Apache beam runners

Apache beam runners DEFAULT

Runner Authoring Guide

This guide walks through how to implement a new runner. It is aimed at someone who has a data processing system and wants to use it to execute a Beam pipeline. The guide starts from the basics, to help you evaluate the work ahead. Then the sections become more and more detailed, to be a resource throughout the development of your runner.

Topics covered:

Implementing the Beam Primitives

Aside from encoding and persisting data - which presumably your engine already does in some way or another - most of what you need to do is implement the Beam primitives. This section provides a detailed look at each primitive, covering what you need to know that might not be obvious and what support code is provided.

The primitives are designed for the benefit of pipeline authors, not runner authors. Each represents a different conceptual mode of operation (external IO, element-wise, grouping, windowing, union) rather than a specific implementation decision. The same primitive may require a very different implementation based on how the user instantiates it. For example, a that uses state or timers may require key partitioning, a with speculative triggering may require a more costly or complex implementation, and is completely different for bounded and unbounded data.

What if you haven’t implemented some of these features?

That’s OK! You don’t have to do it all at once, and there may even be features that don’t make sense for your runner to ever support. We maintain a capability matrix on the Beam site so you can tell users what you support. When you receive a , you should traverse it and determine whether or not you can execute each that you find. If you cannot execute some in the pipeline (or if there is any other requirement that your runner lacks) you should reject the pipeline. In your native environment, this may look like throwing an . The Runner API RPCs will make this explicit, for cross-language portability.

Implementing the ParDo primitive

The primitive describes element-wise transformation for a . is the most complex primitive, because it is where any per-element processing is described. In addition to very simple operations like standard or from functional programming, also supports multiple outputs, side inputs, initialization, flushing, teardown, and stateful processing.

The UDF that is applied to each element is called a . The exact APIs for a can vary per language/SDK but generally follow the same pattern, so we can discuss it with pseudocode. I will also often refer to the Java support code, since I know it and most of our current and future runners are Java-based.

Bundles

For correctness, a should represent an element-wise function, but in fact is a long-lived object that processes elements in small groups called bundles.

Your runner decides how many elements, and which elements, to include in a bundle, and can even decide dynamically in the middle of processing that the current bundle has “ended”. How a bundle is processed ties in with the rest of a DoFn’s lifecycle.

It will generally improve throughput to make the largest bundles possible, so that initialization and finalization costs are amortized over many elements. But if your data is arriving as a stream, then you will want to terminate a bundle in order to achieve appropriate latency, so bundles may be just a few elements.

The DoFn Lifecycle

While each language’s SDK is free to make different decisions, the Python and Java SDKs share an API with the following stages of a DoFn’s lifecycle.

However, if you choose to execute a DoFn directly to improve performance or single-language simplicity, then your runner is responsible for implementing the following sequence:

  • Setup - called once per DoFn instance before anything else; this has not been implemented in the Python SDK so the user can work around just with lazy initialization
  • StartBundle - called once per bundle as initialization (actually, lazy initialization is almost always equivalent and more efficient, but this hook remains for simplicity for users)
  • ProcessElement / OnTimer - called for each element and timer activation
  • FinishBundle - essentially “flush”; required to be called before considering elements as actually processed
  • Teardown - release resources that were used across bundles; calling this can be best effort due to failures

DoFnRunner(s)

This is a support class that has manifestations in both the Java codebase and the Python codebase.

Java

In Java, the library provides an interface for bundle processing, with implementations for many situations.

There are some implementations and variations of this for different scenarios:

These are all used heavily in implementations of Java runners. Invocations via the Fn API may manifest as another implementation of even though it will be doing far more than running a .

Python

See the DoFnRunner pydoc.

Main design document: https://s.apache.org/beam-side-inputs-1-pager

A side input is a global view of a window of a . This distinguishes it from the main input, which is processed one element at a time. The SDK/user prepares a adequately, the runner materializes it, and then the runner feeds it to the .

What you will need to implement is to inspect the materialization requested for the side input, and prepare it appropriately, and corresponding interactions when a reads the side inputs.

The details and available support code vary by language.

Java

If you are using one of the above classes, then the interface for letting them request side inputs is . It is a simple mapping from side input and window to a value. The will perform a mapping with the to request the appropriate window so you do not worry about invoking this UDF. When using the Fn API, it will be the SDK harness that maps windows as well.

A simple, but not necessarily optimal approach to building a is to use a state backend. In our Java support code, this is called and you can build a that will use your to materialize a into the appropriate side input view and then yield the value when requested for a particular side input and window.

When a side input is needed but the side input has no data associated with it for a given window, elements in that window must be deferred until the side input has some data. The aforementioned is used to implement this.

Python

In Python, maps windows to side input values. The manifests as a simple function. See sideinputs.py.

State and Timers

Main design document: https://s.apache.org/beam-state

When a includes state and timers, its execution on your runner is usually very different. See the full details beyond those covered here.

State and timers are partitioned per key and window. You may need or want to explicitly shuffle data to support this.

Java

We provide to help with state cleanup. The non-user-facing interface is what a runner generally implements, and then the Beam support code can use this to implement user-facing state.

Splittable DoFn

Main design document: https://s.apache.org/splittable-do-fn

Splittable is a generalization and combination of and . It is per-element processing where each element has the capability of being “split” in the same ways as a or . This enables better performance for use cases such as a of names of large files where you want to read each of them. Previously they would have to be static data in the pipeline or be read in a non-splittable manner.

This feature is still under development, but likely to become the new primitive for reading. It is best to be aware of it and follow developments.

Implementing the GroupByKey (and window) primitive

The operation (sometimes called GBK for short) groups a of key-value pairs by key and window, emitting results according to the 's triggering configuration.

It is quite a bit more elaborate than simply colocating elements with the same key, and uses many fields from the 's windowing strategy.

