue-surface:#11213d80;--cyan-surface:#11252d80;--teal-surface:#13272580;--jade-surface:#13271f80;--green-surface:#15251d80;--grass-surface:#19231b80;--brown-surface:#271f1b80;--bronze-surface:#27211d80;--gold-surface:#25231d80;--sky-surface:#13233b80;--mint-surface:#15272780;--lime-surface:#1b211580;--yellow-surface:#231f1380;--amber-surface:#271f1380;--orange-surface:#271d1380;@supports (color:color(display-p3 1 1 1)){@media (color-gamut:p3){&{--gray-surface:color(display-p3 0.1255 0.1255 0.1255/0.5);--mauve-surface:color(display-p3 0.1333 0.1255 0.1333/0.5);--slate-surface:color(display-p3 0.1176 0.1255 0.1333/0.5);--sage-surface:color(display-p3 0.1176 0.1255 0.1176/0.5);--olive-surface:color(display-p3 0.1176 0.1255 0.1176/0.5);--sand-surface:color(display-p3 0.1255 0.1255 0.1255/0.5);--tomato-surface:color(display-p3 0.1569 0.0941 0.0784/0.5);--red-surface:color(display-p3 0.1647 0.0863 0.0863/0.5);--ruby-surface:color(display-p3 0.1569 0.0941 0.1098/0.5);--crimson-surface:color(display-p3 0.1647 0.0863 0.1176/0.5);--pink-surface:color(display-p3 0.1725 0.0784 0.149/0.5);--plum-surface:color(display-p3 0.1647 0.0863 0.1725/0.5);--purple-surface:color(display-p3 0.149 0.0941 0.1961/0.5);--violet-surface:color(display-p3 0.1333 0.102 0.2118/0.5);--iris-surface:color(display-p3 0.1098 0.102 0.2118/0.5);--indigo-surface:color(display-p3 0.0941 0.1098 0.2196/0.5);--blue-surface:color(display-p3 0.0706 0.1255 0.2196/0.5);--cyan-surface:color(display-p3 0.0784 0.1412 0.1725/0.5);--teal-surface:color(display-p3 0.0863 0.149 0.1412/0.5);--jade-surface:color(display-p3 0.0863 0.149 0.1176/0.5);--green-surface:color(display-p3 0.0941 0.1412 0.1098/0.5);--grass-surface:color(display-p3 0.102 0.1333 0.102/0.5);--brown-surface:color(display-p3 0.1412 0.1176 0.102/0.5);--bronze-surface:color(display-p3 0.1412 0.1255 0.1176/0.5);--gold-surface:color(display-p3 0.1412 0.1333 0.1098/0.5);--sky-surface:color(display-p3 0.0863 0.1333 0.2196/0.5);--mint-surface:color(display-p3 0.0941 0.149 0.1412/0.5);--lime-surface:color(display-p3 0.1098 0.1255 0.0784/0.5);--yellow-surface:color(display-p3 0.1333 0.1176 0.0706/0.5);--amber-surface:color(display-p3 0.1412 0.1176 0.0784/0.5);--orange-surface:color(display-p3 0.1412 0.1098 0.0706/0.5)}}}}[data-accent-color=tomato]{--color-surface-accent:var(--tomato-surface);--accent-1:var(--tomato-1);--accent-2:var(--tomato-2);--accent-3:var(--tomato-3);--accent-4:var(--tomato-4);--accent-5:var(--tomato-5);--accent-6:var(--tomato-6);--accent-7:var(--tomato-7);--accent-8:var(--tomato-8);--accent-9:var(--tomato-9);--accent-9-contrast:var(--tomato-9-contrast);--accent-10:var(--tomato-10);--accent-11:var(--tomato-11);--accent-12:var(--tomato-12);--accent-a1:var(--tomato-a1);--accent-a2:var(--tomato-a2);--accent-a3:var(--tomato-a3);--accent-a4:var(--tomato-a4);--accent-a5:var(--tomato-a5);--accent-a6:var(--tomato-a6);--accent-a7:var(--tomato-a7);--accent-a8:var(--tomato-a8);--accent-a9:var(--tomato-a9);--accent-a10:var(--tomato-a10);--accent-a11:var(--tomato-a11);--accent-a12:var(--tomato-a12)}[data-accent-color=red]{--color-surface-accent:var(--red-surface);--accent-1:var(--red-1);--accent-2:var(--red-2);--accent-3:var(--red-3);--accent-4:var(--red-4);--accent-5:var(--red-5);--accent-6:var(--red-6);--accent-7:var(--red-7);--accent-8:var(--red-8);--accent-9:var(--red-9);--accent-9-contrast:var(--red-9-contrast);--accent-10:var(--red-10);--accent-11:var(--red-11);--accent-12:var(--red-12);--accent-a1:var(--red-a1);--accent-a2:var(--red-a2);--accent-a3:var(--red-a3);--accent-a4:var(--red-a4);--accent-a5:var(--red-a5);--accent-a6:var(--red-a6);--accent-a7:var(--red-a7);--accent-a8:var(--red-a8);--accent-a9:var(--red-a9);--accent-a10:var(--red-a10);--accent-a11:var(--red-a11);--accent-a12:var(--red-a12)}[data-accent-color=ruby]{--color-surface-accent:var(--ruby-surface);--accent-1:var(--ruby-1);--accent-2:var(--ruby-2);--accent-3:var(--ruby-3);--accent-4:var(--ruby-4);--accent-5:var(--ruby-5);--accent-6:var(--ruby-6);--accent-7:var(--ruby-7);--accent-8:var(--ruby-8);--accent-9:var(--ruby-9);--accent-9-contrast:var(--ruby-9-contrast);--accent-10:var(--ruby-10);--accent-11:var(--ruby-11);--accent-12:var(--ruby-12);--accent-a1:var(--ruby-a1);--accent-a2:var(--ruby-a2);--accent-a3:var(--ruby-a3);--accent-a4:var(--ruby-a4);--accent-a5:var(--ruby-a5);--accent-a6:var(--ruby-a6);--accent-a7:var(--ruby-a7);--accent-a8:var(--ruby-a8);--accent-a9:var(--ruby-a9);--accent-a10:var(--ruby-a10);--accent-a11:var(--ruby-a11);--accent-a12:var(--ruby-a12)}[data-accent-color=crimson]{--color-surface-accent:var(--crimson-surface);--accent-1:var(--crimson-1);--accent-2:var(--crimson-2);--accent-3:var(--crimson-3);--accent-4:var(--crimson-4);--accent-5:var(--crimson-5);--accent-6:var(--crimson-6);--accent-7:var(--crimson-7);--accent-8:var(--crimson-8);--accent-9:var(--crimson-9);--accent-9-contrast:var(--crimson-9-contrast);--accent-10:var(--crimson-10);--accent-11:var(--crimson-11);--accent-12:var(--crimson-12);--accent-a1:var(--crimson-a1);--accent-a2:var(--crimson-a2);--accent-a3:var(--crimson-a3);--accent-a4:var(--crimson-a4);--accent-a5:var(--crimson-a5);--accent-a6:var(--crimson-a6);--accent-a7:var(--crimson-a7);--accent-a8:var(--crimson-a8);--accent-a9:var(--crimson-a9);--accent-a10:var(--crimson-a10);--accent-a11:var(--crimson-a11);--accent-a12:var(--crimson-a12)}[data-accent-color=pink]{--color-surface-accent:var(--pink-surface);--accent-1:var(--pink-1);--accent-2:var(--pink-2);--accent-3:var(--pink-3);--accent-4:var(--pink-4);--accent-5:var(--pink-5);--accent-6:var(--pink-6);--accent-7:var(--pink-7);--accent-8:var(--pink-8);--accent-9:var(--pink-9);--accent-9-contrast:var(--pink-9-contrast);--accent-10:var(--pink-10);--accent-11:var(--pink-11);--accent-12:var(--pink-12);--accent-a1:var(--pink-a1);--accent-a2:var(--pink-a2);--accent-a3:var(--pink-a3);--accent-a4:var(--pink-a4);--accent-a5:var(--pink-a5);--accent-a6:var(--pink-a6);--accent-a7:var(--pink-a7);--accent-a8:var(--pink-a8);--accent-a9:var(--pink-a9);--accent-a10:var(--pink-a10);--accent-a11:var(--pink-a11);--accent-a12:var(--pink-a12)}[data-accent-color=plum]{--color-surface-accent:var(--plum-surface);--accent-1:var(--plum-1);--accent-2:var(--plum-2);--accent-3:var(--plum-3);--accent-4:var(--plum-4);--accent-5:var(--plum-5);--accent-6:var(--plum-6);--accent-7:var(--plum-7);--accent-8:var(--plum-8);--accent-9:var(--plum-9);--accent-9-contrast:var(--plum-9-contrast);--accent-10:var(--plum-10);--accent-11:var(--plum-11);--accent-12:var(--plum-12);--accent-a1:var(--plum-a1);--accent-a2:var(--plum-a2);--accent-a3:var(--plum-a3);--accent-a4:var(--plum-a4);--accent-a5:var(--plum-a5);--accent-a6:var(--plum-a6);--accent-a7:var(--plum-a7);--accent-a8:var(--plum-a8);--accent-a9:var(--plum-a9);--accent-a10:var(--plum-a10);--accent-a11:var(--plum-a11);--accent-a12:var(--plum-a12)}[data-accent-color=purple]{--color-surface-accent:var(--purple-surface);--accent-1:var(--purple-1);--accent-2:var(--purple-2);--accent-3:var(--purple-3);--accent-4:var(--purple-4);--accent-5:var(--purple-5);--accent-6:var(--purple-6);--accent-7:var(--purple-7);--accent-8:var(--purple-8);--accent-9:var(--purple-9);--accent-9-contrast:var(--purple-9-contrast);--accent-10:var(--purple-10);--acce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' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(SphinxHighlight.highlightSearchWords); +_ready(SphinxHighlight.initEscapeListener); diff --git a/architecture.html b/architecture.html new file mode 100644 index 00000000..021a6e31 --- /dev/null +++ b/architecture.html @@ -0,0 +1,271 @@ + + + + + Arroyo Architecture - Arroyo documentation + + + + + + + + + + +
+
+
+ + + + Arroyo + +
+ +
+ + / +
+
+
+
+
+
+
+ + + + +
+
+
+
+

Arroyo Architecture

+

Arroyo is a set of high level abstractions to interact with Kafka. +These are meant to help the developer in writing performant consumers with +specific delivery guarantees.

