Project: Beam (App Definition and Development / Streaming & Messaging)
File: runner-guide.md

title: "Runner Authoring Guide" <!-- Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> 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: {{< toc >}} 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 `ParDo` that uses state or timers may require key partitioning, a `GroupByKey` with speculative triggering may require a more costly or complex implementation. 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 on the Beam site so you can tell users what you support. When you receive a `Pipeline`, you should traverse it and determine whether or not you can execute each `DoFn` that you find. If you cannot execute some `DoFn` 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 `UnsupportedOperationException`. The Runner API RPCs will make this explicit, for cross-language portability. `Impulse` is a PTransform that takes no inputs and produces exactly one output during the lifetime of the pipeline which should be the empty bytes in the global window with the minimum timestamp. This has the encoded value of `7f df 3b 64 5a 1c ac 09 00 00 00 01 0f 00` when encoded with the standard windowed value coder. Though `Impulse` is generally not invoked by a user, it is the only root primitive operation, and other root operations (like `Read`s and `Create`) are composite operations constructed from an `Impulse` followed by a series of (possibly Splittable) `ParDo`s. The `ParDo` primitive describes element-wise transformation for a `PCollection`. `ParDo` is the most complex primitive, because it is where any per-element processing is described. In addition to very simple operations like standard `map` or `flatMap` from functional programming, `ParDo` also supports multiple outputs, side inputs, initialization, flushing, teardown, and stateful processing. The UDF that is applied to each element is called a `DoFn`. The exact APIs for a `DoFn` can vary per language/SDK but generally follow the same pattern, so we can discuss it with

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. Generally, rather than applying a series of `ParDo`s one at a time over the entire input data set, it is more efficient to fuse several `ParDo`s together in a single executable stage that consists of a whole series (in general, a DAG) of mapping operations. In addition to `ParDo`s, windowing operations, local (pre- or post-GBK) combining operations, and other mapping operations may be fused into these stages as well. As DoFns may execute code in a different language, or requiring a different environment, than the runner itself, Beam provides the ability to call these in a cross-process way. This is the crux of the , for which more detail can be found below. It is, however, perfectly acceptable for a runner to invoke this user code in process (for simplicity or efficiency) when the environments are compatible. For correctness, a `DoFn` should represent an element-wise function, but in most SDKS this 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. A bundle is the unit of commitment in Beam. If an error is encountered while processing a bundle, all the prior outputs of that bundle (including any modifications to state or timers) must be discarded by the runner and the entire bundle retried. Upon successful completion of a bundle, its outputs, together with any state/timer modifications and watermark updates, must be committed atomically. `DoFns` in many SDKS have several methods such as `setup`, `start_bundle`, `finish_bundle`, `teardown`, etc. in addition to the standard, element-wise `process` calls. Generally proper invocation of should be handled for you when invoking one or more `DoFn`s from the standard bundle processors (either via the FnAPI or directly using a BundleProcessor ( ()). SDK-independent runners should never have to worry about these details directly. _Main design document: _ A side input is a global view of a window of a `PCollection`. This distinguishes it from the main input, which is processed one element at a time. The SDK/user prepares a `PCollection` adequately, the runner materializes it, and then the runner feeds it to the `DoFn`. Unlike main input data, which is pushed by the runner to the `ParDo` (generally via the FnApi Data channel), side input data is pulled by the `ParDo` from the runner (generally over the FnAPI State channel). A side input is accessed via a specific `access_pattern`. There are currently two access patterns enumerated in the `StandardSideInputTypes` proto: `beam:side_input:iterable:v1` which indicates the runner must return all values in a PCollection corresponding to a specific window and `beam:side_input:multimap:v1` which indicates the runner must return all values corresponding to a specific key and window. Being able to serve these access patterns efficiently may influence how a runner materializes this PCollection. SideInputs can be detected by looking at the `side_inputs` map in the `ParDoPayload` of `ParDo`