Group By Encoded Bytes

For both the key and window, your runner sees them as “just bytes”. So you need to group in a way that is consistent with grouping by those bytes, even if you have some special knowledge of the types involved.

The elements you are processing will be key-value pairs, and you’ll need to extract the keys. For this reason, the format of key-value pairs is standardized and shared across all SDKS. See either in Java or in Python for documentation on the binary format.

Window Merging

As well as grouping by key, your runner must group elements by their window. A has the option of declaring that it merges windows on a per-key basis. For example, session windows for the same key will be merged if they overlap. So your runner must invoke the merge method of the during grouping.

Implementing via GroupByKeyOnly + GroupAlsoByWindow

The Java codebase includes support code for a particularly common way of implementing the full operation: first group the keys, and then group by window. For merging windows, this is essentially required, since merging is per key.

Dropping late data

Main design document: https://s.apache.org/beam-lateness

A window is expired in a if the watermark of the input PCollection has exceeded the end of the window by at least the input 's allowed lateness.

Data for an expired window can be dropped any time and should be dropped at a . If you are using , then just before executing this transform. You may shuffle less data if you drop data prior to , but should only safely be done for non-merging windows, as a window that appears expired may merge to become not expired.

Triggering

Main design document: https://s.apache.org/beam-triggers

The input 's trigger and accumulation mode specify when and how outputs should be emitted from the operation.

In Java, there is a lot of support code for executing triggers in the implementations, (legacy name), and , which is an obvious way of implementing all triggers as an event-driven machine over elements and timers.

TimestampCombiner

When an aggregated output is produced from multiple inputs, the operation has to choose a timestamp for the combination. To do so, first the WindowFn has a chance to shift timestamps - this is needed to ensure watermarks do not prevent progress of windows like sliding windows (the details are beyond this doc). Then, the shifted timestamps need to be combined - this is specified by a , which can either select the minimum or maximum of its inputs, or just ignore inputs and choose the end of the window.

Implementing the Window primitive

The window primitive applies a UDF to place each input element into one or more windows of its output PCollection. Note that the primitive also generally configures other aspects of the windowing strategy for a , but the fully constructed graph that your runner receives will already have a complete windowing strategy for each .

To implement this primitive, you need to invoke the provided WindowFn on each element, which will return some set of windows for that element to be a part of in the output .

Implementation considerations

A “window” is just a second grouping key that has a “maximum timestamp”. It can be any arbitrary user-defined type. The provides the coder for the window type.

Beam’s support code provides which is a compressed representation of an element in multiple windows. You may want to do use this, or your own compressed representation. Remember that it simply represents multiple elements at the same time; there is no such thing as an element “in multiple windows”.

For values in the global window, you may want to use an even further compressed representation that doesn’t bother including the window at all.

In the future, this primitive may be retired as it can be implemented as a ParDo if the capabilities of ParDo are enhanced to allow output to new windows.

Implementing the Read primitive

You implement this primitive to read data from an external system. The APIs are carefully crafted to enable efficient parallel execution. Reading from an is a bit different than reading from a .

Reading from an UnboundedSource

An is a source of potentially infinite data; you can think of it like a stream. The capabilities are:

  • - your runner should call this to get the desired parallelism
  • - call this to start reading elements; it is an enhanced iterator that also provides:
  • watermark (for this source) which you should propagate downstream
  • timestamps, which you should associate with elements read
  • record identifiers, so you can dedup downstream if needed
  • progress indication of its backlog
  • checkpointing
  • - this indicates that there is some chance that the source may emit duplicates; your runner should do its best to dedupe based on the identifier attached to emitted records

An unbounded source has a custom type of checkpoints and an associated coder for serializing them.

Reading from a BoundedSource

A is a source of data that you know is finite, such as a static collection of log files, or a database table. The capabilities are:

  • - your runner should call this to get desired initial parallelism (but you can often steal work later)
  • - self explanatory
  • - call this to start reading elements; it is an enhanced iterator that also provides:
  • timestamps to associate with each element read
  • for dynamic splitting to enable work stealing, and other methods to support it - see the Beam blog post on dynamic work rebalancing

The does not report a watermark currently. Most of the time, reading from a bounded source can be parallelized in ways that result in utterly out-of-order data, so a watermark is not terribly useful. Thus the watermark for the output from a bounded read should remain at the minimum timestamp throughout reading (otherwise data might get dropped) and advance to the maximum timestamp when all data is exhausted.

Implementing the Flatten primitive

This one is easy - take as input a finite set of and outputs their bag union, keeping windows intact.

For this operation to make sense, it is the SDK’s responsibility to make sure the windowing strategies are compatible.

Also note that there is no requirement that the coders for all the be the same. If your runner wants to require that (to avoid tedious re-encoding) you have to enforce it yourself. Or you could just implement the fast path as an optimization.

Special mention: the Combine composite

A composite transform that is almost always treated specially by a runner is (per key), which applies an associative and commutative operator to the elements of a . This composite is not a primitive. It is implemented in terms of and , so your runner will work without treating it - but it does carry additional information that you probably want to use for optimizations: the associative-commutative operator, known as a .

Working with pipelines

When you receive a pipeline from a user, you will need to translate it. This is a tour of the APIs that you’ll use to do it.

Traversing a pipeline

Something you will likely do is to traverse a pipeline, probably to translate it into primitives for your engine. The general pattern is to write a visitor that builds a job specification as it walks the graph of .

The entry point for this in Java is and in Python. See the generated documentation for details.

Altering a pipeline

Often, the best way to keep your translator simple will be to alter the pipeline prior to translation. Some alterations you might perform:

  • Elaboration of a Beam primitive into a composite transform that uses multiple runner-specific primitives
  • Optimization of a Beam composite into a specialized primitive for your runner
  • Replacement of a Beam composite with a different expansion more suitable for your runner

The Java SDK and the “runners core construction” library (the artifact is and the namespaces is ) contain helper code for this sort of work. In Python, support code is still under development.