+

Common problems addressed by Arroyo are guaranteeing at-least-once delivery, +providing a dead letter queue abstraction, support parallel (multi-processing) +message processing, etc.

+

The library is divided into three layers: the basic Kafka connectivity, the +streaming engine and the high level abstractions.

+

The basic connectivity layer is a simple wrapper around the Confluent python +library, which is itself based on librdkafka. Besides some cosmetic changes, +this level provides a Fake in memory broker and consumer to make unit test quick +to run.

+

The streaming engine provides an asynchronous processing interface to write +consumers. The consumer is written as a pipeline where each segment is an +asynchronous operation. The streaming engine implements the main consumer loop +and delegates the processing to the pipeline.

+

On top of the streaming engine, the library provides high-level abstractions that +are common when writing Kafka consumers like: map, reduce, filter together +with some common messaging application patterns like the dead letter queue.

+
+

Streaming Interface and Streaming Engine

+

A Kafka consumer is built as a pipeline where each segment processes messages in +an asynchronous way. The Streaming engine provides a message to a segment. The +segment is not supposed to execute small CPU work in a blocking way or do IO in a +non-blocking way. We generally use futures for this, and heavier CPU work in a +separate process.

+

Arroyo provides an interface to implement to write a pipeline segment. +The segment interface is called ProcessingStrategy and is in +this module. +(TODO: bring the docstrings to the docs and reference that).

+

In most cases, when developing a consumer, the developer would not implement +that interface directly. A higher level abstraction would be used.

+
+_images/arroyo_processing.png +
+

The main consumer loop is managed by the stream engine. +These are the phases:

+
    +

  • Poll from the Kafka consumer through the basic library. If a message is there +proceed or repeat.

    • +

  • Submit the message to the first ProcessingStrategy. This is supposed to deliver +work for the strategy to do. It is not supposed to be a blocking operation. The +strategy should return immediately.

    • +

  • Poll the strategy to execute work or to forward results to the following step +in the pipeline. Ideally all IO should be done in separate threads and heavy cpu +work should be done in separate processes so the poll method should check for +completed work, dispatch to the next step and return. In practice, work is executed +here in a blocking way if the overhead of offloading the work is too high.

    • +
+

The ProcessingStrategy may decide not to take the message and instead apply back-pressure. +This is done by raising the MessageRejected exception. In this case, the streaming +engine pauses the consumer till the strategy is ready to accept the message.

+

The ProcessingStrategy decides when it is time to commit a message. This is done +through a commit callback provided to the strategy when it is instantiated.

+

The streaming engine orchestrates the life cycle of the ProcessingStrategy, thus +when it thinks it is time to shut the strategy down it would wait for all in-flight +work to be completed and then destroy the strategy.

+

There are two scenarios where this can happen:

+
    +

  • The consumer is being terminated.

    • +

  • A rebalancing happened. A rebalancing revokes partitions and assigns new ones. +After a rebalancing is complete it is impossible to commit a message from a partition +that was revoked. In order to ensure the consumer behaves in a consistent way, +upon rebalancing, the streaming engine destroys the strategy and builds a new one. +This allows the strategy to complete all in-flight work before being terminated.

    • +
+
+
+

High level strategies

+

Most consumers follow the same few patterns, so Arroyo provides abstractions that +are based on the ProcessingStrategy but are simpler to implement for the common +use cases.

+

Common examples are:

+
    +

  • run task, run task in threads, run task with multiprocessing. The run task +set of strategies are designed to be the most flexible and simple to use. They take +a function provided by the user and execute it on every message, passing the output +to the next step. The library includes synchronous and asynchronous versions depending +on the kind of concurrency required by the user.

    • +

  • filter, map and forward. This type of consumer inspects a message, decides +whether to process it or discard it, transforms its content, and produces the result +on a new topic. In this case, Arroyo provides three implementations of the +ProcessingStrategy: filter, transform, and produce. The developer only needs +to wire them together and provide the map and filtering logic.

    • +

  • consume, apply side effects, produce. This is a variation of the one above. +In this case, the transform operation can have side-effects like storing the content +of the message somewhere.

    • +

  • high throughput cpu intensive transform. The python GIL does not allow CPU intensive +work to take advantage of parallelism. Arroyo provides an implementation of the map +pattern that batches messages and dispatches the work to separate processes via shared +memory. This is largely transparent to the developers.

    • +

  • map, reduce and store. The reduce function is carried out by the Collector, which +batches messages and executes some logic with side-effects when the batch is full. +This is a typical way to write messages on a storages in batches to reduce the +round trips.

    • +
+

All strategies included with Arroyo are in the strategies module.

+
+
+ +
+ + +
+ Previous
Getting started with Arroyo
+ +
+ +
+ Next +
Processing Strategies
+
+ + +
+
+
+
+
+
+
+

2023, Sentry Team and Contributors

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/backpressure.html b/backpressure.html new file mode 100644 index 00000000..62964899 --- /dev/null +++ b/backpressure.html @@ -0,0 +1,203 @@ + + + + + Backpressure - Arroyo documentation + + + + + + + + + + +
+
+
+ + + + Arroyo + +
+ +
+ + / +
+
+
+
+
+
+
+ + + + +
+
+
+
+

Backpressure

+

Arroyo’s own processing strategies internally apply backpressure by raising +MessageRejected. Most +consumers do not require additional work to deal with backpressure correctly.

+

If you want to slow down the consumer based on some external signal or +condition, you can achieve that most effectively by raising the same exception +from within a callback passed to RunTask while the +consumer is supposed to be paused

+
class ConsumerStrategyFactory(ProcessingStrategyFactory[KafkaPayload]):
+    def __init__(self):
+        self.is_paused = False
+
+    def create_with_partitions(
+        self,
+        commit: Commit,
+        partitions: Mapping[Partition, int],
+    ) -> ProcessingStrategy[KafkaPayload]:
+        def handle_message(message: Message[KafkaPayload]) -> Message[KafkaPayload]:
+            if self.is_paused:
+                raise MessageRejected()
+
+            print(f"MSG: {message.payload}")
+            return message
+
+        return RunTask(handle_message, CommitOffsets(commit))
+
+
+

It is not recommended to apply backpressure by just sleep()-ing in +submit (or, in this example, +handle_message) for more than a few milliseconds. While this definitely +pauses the consumer, it will block the main thread for too long and and prevent +things like consumer rebalancing from occuring.

+

A 0.01 second sleep is applied each time MessageRejected is +raised to prevent the main thread spinning at 100% CPU. However background thread +performance may be impacted during this time.

+
+ +
+ + +
+ Previous
Dead letter queues
+ +
+ +
+ Next +
Metrics
+
+ + +
+
+
+
+
+
+
+

2023, Sentry Team and Contributors

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/dlqs.html b/dlqs.html new file mode 100644 index 00000000..2ce333a7 --- /dev/null +++ b/dlqs.html @@ -0,0 +1,233 @@ + + + + + Dead letter queues - Arroyo documentation + + + + + + + + + + +
+
+
+ + + + Arroyo + +
+ +
+ + / +
+
+
+
+
+
+
+ + + + +
+
+
+
+

Dead letter queues

+
+

Warning

+

Dead letter queues should be used with caution as they break some of the ordering guarantees +otherwise offered by Arroyo and Kafka consumer code. In particular, it must be safe for the +consumer to drop a message. If replaying or later re-processing of the DLQ’ed messages is done, +it is critical that ordering is not a requirement in the relevant downstream code.

+
+

Arroyo provides support for routing invalid messages to dead letter queues in consumers. +Dead letter queues are critical in some applications because messages are ordered in Kafka +and a single invalid message can cause a consumer to crash and every subsequent message to +not be processed.

+

The dead letter queue configuration is passed to the StreamProcessor and, if provided, any +InvalidMessage raise by a strategy will be produced to the dead letter queue.

+
+
+class arroyo.dlq.DlqLimit(max_invalid_ratio: float | None = None, max_consecutive_count: int | None = None)
+

Defines any limits that should be placed on the number of messages that are +forwarded to the DLQ. This exists to prevent 100% of messages from going into +the DLQ if something is misconfigured or bad code is deployed. In this scenario, +it may be preferable to stop processing messages altogether and deploy a fix +rather than rerouting every message to the DLQ.

+

The ratio and max_consecutive_count are counted on a per-partition basis.

+
+ +
+
+class arroyo.dlq.DlqPolicy(producer: DlqProducer[TStrategyPayload], limit: DlqLimit | None = None, max_buffered_messages_per_partition: int | None = None)
+

DLQ policy defines the DLQ configuration, and is passed to the stream processor +upon creation of the consumer. It consists of the DLQ producer implementation and +any limits that should be applied.

+
+ +
+
+exception arroyo.dlq.InvalidMessage(partition: Partition, offset: int, needs_commit: bool = True)
+

InvalidMessage should be raised if a message is not valid for processing and +should not be retried. It will be placed a DLQ if one is configured.

+

It can be raised from the submit, poll or join methods of any processing strategy.

+

Once a filtered message is forwarded to the next step, needs_commit should be set to False, +in order to prevent multiple filtered messages from being forwarded for a single invalid message.