The `ParDo` operation itself is responsible for invoking the `windowmappingfn` (before invoking the runner) and `view_fn` (on the runner-returned values), so the runner need not concern itself with these fields. 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 or the watermark has advances sufficiently such that we can be sure there will be no data for that window. The is an example of implementing this. Main design document: When a `ParDo` includes state and timers, its execution on your runner is usually very different. In particular, the state must be persisted when the bundle completes and retrieved for future bundles. Timers that are set must also be injected into future bundles as the watermark advances sufficiently. State and timers are partitioned per key and window, that is, a `DoFn` processing a given key must have a consistent view of the state and timers across all elements that share this key. You may need or want to explicitly shuffle data to support this. Once the watermark has passed the end of the window (plus an allowance for allowed lateness, if any), state associated with this window can be dropped. State setting and retrieval is performed on the FnAPI State channel, whereas timer setting and firing happens on the FnAPI Data channel. Main design document: Splittable `DoFn` is a generalization of `ParDo` that is useful for high-fanout mappings that can be done in parallel. The prototypical example of such an operation is reading from a file, where a single file name (as an input element) can be mapped to all the elements contained in that file. The `DoFn` is considered splittable in the sense that an element representing, say, a single file can be split (e.g. into ranges of that file) to be processed (e.g. read) by different workers. The full power of this primitive is in the fact that these splits can happen dynamically rather than just statically (i.e. ahead of time) avoiding the problem of over- or undersplitting. A full explanation of Splittable `DoFn` is out of scope for this doc, but here is a brief overview as it pertains to its execution. A Splittable `DoFn` can participate in the dynamic splitting protocol by splitting within an element as well as between elements. Dynamic splitting is triggered by the runner issuing `ProcessBundleSplitRequest` messages on the control channel. The SDK will commit to process just a portion of the indicated element and return a description of the remainder (i.e. the unprocessed portion) to the runner in the `ProcessBundleSplitResponse` to be scheduled by the runner (e.g. on a different worker or as part of a different bundle). A Splittable `DoFn` can also initiate its own spitting, indicating it has processed an element as far as it can for the moment (e.g. when tailing a file) but more remains. These most often occur when reading unbounded sources. In this case a set of elements representing the deferred work are passed back in the `residual_roots` field of the `ProcessBundleResponse`. At a future time, the runner must re-invoke these same operations with the elements given in `residual_roots`. The `GroupByKey` operation (sometimes called GBK for short) groups a `PCollection` of key-value pairs by key and window, emitting results according to the `PCollection`'s triggering configuration. It is quite a bit more elaborate than simply colocating elements with the same key, and uses many fields from the `PCollection`'s windowing strategy. For both the key and window, your runner sees them as "just

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 across all SDKS. See either in Java or in Python for documentation on the binary format. As well as grouping by key, your runner must group elements by their window. A `WindowFn` 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 `WindowFn` during grouping. The Java and Python codebases includes support code for a particularly common way of implementing the full `GroupByKey` operation: first group the keys, and then group by window. For merging windows, this is essentially required, since merging is per key. Often presenting the set of values in timestamp order can allow more efficient grouping of these values into their final windows. _Main design document: _ A window is expired in a `PCollection` if the watermark of the input PCollection has exceeded the end of the window by at least the input `PCollection`'s allowed lateness. Data for an expired window can be dropped any time and should be dropped at a `GroupByKey`. If you are using `GroupAlsoByWindow`, then just before executing this transform. You may shuffle less data if you drop data prior to `GroupByKeyOnly`, but should only safely be done for non-merging windows, as a window that appears expired may merge to become not expired. _Main design document: _ The input `PCollection`'s trigger and accumulation mode specify when and how outputs should be emitted from the `GroupByKey` operation. In Java, there is a lot of support code for executing triggers in the `GroupAlsoByWindow` implementations, `ReduceFnRunner` (legacy name), and `TriggerStateMachine`, which is an obvious way of implementing all triggers as an event-driven machine over elements and timers. In Python this is supported by the classes. When an aggregated output is produced from multiple inputs, the `GroupByKey` 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 `TimestampCombiner`, which can either select the minimum or maximum of its inputs, or just ignore inputs and choose the end of the window. The window primitive applies a `WindowFn` 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 `PCollection`, but the fully constructed graph that your runner receives will already have a complete windowing strategy for each `PCollection`. 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 `PCollection`. Most runners implement this by fusing these window-altering mappings in with the `DoFns`. Implementation considerations A "window" is just a second grouping key that has a "maximum timestamp". It can be any arbitrary user-defined type. The `WindowFn` provides the coder for the window type. Beam's support code provides `WindowedValue` which is a compressed representation of an element in multiple