All pipeline alteration is done via method. A is a pair of a to select transforms for replacement and a to produce the replacement. All that have been needed by runners to date are provided. Examples include: matching a specific class, matching a where the uses state or timers, etc.

Testing your runner

The Beam Java SDK and Python SDK have suites of runner validation tests. The configuration may evolve faster than this document, so check the configuration of other Beam runners. But be aware that we have tests and you can use them very easily! To enable these tests in a Java-based runner using Gradle, you scan the dependencies of the SDK for tests with the JUnit category .

Enabling these tests in other languages is unexplored.

Integrating your runner nicely with SDKs

Whether or not your runner is based in the same language as an SDK (such as Java), you will want to provide a shim to invoke it from another SDK if you want the users of that SDK (such as Python) to use it.

Integrating with the Java SDK

Allowing users to pass options to your runner

The mechanism for configuration is , an interface that works completely differently than normal Java objects. Forget what you know, and follow the rules, and will treat you well.

You must implement a sub-interface for your runner with getters and setters with matching names, like so:

You can set up defaults, etc. See the javadoc for details. When your runner is instantiated with a object, you access your interface by .

To make these options available on the command line, you register your options with a . It is easy if you use :

Registering your runner with SDKs for command line use

To make your runner available on the command line, you register your options with a . It is easy if you use :

Integrating with the Python SDK

In the Python SDK the registration of the code is not automatic. So there are few things to keep in mind when creating a new runner.

Any dependencies on packages for the new runner should be options so create a new target in in that is needed for the new runner.

All runner code should go in it’s own package in directory.

Register the new runner in the function of so that the partial name is matched with the correct class to be used.

Writing an SDK-independent runner

There are two aspects to making your runner SDK-independent, able to run pipelines written in other languages: The Fn API and the Runner API.

The Fn API

Design documents:

To run a user’s pipeline, you need to be able to invoke their UDFs. The Fn API is an RPC interface for the standard UDFs of Beam, implemented using protocol buffers over gRPC.

The Fn API includes:

  • APIs for registering a subgraph of UDFs
  • APIs for streaming elements of a bundle
  • Shared data formats (key-value pairs, timestamps, iterables, etc)

You are fully welcome to also use the SDK for your language for utility code, or provide optimized implementations of bundle processing for same-language UDFs.

The Runner API

The Runner API is an SDK-independent schema for a pipeline along with RPC interfaces for launching a pipeline and checking the status of a job. The RPC interfaces are still in development so for now we focus on the SDK-agnostic representation of a pipeline. By examining a pipeline only through Runner API interfaces, you remove your runner’s dependence on the SDK for its language for pipeline analysis and job translation.

To execute such an SDK-independent pipeline, you will need to support the Fn API. UDFs are embedded in the pipeline as a specification of the function (often just opaque serialized bytes for a particular language) plus a specification of an environment that can execute it (essentially a particular SDK). So far, this specification is expected to be a URI for a Docker container hosting the SDK’s Fn API harness.

You are fully welcome to also use the SDK for your language, which may offer useful utility code.

The language-independent definition of a pipeline is described via a protocol buffers schema, covered below for reference. But your runner should not directly manipulate protobuf messages. Instead, the Beam codebase provides utilities for working with pipelines so that you don’t need to be aware of whether or not the pipeline has ever been serialized or transmitted, or what language it may have been written in to begin with.

Java

If your runner is Java-based, the tools to interact with pipelines in an SDK-agnostic manner are in the artifact, in the namespace. The utilities are named consistently, like so:

  • - registry of known transforms and standard URNs
  • - utilities for working with in a language-independent manner
  • - same for
  • - same for
  • - same for windowing strategies
  • - same for coders
  • … etc, etc …

By inspecting transforms only through these classes, your runner will not depend on the particulars of the Java SDK.

The Runner API protos

The Runner API refers to a specific manifestation of the concepts in the Beam model, as a protocol buffers schema. Even though you should not manipulate these messages directly, it can be helpful to know the canonical data that makes up a pipeline.

Most of the API is exactly the same as the high-level description; you can get started implementing a runner without understanding all the low-level details.

The most important takeaway of the Runner API for you is that it is a language-independent definition of a Beam pipeline. You will probably always interact via a particular SDK’s support code wrapping these definitions with sensible idiomatic APIs, but always be aware that this is the specification and any other data is not necessarily inherent to the pipeline, but may be SDK-specific enrichments (or bugs!).

The UDFs in the pipeline may be written for any Beam SDK, or even multiple in the same pipeline. So this is where we will start, taking a bottom-up approach to understanding the protocol buffers definitions for UDFs before going back to the higher-level, mostly obvious, record definitions.

proto

The heart of cross-language portability is the . This is a language-independent specification of a function, in the usual programming sense that includes side effects, etc.

A includes a URN identifying the function as well as an arbitrary fixed parameter. For example the (hypothetical) “max” CombineFn might have the URN and a parameter that indicates by what comparison to take the max.

For most UDFs in a pipeline constructed using a particular language’s SDK, the URN will indicate that the SDK must interpret it, for example or . The parameter will contain serialized code, such as a Java-serialized or a Python pickled .

A is not only for UDFs. It is just a generic way to name/specify any function. It is also used as the specification for a . But when used in a it describes a function from to and cannot be specific to an SDK because the runner is in charge of evaluating transforms and producing .

proto

When a represents a UDF, in general only the SDK that serialized it will be guaranteed to understand it. So in that case, it will always come with an environment that can understand and execute the function. This is represented by the .

In the Runner API, many objects are stored by reference. Here in the is a pointer, local to the pipeline and just made up by the SDK that serialized it, that can be dereferenced to yield the actual environment proto.

Thus far, an environment is expected to be a Docker container specification for an SDK harness that can execute the specified UDF.

Primitive transform payload protos

The payload for the primitive transforms are just proto serializations of their specifications. Rather than reproduce their full code here, I will just highlight the important pieces to show how they fit together.