+
+ +
+
+class arroyo.dlq.KafkaDlqProducer(producer: Producer[KafkaPayload], topic: Topic)
+

KafkaDLQProducer forwards invalid messages to a Kafka topic

+

Two additional fields are added to the headers of the Kafka message +“original_partition”: The partition of the original message +“original_offset”: The offset of the original message

+
+ +
+
+class arroyo.dlq.NoopDlqProducer(*args, **kwds)
+

Drops all invalid messages

+
+ +
+ +
+ + +
+ Previous
Committing offsets
+ +
+ +
+ Next +
Backpressure
+
+ + +
+
+
+
+
+
+
+

2023, Sentry Team and Contributors

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/genindex.html b/genindex.html new file mode 100644 index 00000000..15713a1d --- /dev/null +++ b/genindex.html @@ -0,0 +1,532 @@ + + + + + Index - Arroyo documentation + + + + + + + + +
+
+
+ + + + Arroyo + +
+ +
+ + / +
+
+
+
+
+
+
+
+ + +

Index

+ +
+ A + | B + | C + | D + | F + | G + | H + | I + | J + | K + | M + | N + | O + | P + | R + | S + | T + | U + | V + +
+

A

+ + + +
    +
  • + arroyo.dlq + +
    • +
  • + arroyo.processing.strategies.abstract + +
    • +
  • + arroyo.processing.strategies.batching + +
    • +
  • + arroyo.processing.strategies.commit + +
    • +
  • + arroyo.processing.strategies.filter + +
    • +
  • + arroyo.processing.strategies.healthcheck + +
    • +
  • + arroyo.processing.strategies.noop + +
    • +
  • + arroyo.processing.strategies.produce + +
    • +
    +
  • + arroyo.processing.strategies.reduce + +
    • +
  • + arroyo.processing.strategies.run_task + +
    • +
  • + arroyo.processing.strategies.run_task_in_threads + +
    • +
  • + arroyo.processing.strategies.run_task_with_multiprocessing + +
    • +
  • + arroyo.processing.strategies.unfold + +
    • +
  • + arroyo.types + +
    • +
  • + arroyo.utils.metrics + +
    • +
+ +

B

+ + + +
+ +

C

+ + + +
+ +

D

+ + + +
+ +

F

+ + + +
+ +

G

+ + +
+ +

H

+ + +
+ +

I

+ + + +
+ +

J

+ + +
+ +

K

+ + +
+ +

M

+ + +
+ +

N

+ + + +
+ +

O

+ + +
+ +

P

+ + + +
+ +

R

+ + + +
+ +

S

+ + + +
+ +

T

+ + + +
+ +

U

+ + + +
+ +

V

+ + + +
+ + + +
+ +
+
+
+
+

2023, Sentry Team and Contributors

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/getstarted.html b/getstarted.html new file mode 100644 index 00000000..af0e6014 --- /dev/null +++ b/getstarted.html @@ -0,0 +1,397 @@ + + + + + Getting started with Arroyo - Arroyo documentation + + + + + + + + + + +
+
+
+ + + + Arroyo + +
+ +
+ + / +
+
+
+
+
+
+
+ + + + +
+
+
+
+

Getting started with Arroyo

+

This tutorial shows how to create a Kafka consumer with Arroyo from scratch.

+
+

Setup

+

This section explains how to setup Kafka, Zookeeper and install the library

+
+

Kafka and Zookeeper

+

In order to run an arroyo Kafka consumer you will need a working Kafka broker. +If you already have one, you can skip this step. +If you do not have a running Kafka broker, this command will install and start +a Kafka docker container. (It requires Docker to be installed).

+
docker network create arroyo
+
+docker run --rm \
+    -v zookeeper_volume:/var/lib/zookeeper \
+    --env ZOOKEEPER_CLIENT_PORT=2181 \
+    --name=sentry_zookeeper \
+    --network=arroyo \
+    -p 2181:2181 \
+    confluentinc/cp-zookeeper:6.2.0
+
+docker run --rm \
+    -v kafka_volume:/var/lib/kafka \
+    --env KAFKA_ZOOKEEPER_CONNECT=sentry_zookeeper:2181 \
+    --env KAFKA_LISTENERS=INTERNAL://0.0.0.0:9093,EXTERNAL://0.0.0.0:9092 \
+    --env KAFKA_ADVERTISED_LISTENERS=INTERNAL://127.0.0.1:9093,EXTERNAL://127.0.0.1:9092 \
+    --env KAFKA_LISTENER_SECURITY_PROTOCOL_MAP=INTERNAL:PLAINTEXT,EXTERNAL:PLAINTEXT \
+    --env KAFKA_INTER_BROKER_LISTENER_NAME=INTERNAL \
+    --env KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR=1 \
+    --env CONFLUENT_SUPPORT_METRICS_ENABLE=false \
+    --env KAFKA_LOG4J_LOGGERS=kafka.cluster=WARN,kafka.controller=WARN,kafka.coordinator=WARN,kafka.log=WARN,kafka.server=WARN,kafka.zookeeper=WARN,state.change.logger=WARN \
+    --env KAFKA_LOG4J_ROOT_LOGLEVEL=WARN \
+    --env KAFKA_TOOLS_LOG4J_LOGLEVEL=WARN \
+    --name=sentry_kafka \
+    --network=arroyo \
+    -p 9092:9092 \
+    confluentinc/cp-kafka:6.2.0
+
+
+

Now you should see Kafka and Zookeeper running with

+
docker ps
+
+
+
+
+

Install Kafkacat

+

This tool will be useful to produce onto and consume from topics.

+
https://docs.confluent.io/platform/current/app-development/kafkacat-usage.html#kcat-formerly-kafkacat-utility
+
+
+
+
+

Development environment

+

You will need to install the library. Most likely in a python venv. So first, create a python virtual +environment. Then you can install arroyo with this.

+
pip install sentry-arroyo
+
+
+
+
+

Create two Kafka topics

+

Our example will consume from one topic and produce the same messages on another topic. So we need +two topics.

+
docker exec sentry_kafka kafka-topics \
+    --create \
+    --topic source-topic \
+    --bootstrap-server 127.0.0.1:9092
+
+docker exec sentry_kafka kafka-topics \
+    --create \
+    --topic dest-topic \
+    --bootstrap-server 127.0.0.1:9092
+
+
+

Now you should be ready to develop with Arroyo.

+
+
+
+

Create a basic consumer

+

Arroyo provides two level of abstractions when writing a consumer: the basic consumer/producer library +and the Streaming library. The first is just a thin wrapper around a librdkafka consumer/producer that +adds some features around offset management. The second provides a more abstract streaming interface +that hides details like rebalancing and the consumer lifecycle.

+
+

Creating a basic consumer

+

This initializes a basic consumer and consumes a message.

+
from arroyo.backends.kafka.configuration import (
+    build_kafka_consumer_configuration,
+)
+from arroyo.backends.kafka.consumer import KafkaConsumer
+from arroyo.types import Topic
+
+TOPIC = Topic("source-topic")
+
+consumer = KafkaConsumer(
+    build_kafka_consumer_configuration(
+        default_config={},
+        bootstrap_servers=["127.0.0.1:9092"],
+        auto_offset_reset="latest",
+        group_id="test-group",
+    )
+)
+
+consumer.subscribe([TOPIC])
+
+while True:
+    msg = consumer.poll(timeout=1.0)
+    if msg is not None:
+        print(f"MSG: {msg.payload}")
+
+
+

Start this script and use kcat to produce a message:

+
echo "MESSAGE" | kcat -P -b 127.0.0.1:9092 -t source-topic
+
+
+

In a while the message should appear on the console:

+
MSG: KafkaPayload(key=None, value=b'MESSAGE', headers=[])
+
+
+
+
+

Create a streaming consumer

+

Add a ProcessingStrategy and ProcessingStrategyFactory. +Here we are using the RunTask strategy which runs a custom function over each message.

+
from typing import Mapping
+
+from arroyo.backends.kafka import KafkaPayload
+from arroyo.processing.strategies import (
+    CommitOffsets,
+    ProcessingStrategy,
+    ProcessingStrategyFactory,
+    RunTask,
+)
+from arroyo.types import Commit, Message, Partition, Topic
+
+
+def handle_message(message: Message[KafkaPayload]) -> Message[KafkaPayload]:
+    print(f"MSG: {message.payload}")
+    return message.payload
+
+class ConsumerStrategyFactory(ProcessingStrategyFactory[KafkaPayload]):
+    """
+    The factory manages the lifecycle of the `ProcessingStrategy`.
+    A strategy is created every time new partitions are assigned to the
+    consumer, while it is destroyed when partitions are revoked or the
+    consumer is closed
+    """
+    def create_with_partitions(
+        self,
+        commit: Commit,
+        partitions: Mapping[Partition, int],
+    ) -> ProcessingStrategy[KafkaPayload]:
+        return RunTask(handle_message, CommitOffsets(commit))
+
+
+

The code above is orchestrated by the Arroyo runtime called StreamProcessor.

+
from arroyo.processing import StreamProcessor
+from arroyo.commit import ONCE_PER_SECOND
+
+processor = StreamProcessor(
+    consumer=consumer,
+    topic=TOPIC,
+    processor_factory=ConsumerStrategyFactory(),
+    commit_policy=ONCE_PER_SECOND,
+)
+
+processor.run()
+
+
+

The main consumer loop is managed by the StreamProcessor no need to periodically poll the +consumer. The ConsumerStrategy works by inversion of control.

+
+
+

Add some useful logic

+

Now we will chain the Produce strategy to produce messages on a second topic after the message is logged

+
from arroyo.backends.kafka import KafkaProducer
+from arroyo.backends.kafka.configuration import build_kafka_configuration
+from arroyo.processing.strategies import Produce
+
+class ConsumerStrategyFactory(ProcessingStrategyFactory[KafkaPayload]):
+    """
+    The factory manages the lifecycle of the `ProcessingStrategy`.
+    A strategy is created every time new partitions are assigned to the
+    consumer, while it is destroyed when partitions are revoked or the
+    consumer is closed
+    """
+    def create_with_partitions(
+        self,
+        commit: Commit,
+        partitions: Mapping[Partition, int],
+    ) -> ProcessingStrategy[KafkaPayload]:
+        producer = KafkaProducer(
+            build_kafka_configuration(
+                default_config={},
+                bootstrap_servers=BOOTSTRAP_SERVERS,
+            )
+        )
+
+        return RunTask(
+            handle_message,
+            Produce(producer, Topic("dest-topic"), CommitOffsets(commit))
+        )
+
+
+

The message is first passed to the RunTask strategy which simply logs the message and submits +the output to the next step. The Produce strategy produces the message asynchronously. Once +the message is produced, the CommitOffsets strategy commits the offset of the message.

+
+
+
+

Further examples

+

Find some complete examples of usage.

+
+
+ +
+ + +
+ Previous
What is Arroyo for?
+ +
+ +
+ Next +
Arroyo Architecture
+
+ + +
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+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
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+ + + + + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 00000000..b7f1c963 --- /dev/null +++ b/index.html @@ -0,0 +1,186 @@ + + + + + Arroyo documentation + + + + + + + + + + +
+
+
+ + + + Arroyo + +
+ +
+ + / +
+
+
+
+
+
+
+ + + + +
+
+
+ _images/arroyo-banner.png +

Arroyo is a library to build streaming applications that consume from +and produce to Kafka.