It is worth emphasizing again that while you probably will not interact directly with these payloads, they are the only data that is inherently part of the transform.

proto

A transform carries its in an and then provides language-independent specifications for its other features - side inputs, state declarations, timer declarations, etc.

proto

A transform carries an for its UDF.

proto

A transform carries an for its UDF. It is part of the Fn API that the runner passes this UDF along and tells the SDK harness to use it to assign windows (as opposed to merging).

proto

is not a primitive. But non-primitives are perfectly able to carry additional information for better optimization. The most important thing that a transform carries is the in an record. In order to effectively carry out the optimizations desired, it is also necessary to know the coder for intermediate accumulations, so it also carries a reference to this coder.

proto

A is a function from to . This is represented in the proto using a FunctionSpec. Note that this is not an , since it is the runner that observes these. They will never be passed back to an SDK harness; they do not represent a UDF.

A may have subtransforms if it is a composite, in which case the may be omitted since the subtransforms define its behavior.

The input and output are unordered and referred to by a local name. The SDK decides what this name is, since it will likely be embedded in serialized UDFs.

proto

A just stores a coder, windowing strategy, and whether or not it is bounded.

proto

This is a very interesting proto. A coder is a parameterized function that may only be understood by a particular SDK, hence an , but also may have component coders that fully define it. For example, a is only a meta-format, while is a fully specified format.

The Runner API RPCs

While your language’s SDK will probably insulate you from touching the Runner API protos directly, you may need to implement adapters for your runner, to expose it to another language. So this section covers proto that you will possibly interact with quite directly.

The specific manner in which the existing runner method calls will be expressed as RPCs is not implemented as proto yet. This RPC layer is to enable, for example, building a pipeline using the Python SDK and launching it on a runner that is written in Java. It is expected that a small Python shim will communicate with a Java process or service hosting the Runner API.

The RPCs themselves will necessarily follow the existing APIs of PipelineRunner and PipelineResult, but altered to be the minimal backend channel, versus a rich and convenient API.

RPC

This will take the same form, but will have to be serialized to JSON (or a proto ) and passed along.

aka “Job API”

The two core pieces of functionality in this API today are getting the state of a job and canceling the job. It is very much likely to evolve, for example to be generalized to support draining a job (stop reading input and let watermarks go to infinity). Today, verifying our test framework benefits (but does not depend upon wholly) querying metrics over this channel.

Last updated on 2021/02/05

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Apache Beam Overview

Apache Beam is an open source, unified model for defining both batch and streaming data-parallel processing pipelines. Using one of the open source Beam SDKs, you build a program that defines the pipeline. The pipeline is then executed by one of Beam’s supported distributed processing back-ends, which include Apache Flink, Apache Spark, and Google Cloud Dataflow.

Beam is particularly useful for embarrassingly parallel data processing tasks, in which the problem can be decomposed into many smaller bundles of data that can be processed independently and in parallel. You can also use Beam for Extract, Transform, and Load (ETL) tasks and pure data integration. These tasks are useful for moving data between different storage media and data sources, transforming data into a more desirable format, or loading data onto a new system.

Apache Beam SDKs

The Beam SDKs provide a unified programming model that can represent and transform data sets of any size, whether the input is a finite data set from a batch data source, or an infinite data set from a streaming data source. The Beam SDKs use the same classes to represent both bounded and unbounded data, and the same transforms to operate on that data. You use the Beam SDK of your choice to build a program that defines your data processing pipeline.

Beam currently supports the following language-specific SDKs:

A Scala Scala logo interface is also available as Scio.

Apache Beam Pipeline Runners

The Beam Pipeline Runners translate the data processing pipeline you define with your Beam program into the API compatible with the distributed processing back-end of your choice. When you run your Beam program, you’ll need to specify an appropriate runner for the back-end where you want to execute your pipeline.

Beam currently supports the following runners:

Note: You can always execute your pipeline locally for testing and debugging purposes.

Get Started

Get started using Beam for your data processing tasks.

If you already know Apache Spark, check our Getting started from Apache Spark page.

  1. Take the Tour of Beam as an online interactive learning experience.

  2. Follow the Quickstart for the Java SDK, the Python SDK, or the Go SDK.

  3. See the WordCount Examples Walkthrough for examples that introduce various features of the SDKs.

  4. Take a self-paced tour through our Learning Resources.

  5. Dive into the Documentation section for in-depth concepts and reference materials for the Beam model, SDKs, and runners.

Contribute

Beam is an Apache Software Foundation project, available under the Apache v2 license. Beam is an open source community and contributions are greatly appreciated! If you’d like to contribute, please see the Contribute section.

Last updated on 2021/02/24

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Beam Capability Matrix

Apache Beam provides a portable API layer for building sophisticated data-parallel processing pipelines that may be executed across a diversity of execution engines, or runners. The core concepts of this layer are based upon the Beam Model (formerly referred to as the Dataflow Model), and implemented to varying degrees in each Beam runner. To help clarify the capabilities of individual runners, we’ve created the capability matrix below.

Individual capabilities have been grouped by their corresponding What / Where / When / How question:

  • What results are being calculated?
  • Where in event time?
  • When in processing time?
  • How do refinements of results relate?

For more details on the What / Where / When / How breakdown of concepts, we recommend reading through the Streaming 102 post on O’Reilly Radar.

Note that in the future, we intend to add additional tables beyond the current set, for things like runtime characterstics (e.g. at-least-once vs exactly-once), performance, etc.

How to read the tables
Tools we are comparing
PropertiesDoes this tool have this property?Yes/Partially/No
What do those signs mean?

What is being computed?

ParDo
GroupByKey
Flatten
Combine
Composite Transforms
Side Inputs
Source API
Metrics
Stateful Processing
~
~
~
~
~
~
~
~
~
~
~
~
~
~
~
~
~
~
~
~
~
~
~
~
~
~
SEE DETAILS AND FULL VERSION HERE.

Bounded Splittable DoFn Support Status

Base
Side Inputs
Splittable DoFn Initiated Checkpointing
Dynamic Splitting
Bundle Finalization
~
~
~
~
~
~
~
~
SEE DETAILS AND FULL VERSION HERE.

Unbounded Splittable DoFn Support Status

Base
Side Inputs
Splittable DoFn Initiated Checkpointing
Dynamic Splitting
Bundle Finalization
~
~
~
~
SEE DETAILS AND FULL VERSION HERE.

Where in event time?

Global windows
Fixed windows
Sliding windows
Session windows
Custom windows
Custom merging windows
Timestamp control
~
~
~
~
~
~
~
SEE DETAILS AND FULL VERSION HERE.

When in processing time?

Configurable triggering
Event-time triggers
Processing-time triggers
Count triggers
Composite triggers
Allowed lateness
Timers
~
~
~
~
~
~
~
~
~
~
~
~
~
SEE DETAILS AND FULL VERSION HERE.

How do refinements relate?

~
SEE DETAILS AND FULL VERSION HERE.

Additional common features not yet part of the Beam model

~
~
~
~
~
SEE DETAILS AND FULL VERSION HERE.

Last updated on 2021/02/05

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Using the Google Cloud Dataflow Runner

The Google Cloud Dataflow Runner uses the Cloud Dataflow managed service. When you run your pipeline with the Cloud Dataflow service, the runner uploads your executable code and dependencies to a Google Cloud Storage bucket and creates a Cloud Dataflow job, which executes your pipeline on managed resources in Google Cloud Platform.

The Cloud Dataflow Runner and service are suitable for large scale, continuous jobs, and provide:

The Beam Capability Matrix documents the supported capabilities of the Cloud Dataflow Runner.

Cloud Dataflow Runner prerequisites and setup

To use the Cloud Dataflow Runner, you must complete the setup in the Before you begin section of the Cloud Dataflow quickstart for your chosen language.

  1. Select or create a Google Cloud Platform Console project.
  2. Enable billing for your project.
  3. Enable the required Google Cloud APIs: Cloud Dataflow, Compute Engine, Stackdriver Logging, Cloud Storage, Cloud Storage JSON, and Cloud Resource Manager. You may need to enable additional APIs (such as BigQuery, Cloud Pub/Sub, or Cloud Datastore) if you use them in your pipeline code.
  4. Authenticate with Google Cloud Platform.
  5. Install the Google Cloud SDK.
  6. Create a Cloud Storage bucket.

Specify your dependency

When using Java, you must specify your dependency on the Cloud Dataflow Runner in your .

This section is not applicable to the Beam SDK for Python.

Self executing JAR

This section is not applicable to the Beam SDK for Python.

In some cases, such as starting a pipeline using a scheduler such as Apache AirFlow, you must have a self-contained application. You can pack a self-executing JAR by explicitly adding the following dependency on the Project section of your pom.xml, in addition to the adding existing dependency shown in the previous section.

Then, add the mainClass name in the Maven JAR plugin.

After running , run and you should see (assuming your artifactId is and the version is 1.0.0) the following output.

To run the self-executing JAR on Cloud Dataflow, use the following command.

Pipeline options for the Cloud Dataflow Runner

When executing your pipeline with the Cloud Dataflow Runner (Java), consider these common pipeline options.When executing your pipeline with the Cloud Dataflow Runner (Python), consider these common pipeline options.

FieldDescriptionDefault Value
The pipeline runner to use. This option allows you to determine the pipeline runner at runtime.Set to or to run on the Cloud Dataflow Service.
The project ID for your Google Cloud Project.If not set, defaults to the default project in the current environment. The default project is set via .
The Google Compute Engine region to create the job.If not set, defaults to the default region in the current environment. The default region is set via .
Whether streaming mode is enabled or disabled; if enabled. Set to if running pipelines with unbounded s.
Optional.Required. Path for temporary files. Must be a valid Google Cloud Storage URL that begins with . If set, is used as the default value for .No default value.
Cloud Storage bucket path for temporary files. Must be a valid Cloud Storage URL that begins with .If not set, defaults to the value of , provided that is a valid Cloud Storage URL. If is not a valid Cloud Storage URL, you must set .
Optional. Cloud Storage bucket path for staging your binary and any temporary files. Must be a valid Cloud Storage URL that begins with .If not set, defaults to a staging directory within .If not set, defaults to a staging directory within .
Save the main session state so that pickled functions and classes defined in (e.g. interactive session) can be unpickled. Some workflows do not need the session state if, for instance, all of their functions/classes are defined in proper modules (not ) and the modules are importable in the worker.
Override the default location from where the Beam SDK is downloaded. This value can be a URL, a Cloud Storage path, or a local path to an SDK tarball. Workflow submissions will download or copy the SDK tarball from this location. If set to the string , a standard SDK location is used. If empty, no SDK is copied.

See the reference documentation for the DataflowPipelineOptions interface (and any subinterfaces) for additional pipeline configuration options.

Additional information and caveats

Monitoring your job

While your pipeline executes, you can monitor the job’s progress, view details on execution, and receive updates on the pipeline’s results by using the Dataflow Monitoring Interface or the Dataflow Command-line Interface.

Blocking Execution

To block until your job completes, call on the returned from . The Cloud Dataflow Runner prints job status updates and console messages while it waits. While the result is connected to the active job, note that pressing Ctrl+C from the command line does not cancel your job. To cancel the job, you can use the Dataflow Monitoring Interface or the Dataflow Command-line Interface.

Streaming Execution

If your pipeline uses an unbounded data source or sink, you must set the option to .

When using streaming execution, keep the following considerations in mind.

  1. Streaming pipelines do not terminate unless explicitly cancelled by the user. You can cancel your streaming job from the Dataflow Monitoring Interface or with the Dataflow Command-line Interface (gcloud dataflow jobs cancel command).