+

It relies on the confluent_kafka python library, which itself relies +on librdkafka.

+

Arroyo provides mainly three functionalities:

+
    +

  • A set of abstractions inspired to common messaging applications patterns.

    • +

  • Some abstractions to simplify offset management and rebalancing.

    • +

  • An in memory broker abstraction to simplify writing unit tests.

    • +
+
+

Contents:

+
+ +
+
+ +
+ +
+ Next +
What is Arroyo for?
+
+ + +
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+
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+
+

2023, Sentry Team and Contributors

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
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+ + + + + + + \ No newline at end of file diff --git a/intro.html b/intro.html new file mode 100644 index 00000000..16d58a53 --- /dev/null +++ b/intro.html @@ -0,0 +1,163 @@ + + + + + <no title> - Arroyo documentation + + + + + + + + + + +
+
+
+ + + + Arroyo + +
+ +
+ + / +
+
+
+
+
+
+
+ + + + +
+
+
+ _images/arroyo-banner.png +

Arroyo is a library to build streaming applications that consume from +and produce to Kafka.

+

It relies on the confluent_kafka python library, which itself relies +on librdkafka.

+

Arroyo provides mainly three functionalities:

+
    +

  • A set of abstractions inspired to common messaging applications patterns.

    • +

  • Some abstractions to simplify offset management and rebalancing.

    • +

  • An in memory broker abstraction to simplify writing unit tests.

    • +
+ +
+
+
+
+
+
+
+

2023, Sentry Team and Contributors

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/metrics.html b/metrics.html new file mode 100644 index 00000000..a7f9926d --- /dev/null +++ b/metrics.html @@ -0,0 +1,354 @@ + + + + + Metrics - Arroyo documentation + + + + + + + + + + +
+
+
+ + + + Arroyo + +
+ +
+ + / +
+
+
+
+
+
+
+ + + + +
+
+
+
+

Metrics

+

Arroyo consumers and strategies attempt to auto instrument some metrics that most people find useful +to understand the behavior and performance of their consumers. These metrics are typically sampled or +buffered as appropriate and flushed periodically (often once per second).

+

In order to use these metrics, you must configure a metrics backend that conforms to the metrics protocol +before creating your consumer.

+

This can be done like so:

+
from arroyo.utils.metrics import Metrics, MetricName
+
+ class MyMetrics(Metrics):
+     def increment(
+         self, name: MetricName, value: Union[int, float] = 1, tags: Optional[Tags] = None
+     ) -> None:
+         # Increment a counter by the given value.
+         record_incr(name, value, tags)
+
+     def gauge(
+         self, name: MetricName, value: Union[int, float], tags: Optional[Tags] = None
+     ) -> None:
+         # Sets a gauge metric to the given value.
+         record_gauge(name, value, tags)
+
+     def timing(
+         self, name: MetricName, value: Union[int, float], tags: Optional[Tags] = None
+     ) -> None:
+         # Emit a timing metric with the given value.
+         record_timing(name, value, tags)
+
+ metrics_backend = MyMetrics()
+
+ configure_metrics(metrics_backend)
+
+
+
+

Available Metrics

+
from typing import Literal
+
+MetricName = Literal[
+    # Time: Number of messages in a multiprocessing batch
+    "arroyo.strategies.run_task_with_multiprocessing.batch.size.msg",
+    # Time: Number of bytes in a multiprocessing batch
+    "arroyo.strategies.run_task_with_multiprocessing.batch.size.bytes",
+    # Time: Number of messages in a multiprocessing batch after the message transformation
+    "arroyo.strategies.run_task_with_multiprocessing.output_batch.size.msg",
+    # Time: Number of bytes in a multiprocessing batch after the message transformation
+    "arroyo.strategies.run_task_with_multiprocessing.output_batch.size.bytes",
+    # Counter: Number of times the consumer is spinning
+    "arroyo.consumer.run.count",
+    # Counter: Number of times the consumer encountered an invalid message.
+    "arroyo.consumer.invalid_message.count",
+    # Time: How long it took the Reduce step to fill up a batch
+    "arroyo.strategies.reduce.batch_time",
+    # Counter: Incremented when a strategy after multiprocessing applies
+    # backpressure to multiprocessing. May be a reason why CPU cannot be
+    # saturated.
+    "arroyo.strategies.run_task_with_multiprocessing.batch.backpressure",
+    # Counter: Incremented when multiprocessing cannot fill the input batch
+    # because not enough memory was allocated. This results in batches smaller
+    # than configured. Increase `input_block_size` to fix.
+    "arroyo.strategies.run_task_with_multiprocessing.batch.input.overflow",
+    # Counter: Incremented when multiprocessing cannot pull results in batches
+    # equal to the input batch size, because not enough memory was allocated.
+    # This can be devastating for throughput. Increase `output_block_size` to
+    # fix.
+    "arroyo.strategies.run_task_with_multiprocessing.batch.output.overflow",
+    # Counter: Arroyo has decided to re-allocate a block in order to combat input
+    # buffer overflow. This behavior can be disabled by explicitly setting
+    # `input_block_size` to a not-None value in `RunTaskWithMultiprocessing`.
+    "arroyo.strategies.run_task_with_multiprocessing.batch.input.resize",
+    # Counter: Arroyo has decided to re-allocate a block in order to combat output
+    # buffer overflow. This behavior can be disabled by explicitly setting
+    # `output_block_size` to a not-None value in `RunTaskWithMultiprocessing`.
+    "arroyo.strategies.run_task_with_multiprocessing.batch.output.resize",
+    # Gauge: How many batches are being processed in parallel by multiprocessing.
+    "arroyo.strategies.run_task_with_multiprocessing.batches_in_progress",
+    # Counter: A subprocess by multiprocessing unexpectedly died.
+    "sigchld.detected",
+    # Gauge: Shows how many processes the multiprocessing strategy is
+    # configured with.
+    "arroyo.strategies.run_task_with_multiprocessing.processes",
+    # Counter: Incremented when the multiprocessing pool is created (or re-created).
+    "arroyo.strategies.run_task_with_multiprocessing.pool.create",
+    # Time: (unitless) spent polling librdkafka for new messages.
+    "arroyo.consumer.poll.time",
+    # Time: (unitless) spent in strategies (blocking in strategy.submit or
+    # strategy.poll)
+    "arroyo.consumer.processing.time",
+    # Time: (unitless) spent pausing the consumer due to backpressure (MessageRejected)
+    "arroyo.consumer.backpressure.time",
+    # Time: (unitless) spent in handling `InvalidMessage` exceptions and sending
+    # messages to the the DLQ.
+    "arroyo.consumer.dlq.time",
+    # Time: (unitless) spent in waiting for the strategy to exit, such as during
+    # shutdown or rebalancing.
+    "arroyo.consumer.join.time",
+    # Time: (unitless) spent in librdkafka callbacks. This metric's timings
+    # overlap other timings, and might spike at the same time.
+    "arroyo.consumer.callback.time",
+    # Time: (unitless) spent in shutting down the consumer. This metric's
+    # timings overlap other timings, and might spike at the same time.
+    "arroyo.consumer.shutdown.time",
+    # Time: A regular duration metric where each datapoint is measuring the time it
+    # took to execute a single callback. This metric is distinct from the
+    # arroyo.consumer.*.time metrics as it does not attempt to accumulate time
+    # spent per second in an attempt to keep monitoring overhead low.
+    #
+    # The metric is tagged by the name of the internal callback function being
+    # executed, as 'callback_name'. Possible values are on_partitions_assigned
+    # and on_partitions_revoked.
+    "arroyo.consumer.run.callback",
+    # Time: Duration metric measuring the time it took to flush in-flight messages
+    # and shut down the strategies.
+    "arroyo.consumer.run.close_strategy",
+    # Time: Duration metric measuring the time it took to create the processing strategy.
+    "arroyo.consumer.run.create_strategy",
+    # Counter: How many partitions have been revoked just now.
+    "arroyo.consumer.partitions_revoked.count",
+    # Counter: How many partitions have been assigned just now.
+    "arroyo.consumer.partitions_assigned.count",
+    # Time: Consumer latency in seconds. Recorded by the commit offsets strategy.
+    "arroyo.consumer.latency",
+    # Counter: Metric for when the underlying rdkafka consumer is being paused.
+    #
+    # This flushes internal prefetch buffers.
+    "arroyo.consumer.pause",
+    # Counter: Metric for when the underlying rdkafka consumer is being resumed.
+    #
+    # This might cause increased network usage as messages are being re-fetched.
+    "arroyo.consumer.resume",
+    # Gauge: Queue size of background queue that librdkafka uses to prefetch messages.
+    "arroyo.consumer.librdkafka.total_queue_size",
+    # Counter: Counter metric to measure how often the healthcheck file has been touched.
+    "arroyo.processing.strategies.healthcheck.touch",
+    # Counter: Number of messages dropped in the FilterStep strategy
+    "arroyo.strategies.filter.dropped_messages",
+]
+
+
+

For convenience Arroyo includes a machine readable version which can be loaded like:

+
import importlib.resources
+import json
+
+with importlib.resources.files("arroyo.utils").joinpath("metricDefs.json").open() as f:
+    metric_defs = json.load(f)
+
+
+
+
+

API

+
+
+class arroyo.utils.metrics.Metrics(*args, **kwargs)
+

An abstract class that defines the interface for metrics backends.

+
+
+abstract gauge(name: MetricName, value: int | float, tags: Mapping[str, str] | None = None) → None
+

Sets a gauge metric to the given value.

+
+ +
+
+abstract increment(name: MetricName, value: int | float = 1, tags: Mapping[str, str] | None = None) → None
+

Increments a counter metric by a given value.

+
+ +
+
+abstract timing(name: MetricName, value: int | float, tags: Mapping[str, str] | None = None) → None
+

Records a timing metric.

+
+ +
+ +
+
+arroyo.utils.metrics.configure_metrics(metrics: Metrics, force: bool = False) → None
+

Metrics can generally only be configured once, unless force is passed +on subsequent initializations.