  2. Streaming jobs use a Google Compute Engine machine type of or higher by default. You must not override this, as is the minimum required machine type for running streaming jobs.

  3. Streaming execution pricing differs from batch execution.

Last updated on 2020/05/15

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Runners apache beam

Apache Beam Documentation

This section provides in-depth conceptual information and reference material for the Beam Model, SDKs, and Runners:

Concepts

Learn about the Beam Programming Model and the concepts common to all Beam SDKs and Runners.

  • Read the Programming Guide, which introduces all the key Beam concepts.
  • Learn about Beam’s execution model to better understand how pipelines execute.
  • Visit Learning Resources for some of our favorite articles and talks about Beam.
  • Visit the glossary to learn the terminology of the Beam programming model.

Pipeline Fundamentals

SDKs

Find status and reference information on all of the available Beam SDKs.

Runners

A Beam Runner runs a Beam pipeline on a specific (often distributed) data processing system.

Available Runners

Choosing a Runner

Beam is designed to enable pipelines to be portable across different runners. However, given every runner has different capabilities, they also have different abilities to implement the core concepts in the Beam model. The Capability Matrix provides a detailed comparison of runner functionality.

Once you have chosen which runner to use, see that runner’s page for more information about any initial runner-specific setup as well as any required or optional for configuring its execution. You may also want to refer back to the Quickstart for Java, Python or Go for instructions on executing the sample WordCount pipeline.

Last updated on 2021/05/04

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Portable Spark Runner: Running Beam Pipelines Written in Python and Go with Spark

Overview

The Apache Flink Runner can be used to execute Beam pipelines using Apache Flink. For execution you can choose between a cluster execution mode (e.g. Yarn/Kubernetes/Mesos) or a local embedded execution mode which is useful for testing pipelines.

The Flink Runner and Flink are suitable for large scale, continuous jobs, and provide:

  • A streaming-first runtime that supports both batch processing and data streaming programs
  • A runtime that supports very high throughput and low event latency at the same time
  • Fault-tolerance with exactly-once processing guarantees
  • Natural back-pressure in streaming programs
  • Custom memory management for efficient and robust switching between in-memory and out-of-core data processing algorithms
  • Integration with YARN and other components of the Apache Hadoop ecosystem

It is important to understand that the Flink Runner comes in two flavors:

  1. The original classic Runner which supports only Java (and other JVM-based languages)
  2. The newer portable Runner which supports Java/Python/Go

You may ask why there are two Runners?

Beam and its Runners originally only supported JVM-based languages (e.g. Java/Scala/Kotlin). Python and Go SDKs were added later on. The architecture of the Runners had to be changed significantly to support executing pipelines written in other languages.

If your applications only use Java, then you should currently go with the classic Runner. Eventually, the portable Runner will replace the classic Runner because it contains the generalized framework for executing Java, Python, Go, and more languages in the future.

If you want to run Python pipelines with Beam on Flink you want to use the portable Runner. For more information on portability, please visit the Portability page.

Consequently, this guide is split into parts to document the classic and the portable functionality of the Flink Runner. In addition, Python provides convenience wrappers to handle the full lifecycle of the runner, and so is further split depending on whether to manage the portability components automatically (recommended) or manually. Please use the switcher below to select the appropriate mode for the Runner:

Prerequisites and Setup

If you want to use the local execution mode with the Flink Runner you don’t have to complete any cluster setup. You can simply run your Beam pipeline. Be sure to set the Runner to .

To use the Flink Runner for executing on a cluster, you have to setup a Flink cluster by following the Flink Setup Quickstart.

Dependencies

You must specify your dependency on the Flink Runner in your or . Use the Beam version and the artifact id from the above table. For example:

You will need Docker to be installed in your execution environment. To run an embedded flink cluster or use the Flink runner for Python < 3.6 you will also need to have java available in your execution environment.

You will need Docker to be installed in your execution environment.

Executing a Beam pipeline on a Flink Cluster

For executing a pipeline on a Flink cluster you need to package your program along with all dependencies in a so-called fat jar. How you do this depends on your build system but if you follow along the Beam Quickstart this is the command that you have to run:

Look for the output JAR of this command in the folder.

The Beam Quickstart Maven project is setup to use the Maven Shade plugin to create a fat jar and the argument makes sure to include the dependency on the Flink Runner.

For running the pipeline the easiest option is to use the command which is part of Flink:

$ bin/flink run -c org.apache.beam.examples.WordCount /path/to/your.jar –runner=FlinkRunner –other-parameters

Alternatively you can also use Maven’s exec command. For example, to execute the WordCount example:

If you have a Flink running on your local machine you can provide for . Otherwise an embedded Flink cluster will be started for the job.

To run a pipeline on Flink, set the runner to and to the master URL of a Flink cluster. In addition, optionally set set to . For example, after starting up a local flink cluster, one could run:

To run on an embedded Flink cluster, simply omit the option and an embedded Flink cluster will be automatically started and shut down for the job.

The optional option may be required as well for older versions of Python.

Starting with Beam 2.18.0, pre-built Flink Job Service Docker images are available at Docker Hub: Flink 1.10, Flink 1.11, Flink 1.12. Flink 1.13.

To run a pipeline on an embedded Flink cluster:

(1) Start the JobService endpoint:

The JobService is the central instance where you submit your Beam pipeline to. The JobService will create a Flink job for the pipeline and execute the job.

(2) Submit the Python pipeline to the above endpoint by using the , set to (this is the default address of the JobService). Optionally set set to . For example:

To run on a separate Flink cluster:

(1) Start a Flink cluster which exposes the Rest interface (e.g. by default).

(2) Start JobService with Flink Rest endpoint: .

(3) Submit the pipeline as above.

Note that is only intended for local testing, and will not work on remote clusters. See here for details.