+
+ +
+
+ +
+ + +
+ Previous
Backpressure
+ +
+
+
+
+
+
+
+

2023, Sentry Team and Contributors

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
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+ + + + + + + \ No newline at end of file diff --git a/objects.inv b/objects.inv new file mode 100644 index 00000000..35b2a96d Binary files /dev/null and b/objects.inv differ diff --git a/offsets.html b/offsets.html new file mode 100644 index 00000000..313783cc --- /dev/null +++ b/offsets.html @@ -0,0 +1,197 @@ + + + + + Committing offsets - Arroyo documentation + + + + + + + + + + +
+
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+ + + + Arroyo + +
+ +
+ + / +
+
+
+
+
+
+
+ + + + +
+
+
+
+

Committing offsets

+

Arroyo does not auto commit offsets. It is up to you to manually commit offsets when processing for that +message is completed.

+

The commit callback will be passed to processing strategy via ProcessingStrategyFactory.create_with_partitions. +You should pass this to the strategy and have your strategy call this commit function once the rest of the message +processing has been done.

+

The offset to be committed in Kafka is always the next offset to be consumed from, i.e. message’s offset + 1. +In Arroyo, this means you should commit Message.next_offset and never Message.offset when done processing +that message. Arroyo exposes Message.position_to_commit to make this easier.

+

It is not safe to commit every offset in a high throughput consumer as this will add a lot of load to the system. +Commits should generally be throttled. CommitPolicy is the Arroyo way of specifying commit frequency. A CommitPolicy +must be passed to the stream processor, which allows specifying a minimum commit frequency (or messages between commits). +Commit throttling can be skipped when needed (i.e. during consumer shutdown) by passing force=True to the commit callback. +If you are not sure how often to commit, ONCE_PER_SECOND is a reasonable option.

+

The easiest way is to use the CommitOffsets strategy as the last step in a chain of processing strategies to commit offsets.

+
class MyConsumerFactoryFactory(ProcessingStrategyFactory[KafkaPayload]):
+    def create_with_partitions(
+        self,
+        commit: Commit,
+        partitions: Mapping[Partition, int],
+    ) -> ProcessingStrategy[KafkaPayload]:
+        def my_processing_function(message: Message[KafkaPayload]) -> None:
+            # do something (synchronous) with the message
+            do_something()
+
+
+        return RunTask(my_processing_function, CommitOffsets(commit))
+
+
+
+ +
+ + +
+ Previous
Healthchecks
+ +
+ +
+ Next +
Dead letter queues
+
+ + +
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+
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+
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+
+

2023, Sentry Team and Contributors

+ +

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+ + +

Python Module Index

+ +
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    + arroyo.processing.strategies.filter +
    + arroyo.processing.strategies.healthcheck +
    + arroyo.processing.strategies.noop +
    + arroyo.processing.strategies.produce +
    + arroyo.processing.strategies.reduce +
    + arroyo.processing.strategies.run_task +
    + arroyo.processing.strategies.run_task_in_threads +
    + arroyo.processing.strategies.run_task_with_multiprocessing +
    + arroyo.processing.strategies.unfold +
    + arroyo.types +
    + arroyo.utils.metrics +
+ + +
+ +
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+
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+

2023, Sentry Team and Contributors

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

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"strategies/run_task.rst", "strategies/run_task_in_threads.rst", "strategies/run_task_with_multiprocessing.rst", "strategies/unfold.rst", "what_for.rst"], "titles": ["Arroyo Architecture", "Backpressure", "Dead letter queues", "Getting started with Arroyo", "Contents:", "<no title>", "Metrics", "Committing offsets", "Batch and Unbatch", "Commit offsets", "Filter", "Healthchecks", "Processing Strategies", "Noop", "Produce", "Reduce (Fold)", "Run Task", "Run Task in Threads", "Run Task with Multiprocessing", "Unfold", "What is Arroyo for?"], "terms": {"i": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19], "set": [0, 2, 4, 5, 6, 11, 18], "abstract": [0, 3, 4, 5, 6, 12, 20], "interact": 0, "kafka": [0, 2, 4, 5, 7, 11, 12, 14], "These": [0, 6], "ar": [0, 2, 3, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20], "meant": 0, "help": [0, 20], "develop": 0, "write": [0, 3, 4, 5, 12, 20], "perform": [0, 1, 6, 12, 18, 20], "consum": [0, 1, 2, 4, 5, 6, 7, 9, 10, 11, 12, 14], 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(arroyo.processing.strategies.abstract.processingstrategy method)": [[12, "arroyo.processing.strategies.abstract.ProcessingStrategy.submit"]], "terminate() (arroyo.processing.strategies.abstract.processingstrategy method)": [[12, "arroyo.processing.strategies.abstract.ProcessingStrategy.terminate"]], "timestamp (arroyo.types.basevalue property)": [[12, "arroyo.types.BaseValue.timestamp"]], "timestamp (arroyo.types.brokervalue attribute)": [[12, "arroyo.types.BrokerValue.timestamp"]], "timestamp (arroyo.types.message property)": [[12, "arroyo.types.Message.timestamp"]], "timestamp (arroyo.types.value property)": [[12, "arroyo.types.Value.timestamp"]], "topic (arroyo.types.partition attribute)": [[12, "arroyo.types.Partition.topic"]], "value (arroyo.types.message attribute)": [[12, "arroyo.types.Message.value"]], "noop (class in arroyo.processing.strategies.noop)": [[13, "arroyo.processing.strategies.noop.Noop"]], "arroyo.processing.strategies.noop": [[13, "module-arroyo.processing.strategies.noop"]], "produce (class in arroyo.processing.strategies.produce)": [[14, "arroyo.processing.strategies.produce.Produce"]], "arroyo.processing.strategies.produce": [[14, "module-arroyo.processing.strategies.produce"]], "reduce (class in arroyo.processing.strategies.reduce)": [[15, "arroyo.processing.strategies.reduce.Reduce"]], "arroyo.processing.strategies.reduce": [[15, "module-arroyo.processing.strategies.reduce"]], "runtask (class in arroyo.processing.strategies.run_task)": [[16, "arroyo.processing.strategies.run_task.RunTask"]], "arroyo.processing.strategies.run_task": [[16, "module-arroyo.processing.strategies.run_task"]], "runtaskinthreads (class in arroyo.processing.strategies.run_task_in_threads)": [[17, "arroyo.processing.strategies.run_task_in_threads.RunTaskInThreads"]], "arroyo.processing.strategies.run_task_in_threads": [[17, "module-arroyo.processing.strategies.run_task_in_threads"]], "runtaskwithmultiprocessing (class in arroyo.processing.strategies.run_task_with_multiprocessing)": [[18, "arroyo.processing.strategies.run_task_with_multiprocessing.RunTaskWithMultiprocessing"]], "arroyo.processing.strategies.run_task_with_multiprocessing": [[18, "module-arroyo.processing.strategies.run_task_with_multiprocessing"]], "unfold (class in arroyo.processing.strategies.unfold)": [[19, "arroyo.processing.strategies.unfold.Unfold"]], "arroyo.processing.strategies.unfold": [[19, "module-arroyo.processing.strategies.unfold"]]}}) \ No newline at end of file diff --git a/strategies/batching.html b/strategies/batching.html new file mode 100644 index 00000000..a0a01ae7 --- /dev/null +++ b/strategies/batching.html @@ -0,0 +1,245 @@ + + + + + Batch and Unbatch - Arroyo documentation + + + + + + + + + + +
+
+
+ + + + Arroyo + +
+ +
+ + / +
+
+
+
+
+
+
+ + + + +
+
+
+
+

Batch and Unbatch

+

Accumulate messages into a batch and pass to the next step. +The batch and unbatch strategies are based on reduce and unfold. +Use reduce/unfold instead if you want to provide custom +accumulator/generator functions.

+
+
+class arroyo.processing.strategies.batching.BatchStep(max_batch_size: int, max_batch_time: float, next_step: ProcessingStrategy[MutableSequence[BaseValue[TStrategyPayload]]], compute_batch_size: Callable[[BaseValue[TStrategyPayload]], int] | None = None)
+

Accumulates messages into a batch. When the batch is full, this +strategy submits it to the next step.

+

A batch is represented as a ValuesBatch object which is a sequence +of BaseValue. This includes both the messages and the high offset +watermark.

+

A messages batch is closed and submitted when the maximum number of +messages is received or when the max_batch_time has passed since the +first message was received.

+

This step does not require in order processing. If messages are sent +out of order, though, the highest observed offset per partition is +still the committable one, whether or not all messages with lower +offsets have been observed by this step.

+

This strategy propagates MessageRejected exceptions from the +downstream steps if they are thrown.

+
+
Parameters:
+
    +

  • max_batch_size – How many messages should be reduced into one at maximum.

    • +

  • max_batch_time – How much time (in seconds) should be spent reducing +messages together before flushing the batch.

    • +
+
+
+
+
+join(timeout: float | None = None) → None
+

Terminates the strategy by joining the following step. +This method tries to flush the current batch no matter +whether the batch is ready or not.

+
+ +
+
+submit(message: Message[FilteredPayload | TStrategyPayload]) → None
+

Accumulates messages in the current batch. +A new batch is created at the first message received.

+

This method tries to flush before adding the message +to the current batch. This is so that, if we receive +MessageRejected exception from the following step, +we can propagate the exception without processing the +new message. This allows the previous step to try again +without introducing duplications.

+
+ +
+ +
+
+class arroyo.processing.strategies.batching.UnbatchStep(next_step: ProcessingStrategy[FilteredPayload | TStrategyPayload])
+

This processing step receives batches and explodes them thus sending +the content to the next step message by message.

+

A batch is represented as a ValuesBatch object.

+

If this step receives a MessageRejected exception from the next +step it would keep the remaining messages and attempt to submit +them at the following call to poll

+
+ +
+ +
+ + +
+ Previous
Unfold
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+
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+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/strategies/commit_offsets.html b/strategies/commit_offsets.html new file mode 100644 index 00000000..b5f165fc --- /dev/null +++ b/strategies/commit_offsets.html @@ -0,0 +1,187 @@ + + + + + Commit offsets - Arroyo documentation + + + + + + + + + + +
+
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+ +
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+
+
+
+
+
+
+ + + + +
+
+
+
+

Commit offsets

+

Should be used as the last strategy in the chain, to ensure +that offsets are only committed once all processing is complete.

+
+
+class arroyo.processing.strategies.commit.CommitOffsets(commit: Commit)
+

Commits offset and records consumer latency metric.

+

This should always be used as the last step in a chain of processing +strategies. It commits offsets back to the broker after all prior +processing of that message is completed.