Additional information and caveats

Monitoring your job

You can monitor a running Flink job using the Flink JobManager Dashboard or its Rest interfaces. By default, this is available at port of the JobManager node. If you have a Flink installation on your local machine that would be . Note: When you use the mode an embedded Flink cluster will be started which does not make a dashboard available.

Streaming Execution

If your pipeline uses an unbounded data source or sink, the Flink Runner will automatically switch to streaming mode. You can enforce streaming mode by using the flag.

Note: The Runner will print a warning message when unbounded sources are used and checkpointing is not enabled. Many sources like rely on their checkpoints to be acknowledged which can only be done when checkpointing is enabled for the . To enable checkpointing, please set to the desired checkpointing interval in milliseconds.

Pipeline options for the Flink Runner

When executing your pipeline with the Flink Runner, you can set these pipeline options.

The following list of Flink-specific pipeline options is generated automatically from the FlinkPipelineOptions reference class:

Flag indicating whether non restored state is allowed if the savepoint contains state for an operator that is no longer part of the pipeline.Default:
Flag indicating whether auto-balance sharding for WriteFiles transform should be enabled. This might prove useful in streaming use-case, where pipeline needs to write quite many events into files, typically divided into N shards. Default behavior on Flink would be, that some workers will receive more shards to take care of than others. This cause workers to go out of balance in terms of processing backlog and memory usage. Enabling this feature will make shards to be spread evenly among available workers in improve throughput and memory usage stability.Default:
The interval in milliseconds for automatic watermark emission.
The maximum time in milliseconds that a checkpoint may take before being discarded.Default:
The interval in milliseconds at which to trigger checkpoints of the running pipeline. Default: No checkpointing.Default:
The checkpointing mode that defines consistency guarantee.Default:
Disable Beam metrics in Flink RunnerDefault:
Flink mode for data exchange of batch pipelines. Reference {@link org.apache.flink.api.common.ExecutionMode}. Set this to BATCH_FORCED if pipelines get blocked, see https://issues.apache.org/jira/browse/FLINK-10672Default:
Sets the delay in milliseconds between executions. A value of {@code -1} indicates that the default value should be used.Default:
Enables or disables externalized checkpoints. Works in conjunction with CheckpointingIntervalDefault:
Sets the expected behaviour for tasks in case that they encounter an error in their checkpointing procedure. If this is set to true, the task will fail on checkpointing error. If this is set to false, the task will only decline a the checkpoint and continue running.Default:
Remove unneeded deep copy between operators. See https://issues.apache.org/jira/browse/BEAM-11146Default:
Jar-Files to send to all workers and put on the classpath. The default value is all files from the classpath.
If set, finishes the current bundle and flushes all output before checkpointing the state of the operators. By default, starts checkpointing immediately and buffers any remaining bundle output as part of the checkpoint. The setting may affect the checkpoint alignment.Default:
Address of the Flink Master where the Pipeline should be executed. Can either be of the form "host:port" or one of the special values [local], [collection] or [auto].Default:
Interval in milliseconds for sending latency tracking marks from the sources to the sinks. Interval value <= 0 disables the feature.Default:
The maximum number of elements in a bundle.Default:
The maximum time to wait before finalising a bundle (in milliseconds).Default:
The pipeline wide maximum degree of parallelism to be used. The maximum parallelism specifies the upper limit for dynamic scaling and the number of key groups used for partitioned state.Default:
The minimal pause in milliseconds before the next checkpoint is triggered.Default:
The maximum number of concurrent checkpoints. Defaults to 1 (=no concurrent checkpoints).Default:
Sets the number of times that failed tasks are re-executed. A value of zero effectively disables fault tolerance. A value of -1 indicates that the system default value (as defined in the configuration) should be used.Default:
Sets the behavior of reusing objects.Default:
The degree of parallelism to be used when distributing operations onto workers. If the parallelism is not set, the configured Flink default is used, or 1 if none can be found.Default:
Flag indicating whether result of GBK needs to be re-iterable. Re-iterable result implies that all values for a single key must fit in memory as we currently do not support spilling to disk.Default:
If not null, reports the checkpoint duration of each ParDo stage in the provided metric namespace.
Sets the behavior of externalized checkpoints on cancellation.Default:
Savepoint restore path. If specified, restores the streaming pipeline from the provided path.
Shuts down sources which have been idle for the configured time of milliseconds. Once a source has been shut down, checkpointing is not possible anymore. Shutting down the sources eventually leads to pipeline shutdown (=Flink job finishes) once all input has been processed. Unless explicitly set, this will default to Long.MAX_VALUE when checkpointing is enabled and to 0 when checkpointing is disabled. See https://issues.apache.org/jira/browse/FLINK-2491 for progress on this issue.Default:
State backend to store Beam's state. Use 'rocksdb' or 'filesystem'.
Sets the state backend factory to use in streaming mode. Defaults to the flink cluster's state.backend configuration.
State backend path to persist state backend data. Used to initialize state backend.
Flag indicating whether non restored state is allowed if the savepoint contains state for an operator that is no longer part of the pipeline.Default:
Flag indicating whether auto-balance sharding for WriteFiles transform should be enabled. This might prove useful in streaming use-case, where pipeline needs to write quite many events into files, typically divided into N shards. Default behavior on Flink would be, that some workers will receive more shards to take care of than others. This cause workers to go out of balance in terms of processing backlog and memory usage. Enabling this feature will make shards to be spread evenly among available workers in improve throughput and memory usage stability.Default:
The interval in milliseconds for automatic watermark emission.
The maximum time in milliseconds that a checkpoint may take before being discarded.Default:
The interval in milliseconds at which to trigger checkpoints of the running pipeline. Default: No checkpointing.Default:
The checkpointing mode that defines consistency guarantee.Default:
Disable Beam metrics in Flink RunnerDefault:
Flink mode for data exchange of batch pipelines. Reference {@link org.apache.flink.api.common.ExecutionMode}. Set this to BATCH_FORCED if pipelines get blocked, see https://issues.apache.org/jira/browse/FLINK-10672Default:
Sets the delay in milliseconds between executions. A value of {@code -1} indicates that the default value should be used.Default:
Enables or disables externalized checkpoints. Works in conjunction with CheckpointingIntervalDefault:
Sets the expected behaviour for tasks in case that they encounter an error in their checkpointing procedure. If this is set to true, the task will fail on checkpointing error. If this is set to false, the task will only decline a the checkpoint and continue running.Default:
Remove unneeded deep copy between operators. See https://issues.apache.org/jira/browse/BEAM-11146Default:
Jar-Files to send to all workers and put on the classpath. The default value is all files from the classpath.
If set, finishes the current bundle and flushes all output before checkpointing the state of the operators. By default, starts checkpointing immediately and buffers any remaining bundle output as part of the checkpoint. The setting may affect the checkpoint alignment.Default:
Address of the Flink Master where the Pipeline should be executed. Can either be of the form "host:port" or one of the special values [local], [collection] or [auto].Default:
Interval in milliseconds for sending latency tracking marks from the sources to the sinks. Interval value <= 0 disables the feature.Default:
The maximum number of elements in a bundle.Default:
The maximum time to wait before finalising a bundle (in milliseconds).Default:
The pipeline wide maximum degree of parallelism to be used. The maximum parallelism specifies the upper limit for dynamic scaling and the number of key groups used for partitioned state.Default:
The minimal pause in milliseconds before the next checkpoint is triggered.Default:
The maximum number of concurrent checkpoints. Defaults to 1 (=no concurrent checkpoints).Default:
Sets the number of times that failed tasks are re-executed. A value of zero effectively disables fault tolerance. A value of -1 indicates that the system default value (as defined in the configuration) should be used.Default:
Sets the behavior of reusing objects.Default:
The degree of parallelism to be used when distributing operations onto workers. If the parallelism is not set, the configured Flink default is used, or 1 if none can be found.Default:
Flag indicating whether result of GBK needs to be re-iterable. Re-iterable result implies that all values for a single key must fit in memory as we currently do not support spilling to disk.Default:
If not null, reports the checkpoint duration of each ParDo stage in the provided metric namespace.
Sets the behavior of externalized checkpoints on cancellation.Default:
Savepoint restore path. If specified, restores the streaming pipeline from the provided path.
Shuts down sources which have been idle for the configured time of milliseconds. Once a source has been shut down, checkpointing is not possible anymore. Shutting down the sources eventually leads to pipeline shutdown (=Flink job finishes) once all input has been processed. Unless explicitly set, this will default to Long.MAX_VALUE when checkpointing is enabled and to 0 when checkpointing is disabled. See https://issues.apache.org/jira/browse/FLINK-2491 for progress on this issue.Default:
State backend to store Beam's state. Use 'rocksdb' or 'filesystem'.
Sets the state backend factory to use in streaming mode. Defaults to the flink cluster's state.backend configuration.
State backend path to persist state backend data. Used to initialize state backend.