+
+ +
+ +
+ + +
+ Previous
Produce
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+ Next +
Noop
+
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+
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+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
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+ + + + + + + \ No newline at end of file diff --git a/strategies/filter.html b/strategies/filter.html new file mode 100644 index 00000000..2fbd3dc3 --- /dev/null +++ b/strategies/filter.html @@ -0,0 +1,200 @@ + + + + + Filter - Arroyo documentation + + + + + + + + + + +
+
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+ +
+ + / +
+
+
+
+
+
+
+ + + + +
+
+
+
+

Filter

+
+
+class arroyo.processing.strategies.filter.FilterStep(function: Callable[[Message[TStrategyPayload]], bool], next_step: ProcessingStrategy[FilteredPayload | TStrategyPayload], commit_policy: CommitPolicy | None = None)
+

Determines if a message should be submitted to the next processing step.

+

FilterStep takes a callback, function, and if that callback returns +False, the message is dropped.

+

Sometimes that behavior is not actually desirable because streams of +messages is what makes the consumer commit in regular intervals. If you +filter 100% of messages for a period of time, your consumer may not commit +its offsets as a result.

+

For that scenario, you can pass your CommitPolicy to FilterStep. That +will cause FilterStep to emit “sentinel messages” that contain no +payload, but only carry forward partition offsets for later strategies to +commit. Those messages have a payload of type FilteredPayload.

+

For that reason, basically every strategy needs to be able to handle +Message[Union[FilteredPayload, T]] instead of Message[T], i.e. it needs +to subtype ProcessingStrategy[Union[FilteredPayload, TStrategyPayload]]. +If it doesn’t, and rather just handles the regular Message[T], it cannot +be composed with this step, and many other default strategies of arroyo.

+

If no CommitPolicy is passed, no “sentinel messages” are emitted and +downstream steps do not have to deal with such messages (despite the type +system telling them so).

+
+ +
+ +
+ + +
+ Previous
Processing Strategies
+ +
+ +
+ Next +
Reduce (Fold)
+
+ + +
+
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+
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+
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+

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+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/strategies/healthcheck.html b/strategies/healthcheck.html new file mode 100644 index 00000000..17a32df7 --- /dev/null +++ b/strategies/healthcheck.html @@ -0,0 +1,243 @@ + + + + + Healthchecks - Arroyo documentation + + + + + + + + + + +
+
+
+ + + + Arroyo + +
+ +
+ + / +
+
+
+
+
+
+
+ + + + +
+
+
+
+

Healthchecks

+

If your code blocks for too long in the main thread, the consumer can turn +unhealthy.

+

Kafka has a setting called max.poll.interval.ms for this that tells Kafka +to kick the consumer out of the broker after this many milliseconds of not polling.

+

You can pass this option into arroyo.backends.kafka.consumer.KafkaConsumer like so:

+
consumer = KafkaConsumer(
+    {
+        "max.poll.interval.ms": 300000, # default 5 minutes
+    }
+)
+
+
+

However, this will not shut down the consumer, it will just keep running doing +nothing (because it is blocked in the main thread). You want a pod-level +healthcheck as well.

+

Arroyo supports touching a file repeatedly from the main thread to indicate +health. Start your pipeline with the +arroyo.processing.strategies.healthcheck.Healthcheck strategy.

+
def handle_message(message: Message[KafkaPayload]) -> Message[KafkaPayload]:
+    ...
+    return message
+
+class ConsumerStrategyFactory(ProcessingStrategyFactory[KafkaPayload]):
+    def __init__(self):
+        self.is_paused = False
+
+    def create_with_partitions(
+        self,
+        commit: Commit,
+        partitions: Mapping[Partition, int],
+    ) -> ProcessingStrategy[KafkaPayload]:
+        step = RunTask(handle_message, CommitOffsets(commit))
+        return Healthcheck("/tmp/health.txt", step)
+
+
+

The Kubernetes liveness +command would look like:

+
apiVersion: v1
+kind: Pod
+metadata:
+  labels:
+    test: liveness
+  name: liveness-exec
+spec:
+  containers:
+  - name: liveness
+    image: registry.k8s.io/busybox
+    args:
+      - bin/my_arroyo_consumer
+    livenessProbe:
+      exec:
+        command:
+        - rm
+        - /tmp/health.txt
+      initialDelaySeconds: 5
+      periodSeconds: 320  # should be higher than max.poll.interval.ms
+
+
+
+
+class arroyo.processing.strategies.healthcheck.Healthcheck(healthcheck_file: str, next_step: ProcessingStrategy[TStrategyPayload])
+

A strategy that takes a filepath, and touches that file everytime +Strategy.poll is called. If that function is not called multiple times +per minute, it indicates that the consumer is overall unhealthy.

+

File touches are debounced to happen once per second at most.

+
+ +
+ +
+ + +
+ Previous
Noop
+ +
+ +
+ Next +
Committing offsets
+
+ + +
+
+
+
+
+
+
+

2023, Sentry Team and Contributors

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/strategies/index.html b/strategies/index.html new file mode 100644 index 00000000..850a096c --- /dev/null +++ b/strategies/index.html @@ -0,0 +1,527 @@ + + + + + Processing Strategies - Arroyo documentation + + + + + + + + + + +
+
+
+ + + + Arroyo + +
+ +
+ + / +
+
+
+
+
+
+
+ + + + +
+
+
+
+

Processing Strategies

+

The processing strategies are the components to be wired together to +build a consumer.

+
+

Strategy interface

+

We normally don’t recommend writing your own strategy, and encourage you to use +built-in ones such as “reduce” or “run task” to plug in your application logic. +Nevertheless, all arroyo strategies are written against the following interface:

+
+
+exception arroyo.processing.strategies.abstract.MessageRejected
+

Bases: Exception

+

MessageRejected should be raised in a processing strategy’s submit method +if it is unable to keep up with the rate of incoming messages. It tells +the consumer to slow down and retry the message later.

+
+ +
+
+class arroyo.processing.strategies.abstract.ProcessingStrategy(*args, **kwds)
+

Bases: ABC, Generic[TStrategyPayload]

+

A processing strategy defines how a stream processor processes messages +during the course of a single assignment. The processor is instantiated +when the assignment is received, and closed when the assignment is +revoked.

+

This interface is intentionally not prescriptive, and affords a +significant degree of flexibility for the various implementations.

+
+
+abstract close() → None
+

Close this instance. No more messages should be accepted by the +instance after this method has been called.

+

This method should not block. Once this strategy instance has +finished processing (or discarded) all messages that were submitted +prior to this method being called, the strategy should commit its +partition offsets and release any resources that will no longer be +used (threads, processes, sockets, files, etc.)

+
+ +
+
+abstract join(timeout: float | None = None) → None
+

Block until the processing strategy has completed all previously +submitted work, or the provided timeout has been reached. This method +should be called after close to provide a graceful shutdown.

+

This method is called synchronously by the stream processor during +assignment revocation, and blocks the assignment from being released +until this function exits, allowing any work in progress to be +completed and committed before the continuing the rebalancing +process.

+
+ +
+
+abstract poll() → None
+

Poll the processor to check on the status of asynchronous tasks or +perform other scheduled work.

+

This method is called on each consumer loop iteration, so this method +should not be used to perform work that may block for a significant +amount of time and block the progress of the consumer or exceed the +consumer poll interval timeout.

+

This method may raise exceptions that were thrown by asynchronous +tasks since the previous call to poll.

+
+ +
+
+abstract submit(message: Message[TStrategyPayload]) → None
+

Submit a message for processing.

+

Messages may be processed synchronously or asynchronously, depending +on the implementation of the processing strategy. Callers of this +method should not assume that this method returning successfully +implies that the message was successfully processed.

+

If the processing strategy is unable to accept a message (due to it +being at or over capacity, for example), this method will raise a +MessageRejected exception.

+
+ +
+
+abstract terminate() → None
+

Close the processing strategy immediately, abandoning any work in +progress. No more messages should be accepted by the instance after +this method has been called.

+
+ +
+ +
+
+class arroyo.processing.strategies.abstract.ProcessingStrategyFactory(*args, **kwds)
+

Bases: ABC, Generic[TStrategyPayload]

+

A ProcessingStrategyFactory is used to wrap a series of +ProcessingStrategy steps, and calls create_with_partitions +to instantiate the ProcessingStrategy on partition assignment +or partition revocation if the strategy needs to be recreated.

+
+
+abstract create_with_partitions(commit: Commit, partitions: Mapping[Partition, int]) → ProcessingStrategy[TStrategyPayload]
+

Instantiate and return a ProcessingStrategy instance.

+
+
Parameters:
+
    +

  • commit – A function that accepts a mapping of Partition instances to offset values that should be committed.

    • +

  • partitions – A mapping of a Partition to it’s most recent offset.

    • +
+
+
+
+ +
+
+shutdown() → None
+

Custom code to execute when the StreamProcessor shuts down entirely.

+

Note that this code will also be executed on crashes of the strategy.

+
+ +
+ +
+
+

Messages

+
+
+class arroyo.types.BaseValue(*args, **kwds)
+
+
+property committable: Mapping[Partition, int]
+
+ +
+
+property payload: TMessagePayload
+
+ +
+
+replace(value: TReplaced) → BaseValue[TReplaced]
+
+ +
+
+property timestamp: datetime | None
+
+ +
+ +
+
+class arroyo.types.BrokerValue(payload: TMessagePayload, partition: Partition, offset: int, timestamp: datetime)
+

A payload received from the consumer or producer after it is done producing. +Partition, offset, and timestamp values are present.

+
+
+property committable: Mapping[Partition, int]
+
+ +
+
+property next_offset: int
+
+ +
+
+offset: int
+
+ +
+
+partition: Partition
+
+ +
+
+property payload: TMessagePayload
+
+ +
+
+replace(value: TReplaced) → BaseValue[TReplaced]
+
+ +
+
+timestamp: datetime
+
+ +
+ +
+
+class arroyo.types.Commit(*args, **kwargs)
+
+ +
+
+class arroyo.types.FilteredPayload
+
+ +
+
+class arroyo.types.Message(value: BaseValue[TMessagePayload])
+

Contains a payload and partitions to be committed after processing. +Can either represent a single message from a Kafka broker (BrokerValue) +or something else, such as a number of messages grouped together for a +batch processing step (Payload).

+
+
+property committable: Mapping[Partition, int]
+
+ +
+
+property payload: TMessagePayload
+
+ +
+
+property payload_unfiltered: TMessagePayload
+
+ +
+
+replace(payload: TReplaced) → Message[TReplaced]
+
+ +
+
+property timestamp: datetime | None
+
+ +
+
+value: BaseValue[TMessagePayload]
+
+ +
+ +
+
+class arroyo.types.Partition(topic: 'Topic', index: 'int')
+
+
+index: int
+
+ +
+
+topic: Topic
+
+ +
+ +
+
+class arroyo.types.Topic(name: 'str')
+
+
+name: str
+
+ +
+ +
+
+class arroyo.types.Value(payload: TMessagePayload, committable: Mapping[Partition, int], timestamp: datetime | None = None)
+

Any other payload that may not map 1:1 to a single message from a +consumer. May represent a batch spanning many partitions.

+
+
+property committable: Mapping[Partition, int]
+
+ +
+
+property payload: TMessagePayload
+
+ +
+
+replace(value: TReplaced) → BaseValue[TReplaced]
+
+ +
+
+property timestamp: datetime | None
+
+ +
+ +
+
+
+
+ +
+ + +
+ Previous
Arroyo Architecture
+ +
+ +
+ Next +
Filter
+
+ + +
+
+
+
+
+
+
+

2023, Sentry Team and Contributors

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/strategies/noop.html b/strategies/noop.html new file mode 100644 index 00000000..27fb770e --- /dev/null +++ b/strategies/noop.html @@ -0,0 +1,182 @@ + + + + + Noop - Arroyo documentation + + + + + + + + + + +
+
+
+ + + + Arroyo + +
+ +
+ + / +
+
+
+
+
+
+
+ + + + +
+
+
+
+

Noop

+
+
+class arroyo.processing.strategies.noop.Noop
+

Noop strategy that takes a message and does nothing.

+
+ +
+ +
+ + +
+ Previous
Commit offsets
+ +
+ +
+ Next +
Healthchecks
+
+ + +
+
+
+
+
+
+
+

2023, Sentry Team and Contributors

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/strategies/produce.html b/strategies/produce.html new file mode 100644 index 00000000..d484a919 --- /dev/null +++ b/strategies/produce.html @@ -0,0 +1,193 @@ + + + + + Produce - Arroyo documentation + + + + + + + + + + +
+
+
+ + + + Arroyo + +
+ +
+ + / +
+
+
+
+
+
+
+ + + + +
+
+
+
+

Produce

+
+
+class arroyo.processing.strategies.produce.Produce(producer: Producer[TStrategyPayload], topic: Topic, next_step: ProcessingStrategy[FilteredPayload | TStrategyPayload], max_buffer_size: int = 10000)
+

This strategy can be used to produce Kafka messages to a destination topic. A typical use +case could be to consume messages from one topic, apply some transformations and then output +to another topic.

+

For each message received in the submit method, it attempts to produce a single Kafka message +in a thread. If there are too many pending futures, we MessageRejected will be raised to notify +stream processor to slow down.

+

On poll we check for completion of the produced messages. If the message has been successfully +produced then the message is submitted to the next step. If an error occured the exception will +be raised.

+

Important: The destination topic is always the topic passed into the constructor and not the +topic being referenced in the message itself (which typically refers to the original topic from +where the message was consumed from).

+
+ +
+ +
+ + +
+ Previous
Run Task with Multiprocessing
+ +
+ +
+ Next +
Commit offsets
+
+ + +
+
+
+
+
+
+
+

2023, Sentry Team and Contributors

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/strategies/reduce.html b/strategies/reduce.html new file mode 100644 index 00000000..e5349895 --- /dev/null +++ b/strategies/reduce.html @@ -0,0 +1,196 @@ + + + + + Reduce (Fold) - Arroyo documentation + + + + + + + + + + +
+
+
+ + + + Arroyo + +
+ +
+ + / +
+
+
+
+
+
+
+ + + + +
+
+
+
+

Reduce (Fold)

+

Accumulate messages based on a custom accumulator function

+
+
+class arroyo.processing.strategies.reduce.Reduce(max_batch_size: int, max_batch_time: float, accumulator: Callable[[TResult, BaseValue[TPayload]], TResult], initial_value: Callable[[], TResult], next_step: ProcessingStrategy[TResult], compute_batch_size: Callable[[BaseValue[TPayload]], int] | None = None)
+

Accumulates messages until the max size or max time condition is hit. +The accumulator function is run on each message in the order it is received.

+

Once the “batch” is full, the accumulated value is submitted to the next step.

+

This strategy propagates MessageRejected exceptions from the +downstream steps if they are thrown.

+
+
Parameters:
+
    +

  • max_batch_size – How many messages should be reduced into one at maximum.

    • +

  • max_batch_time – How much time (in seconds) should be spent reducing +messages together before flushing the batch.

    • +
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Run Task

+
+
+class arroyo.processing.strategies.run_task.RunTask(function: Callable[[Message[TStrategyPayload]], TResult], next_step: ProcessingStrategy[FilteredPayload | TResult])
+

Basic strategy to run a custom processing function on a message.

+

The processing function provided can raise InvalidMessage to indicate that +the message is invalid and should be put in a dead letter queue.

+
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Run Task in Threads

+
+
+class arroyo.processing.strategies.run_task_in_threads.RunTaskInThreads(processing_function: Callable[[Message[TStrategyPayload]], TResult], concurrency: int, max_pending_futures: int, next_step: ProcessingStrategy[FilteredPayload | TResult])
+

This strategy can be used to run IO-bound tasks in parallel.

+

The user specifies a processing function (a callable that takes a message). For each message received +in the submit method, it runs that processing function. Once completed, the message is submitted +to the next step (with the payload containing the result of the processing function).

+

Since the processing function will be run in threads, avoid using objects which can be modified +by different threads or protect it using locks.

+

If there are too many pending futures, we MessageRejected will be raised to notify the stream processor +to slow down.

+

On poll we check for completion of futures. If processing is done, we submit to the next step. +If an error occured the original exception will be raised.

+
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Run Task with Multiprocessing
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Run Task with Multiprocessing

+
+
+class arroyo.processing.strategies.run_task_with_multiprocessing.RunTaskWithMultiprocessing(function: Callable[[Message[TStrategyPayload]], TResult], next_step: ProcessingStrategy[FilteredPayload | TResult], max_batch_size: int, max_batch_time: float, pool: MultiprocessingPool, input_block_size: int | None = None, output_block_size: int | None = None, max_input_block_size: int | None = None, max_output_block_size: int | None = None)
+

Run a function in parallel across messages using subprocesses.

+

RunTaskWithMultiprocessing uses the multiprocessing stdlib module to +transform messages in parallel.

+
+
Parameters:
+
    +

  • function – The function to use for transforming.

    • +

  • next_step – The processing strategy to forward transformed messages to.

    • +

  • max_batch_size – Wait at most for this many messages before “closing” a batch.

    • +

  • max_batch_time – Wait at most for this many seconds before closing a batch.

    • +

  • pool – The multiprocessing pool to use for parallel processing. The same pool +instance can be re-used each time RunTaskWithMultiprocessing is created on +rebalance.

    • +

  • input_block_size –

    For each subprocess, a shared memory buffer of +input_block_size is allocated. This value should be at least +message_size * max_batch_size large, where message_size is the expected +average message size.

    +

    If the value is too small, the batch is implicitly broken up. In that +case, the +arroyo.strategies.run_task_with_multiprocessing.batch.input.overflow +metric is emitted.

    +

    If the value is set to None, the input_block_size is automatically +adjusted to adapt to traffic. Keep in mind that this is a rather +experimental feature and less productionized than explicitly setting a +value.

    +

    • +

  • output_block_size –

    Size of the shared memory buffer used to store +results. Like with input data, the batch is implicitly broken up on +overflow, and +arroyo.strategies.run_task_with_multiprocessing.batch.output.overflow +metric is incremented.

    +

    Like with input_block_size, the value can be set to None to enable +automatic resizing.

    +

    • +

  • max_input_block_size – If automatic resizing is enabled, this sets an +upper limit on how large those blocks can get.

    • +

  • max_output_block_size – Same as max_input_block_size but for output +blocks.

    • +
+
+
+
+

Number of processes

+

The metric +arroyo.strategies.run_task_with_multiprocessing.batches_in_progress +shows you how many processes arroyo is able to effectively use at any given +point.

+

The metric arroyo.strategies.run_task_with_multiprocessing.processes +shows how many processes arroyo was configured with.

+

If those two metrics don’t line up, your consumer is not bottlenecked on +number of processes. That’s a good thing, you want to have some reserve +capacity. But it means that increasing num_processes will not make your +consumer faster.

+
+
+

Batching

+

Arroyo sends messages in batches to subprocesses. max_batch_size and max_batch_time +should be tweaked for optimal performance. You can observe the effect in the following metrics:

+
    +

  • arroyo.strategies.run_task_with_multiprocessing.batch.size.msg: The number of messages per batch.

    • +

  • arroyo.strategies.run_task_with_multiprocessing.batch.size.bytes: The number of bytes used per batch.

    • +
+

The cost of batches (locking, synchronization) generally amortizes with +increased batch sizes. Too small batches, and this strategy will spend a +lot of time synchronizing between processes. Too large batches, however, +can cause your consumer to not use all processes effectively, as a lot of +time may be spent waiting for batches to fill up.

+

If batch.size.msg is flat (as in, it’s a perfectly straight line at a +constant), you are hitting max_batch_size. If batch.size.bytes is +flat, you are hitting input buffer overflow (see next section). If neither +are flat, you are hitting max_batch_time.

+
+
+

Input and output buffers

+

You want to keep an eye on these metrics:

+
    +

  1. arroyo.strategies.run_task_with_multiprocessing.batch.input.overflow

    2. +

  2. arroyo.strategies.run_task_with_multiprocessing.batch.output.overflow

    3. +

  3. arroyo.strategies.run_task_with_multiprocessing.batch.backpressure

    4. +
+

If batch.input.overflow is emitted at all, arroyo ran out of memory for +batching and started breaking up your batches into smaller ones. You want +to increase input_block_size in response. Note that when you do this, +you may have to re-tweak max_batch_size and max_batch_time, as you +were never hitting those configured limits before. Input overflow is not +really all that expensive in Arroyo, but since it affects how batching +works it can still make performance tuning of your consumer more confusing. +Best to avoid it anyway.