For general Beam pipeline options see the PipelineOptions reference.

Flink Version Compatibility

The Flink cluster version has to match the minor version used by the FlinkRunner. The minor version is the first two numbers in the version string, e.g. in the minor version is .

We try to track the latest version of Apache Flink at the time of the Beam release. A Flink version is supported by Beam for the time it is supported by the Flink community. The Flink community supports the last two minor versions. When support for a Flink version is dropped, it may be deprecated and removed also from Beam. To find out which version of Flink is compatible with Beam please see the table below:

Beam VersionFlink VersionArtifact Id
≥ 2.31.01.13.x *beam-runners-flink-1.13
1.12.x *beam-runners-flink-1.12
1.11.x *beam-runners-flink-1.11
2.30.01.12.x *beam-runners-flink-1.12
1.11.x *beam-runners-flink-1.11
1.10.xbeam-runners-flink-1.10
2.27.0 - 2.29.01.12.x *beam-runners-flink-1.12
1.11.x *beam-runners-flink-1.11
1.10.xbeam-runners-flink-1.10
1.9.xbeam-runners-flink-1.9
1.8.xbeam-runners-flink-1.8
2.25.0 - 2.26.01.11.x *beam-runners-flink-1.11
1.10.xbeam-runners-flink-1.10
1.9.xbeam-runners-flink-1.9
1.8.xbeam-runners-flink-1.8
2.21.0 - 2.24.01.10.xbeam-runners-flink-1.10
1.9.xbeam-runners-flink-1.9
1.8.xbeam-runners-flink-1.8
2.17.0 - 2.20.01.9.xbeam-runners-flink-1.9
1.8.xbeam-runners-flink-1.8
1.7.xbeam-runners-flink-1.7
2.13.0 - 2.16.01.8.xbeam-runners-flink-1.8
1.7.xbeam-runners-flink-1.7
1.6.xbeam-runners-flink-1.6
1.5.xbeam-runners-flink_2.11
2.10.0 - 2.16.01.7.xbeam-runners-flink-1.7
1.6.xbeam-runners-flink-1.6
1.5.xbeam-runners-flink_2.11
2.9.01.5.xbeam-runners-flink_2.11
2.8.0
2.7.0
2.6.0
2.5.01.4.x with Scala 2.11beam-runners-flink_2.11
2.4.0
2.3.0
2.2.01.3.x with Scala 2.10beam-runners-flink_2.10
2.1.x
2.0.01.2.x with Scala 2.10beam-runners-flink_2.10

* This version does not have a published docker image for the Flink Job Service.

For retrieving the right Flink version, see the Flink downloads page.

For more information, the Flink Documentation can be helpful.

Beam Capability

The Beam Capability Matrix documents the capabilities of the classic Flink Runner.

The Portable Capability Matrix documents the capabilities of the portable Flink Runner.

Last updated on 2021/07/09

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