+

If batch.output.overflow is emitted at all, arroyo ran out of memory +when fetching the data from subprocesses, and so the response from +subprocesses to the main processes is chunked. Output overflow is very +expensive, and you want to avoid it. Increase output_block_size in +response.

+

If batch.backpressure is continuously emitted, you are not bottlenecked +on multiprocessing at all, but instead the next strategy can’t keep up and +is applying backpressure. You can likely reduce num_processes and won’t +notice a performance regression.

+
+
+

How to tune your consumer

+

Note that it doesn’t make sense to fix output overflow without fixing input +overflow first. If you increase output block size to get rid of output +overflows, then increase input block size, your effective batch size may +increase to a point where you encounter output overflow again. If you +encounter a lot of issues at once, best to fix them in this order:

+
    +

  1. First, tune input_block_size to fix input overflow. This will +increase average/effective batch size. Alternatively, set it to None +(default) to let arroyo auto-tune it.

    2. +

  2. Then, tune max_batch_size and max_batch_time so that you get the +highest throughput. Test this by running your consumer on a backlog of +messages and look at consumer offset rate (vs broker/total offset rate), +or time it takes to get consumer lag back to normal. For as long as you +have enough RAM, increment it in large steps (like 2x) and fine-tune +afterwards.

    3. +

  3. Then, tune output_block_size to fix output overflow. If in your +previous tests there was a lot of output overflow, this will remove a lot +of CPU load from your consumer and potentially also increase throughput.

    4. +

  4. Now take a look at the batch.backpressure metric. If it is emitted, +you need to optimize the next strategy (next_step) because that’s what +you’re bottlenecked on. If it is not emitted, you may need to increase +num_processes or increase batch size, so that +RunTaskWithMultiprocessing itself is not the bottleneck.

    5. +
+
+
+ +
+ +
+ + +
+ Previous
Run Task in Threads
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Produce
+
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+ +

+ Made with + + Sphinx and + + Shibuya theme. +

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+
+

Unfold

+

Generates a sequence of messages from a single message based on a custom generator function

+
+
+class arroyo.processing.strategies.unfold.Unfold(generator: Callable[[TInput], Iterable[BaseValue[TOutput]]], next_step: ProcessingStrategy[FilteredPayload | TOutput])
+

Unfold receives a message and explodes it to generate a collection of +messages submitting them one by one to the next step. The generated +messages are created according to the generator function provided by the user.

+

The generator function provided must return an iterable (i.e. a class that +implements __iter__ ).

+

If this step receives a MessageRejected exception from the next +step it keeps the remaining messages and attempts to submit +them on subsequent calls to poll

+
+ +
+ +
+ + +
+ Previous
Reduce (Fold)
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Batch and Unbatch
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+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
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+ + + + + + + \ No newline at end of file diff --git a/what_for.html b/what_for.html new file mode 100644 index 00000000..27888a6c --- /dev/null +++ b/what_for.html @@ -0,0 +1,432 @@ + + + + + What is Arroyo for? - Arroyo documentation + + + + + + + + + + +
+
+
+ + + + Arroyo + +
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+
+
+
+

What is Arroyo for?

+

Arroyo is a library for writing high-throughput, testable kafka +consumers and producers. This document attempts to outline the +intricacies of writing such consumers.

+
+

Goals

+
    +

  1. Make it easy to build consumers that provide delivery guarantees

    2. +

  2. Make it easy to write high-throughput kafka consumers

    3. +

  3. Make it easy to write reliable, high throughput kafka producers

    4. +

  4. Abstract away rebalancing such that users of the library do not have +to worry about it

    5. +

  5. Make it possible to test kafka consumer logic without having to +reproduce the entire kafka environment

    6. +

  6. Provide a way for the application logic to signal backpressure

    7. +
+
+
+

Why Simple Doesn’t Cut It

+

When visualizing event-driven architecture, Kafka is viewed as an +abstract queue with groups of producers pushing to it, and consumers +consuming from it (as in the diagram below).

+
+ graph TD + Producer --> Kafka_Topic + Kafka_Topic --> Consumer + Consumer --> Destination +

A more accurate model is that kafka is like a log file which is +persistent and there are offsets of the file that different consumers +have read or not read.

+

The most simple kafka consumer looks something like this:

+
from confluent_kafka import Consumer
+
+conf = {
+    "bootstrap.servers": "localhost:9092",
+    "group.id": "my_group",
+    "auto.offset.reset": "latest",
+}
+
+consumer = Consumer(conf)
+consumer.subscribe(["my_topic"])
+
+while True:
+    message = consumer.poll()
+    send_to_destination(process_message(message))
+
+
+

This simple consumer would not satisfy the goals mentioned at the top of +this page. The following subsections will explain why

+
+
+

Providing delivery guarantees

+

By default, a consumer in the confluent_kafka library will +auto-commit on poll. To understand what it means to commit to a kafka +topic, see the Appendix. This can lead to the following issue:

+
# get message from kafka, commit immediately
+message = consumer.poll()
+# ❗❗❗ throws exception due to a network issue
+send_to_destination(process_message(message))
+# this message is now lost and we're on to the next one
+
+
+

This can be fixed by only committing after we know that the message has +reached its destination in the following way:

+
# add this value to the config:
+"enable.auto.commit": "false"
+# -------
+message = consumer.poll(timeout=0)
+send_to_destination(process_message(message))
+consumer.commit(message.offset())
+
+
+
+
+

High Throughput

+

The previous section has allowed us to not commit messages that are not +processed however committing every message severely hurts throughput. +Every call to commit is a network operation, it also makes the broker +persist and replicate the information. If we can reduce the number of +commit calls, our throughput can be much higher. And so we commit in +batches

+
# this code is purely descriptive.
+# We have to commit to each partition separately
+# but that code is not helpful for this example
+message = consumer.poll(timeout=0)
+batch.append(process_message(message))
+if len(batch) == batch_size:
+    consumer.commit(offsets=[m.offset() for m in batch])
+
+
+

This will get us faster throughput however we are currently hand-waving +away how we send the message to its destination

+
+
+

Reliable High Throughput Batched Producers

+

Producing to Kafka reliably and at high throughput is not a simple +operation. Here is how a simple Kafka Producer looks in code:

+
from confluent_kafka import Producer
+
+conf = {
+  "bootstrap.servers": "localhost:9092",
+}
+producer = Producer(conf)
+def send_to_destination(message):
+    # ❗ This does not do what it says
+    # it writes to a buffer
+    producer.produce("destination_topic", message)
+    # this will actually block until the messages are produced
+    # calling this after produce every time is very expensive,
+    # how often we flush has high impacts on the producer throughput
+    producer.flush()
+
+
+

At a high level, the producer is actually buffering the messages +produced to the topic

+_images/kafka_producer.png +

A kafka producer writes to an internal buffer. This batches the IO +(good) but you don’t know when it will ever make it to the +destination

+

In order to allow for reliability of transmission, the +confluent_kafka library provides a callback to +produce +like so

+
def delivery_callback(error, message):
+    # do something here to make sure your message is in the state
+    # you want it to be
+
+producer.produce("destination_topic", message, on_delivery=delivery_callback)
+
+
+
+
+

Dealing With Rebalancing

+
+

What is Rebalancing

+

A kafka topic is divided into n partitions, each partition can be +consumed by exactly one consumer per consumer +group. A consumer can +consume multiple partitions

+
+_images/consumer_groups.png +
+
+
+

When Rebalancing Can Happen

+

Rebalancing can happen due to:

+
    +

  • An addition or removal of a consumer to a consumer group

    +
      +

    • (Every deploy does this)

      • +
    +
    • +

  • A rebalance being kicked off manually

    • +

  • A consumer pod dies and now its partition needs to be re-assigned

    • +

  • Whenever the broker decides it’s a good idea (it can happen at any +time)

    • +

  • TODO: More things?

    • +
+
+
+

How Rebalancing Affects a Consumer

+

Rebalancing is annoying to handle for a consumer that processes batches, +imagine the following scenario:

+
+ sequenceDiagram + Broker->>Consumer: message + activate Consumer + note right of Consumer: start building batch + Broker->>Consumer: message + Broker->>Consumer: Revoke Partition + deactivate Consumer + Consumer->>Broker: commit batch + note left of Broker: Received commit from revoked Consumer! +

Once a partition is revoked for a consumer, it cannot commit to it. This +is bad news for the batch that the consumer has built up. Each consumer +has different requirements but a decision has to be made as to whether +to flush the batch or to discard its work and let the next consumer +assigned to this partition pick it up. The rebalancing behavior can be +customized by providing an on_revoke callback to the consumer when +subscribing.

+
from confluent_kafka import Consumer
+
+conf = {
+    "bootstrap.servers": "localhost:9092",
+    "group.id": "my_group",
+    "auto.offset.reset": "latest",
+}
+
+def flush_current_batch(consumer, partitions):
+    # flush the current batch
+    pass
+
+consumer = Consumer(conf)
+consumer.subscribe(["my_topic"], on_revoke=flush_current_batch)
+
+
+
+
+
+

librdkafka’s Callback Hell

+

librdkafka uses callbacks as a core mechanic for control flow. A few +such examples have been mentioned in this document already. What is not +clear however, is that callbacks are only called when ``poll`` is +called

+

This means that:

+
    +

  • this line could possibly do a lot of work:

    • +
+
# any scheduled callbacks will run within this call
+message = consumer.poll()
+
+
+
    +

  • No callbacks will be invoked until the consumer or producer call +poll again (for their respective callbacks)

    • +

  • poll has to be called periodically on a consumer otherwise the +broker will kick the consumer out of the consumer group

    +
      +

    • The handling of that revocation won’t happen until poll is +called

      • +
    +
    • +
+
+
+

Conclusion

+

There are many intricacies and gotchas to writing high performant, +reliable kafka consumers. This document does not outline all of them but +all of what is outlined here should be kept in mind when designing any +kafka consumer library.

+
+
+

Appendix

+
+
+

Committing to a Kafka Topic

+

A consumer comitting to a topic signals to the broker that this message +has been processed. When poll is called next by that consumer, it +will return the next message.

+

API +Doc

+
+
+

What is a Kafka Consumer Group

+

https://www.educba.com/kafka-consumer-group/

+
+
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Home
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2023, Sentry Team and Contributors

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
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+ + + + + + + \ No newline at end of file