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. We provide coders with these optimizations such as that can be used to reduce the size of serialized data. 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. This one is easy - take as input a finite set of `PCollections` 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 `PCollections` 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. A composite transform that is almost always treated specially by a runner is `CombinePerKey`, which applies an associative and commutative operator to the elements of a `PCollection`. This composite is not a primitive. It is implemented in terms of `ParDo` and `GroupByKey`, 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 `CombineFn`. Generally runners will want to implement this via what is called combiner lifting, where a new operation is placed before the `GroupByKey` that does partial (within-bundle) combining, which often requires a slight modification of what comes after the `GroupByKey` as well. An example of this transformation can be found in the or implementations of this optimization. The resulting pre- and post-`GroupByKey` operations are generally fused in with the `ParDo`s and executed as above. When you receive a pipeline from a user, you will need to translate it. An explanation of how Beam pipelines are represented can be found which compliment the . 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 `ValidatesRunner`. {{< highlight class="no-toggle" >}} task validatesRunner(type: Test) { group = "Verification" description = "Validates the runner" def pipelineOptions = JsonOutput.toJson(["--runner=MyRunner", ... misc test options ...]) systemProperty "beamTestPipelineOptions", pipelineOptions classpath = configurations.validatesRunner testClassesDirs = files(project(":sdks:java:core").sourceSets.test.output.classesDirs) useJUnit { includeCategories 'org.apache.beam.sdk.testing.ValidatesRunner' } } {{< /highlight >}} Enabling these tests in other languages is unexplored. 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. The mechanism for configuration is , an interface that works completely differently than normal Java objects. Forget what you know, and follow the rules, and `PipelineOptions` will treat you

You must implement a sub-interface for your runner with getters and setters with matching names, like so: {{< highlight class="language-java no-toggle" >}} public interface MyRunnerOptions extends PipelineOptions { @Description("The Foo to use with MyRunner") @Required public Foo getMyRequiredFoo(); public void setMyRequiredFoo(Foo newValue); @Description("Enable Baz; on by default") @Default.Boolean(true) public Boolean isBazEnabled(); public void setBazEnabled(Boolean newValue); } {{< /highlight >}} You can set up defaults, etc. See the javadoc for details. When your runner is instantiated with a `PipelineOptions` object, you access your interface by `options.as(MyRunnerOptions.class)`. To make these options available on the command line, you register your options with a `PipelineOptionsRegistrar`. It is easy if you use `@AutoService`: {{< highlight class="language-java no-toggle" >}} @AutoService(PipelineOptionsRegistrar.class) public static class MyOptionsRegistrar implements PipelineOptionsRegistrar { @Override public Iterable<Class<? extends PipelineOptions>> getPipelineOptions() { return ImmutableList.<Class<? extends PipelineOptions>>of(MyRunnerOptions.class); } } {{< /highlight >}} To make your runner available on the command line, you register your options with a `PipelineRunnerRegistrar`. It is easy if you use `@AutoService`: {{< highlight class="language-java no-toggle" >}} @AutoService(PipelineRunnerRegistrar.class) public static class MyRunnerRegistrar implements PipelineRunnerRegistrar { @Override public Iterable<Class<? extends PipelineRunner>> getPipelineRunners() { return ImmutableList.<Class<? extends PipelineRunner>>of(MyRunner.class); } } {{< /highlight >}} 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 `extra_requires` in `setup.py` that is needed for the new runner. All runner code should go in it's own package in `apache_beam/runners` directory. Register the new runner in the `create_runner` function of `runner.py` so that the partial name is matched with the correct class to be used. Python Runners can also be identified (e.g. when passing the runner parameter) by their fully qualified name whether or not they live in the Beam repository. 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. 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 is an SDK-independent schema for a pipeline along with RPC interfaces for launching a pipeline and checking the status of a job. 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 need 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

Java If your runner is Java-based, the tools to interact with pipelines in an SDK-agnostic manner are in the `beam-runners-core-construction-java` artifact, in the `org.apache.beam.sdk.util.construction` namespace. The utilities are named consistently, like so: `PTransformTranslation` - registry of known transforms and standard URNs `ParDoTranslation` - utilities for working with `ParDo` in a language-independent manner `WindowIntoTranslation` - same for `Window` `FlattenTranslation` - same for `Flatten` `WindowingStrategyTranslation` - same for windowing strategies `CoderTranslation` - 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](https://github.com/apache/beam/blob/master/model/pipeline/src/main/proto/org/apache/beam/model/pipeline/v1/beamrunnerapi.proto) 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. The heart of cross-language portability is the `FunctionSpec`. This is a language-independent specification of a function, in the usual programming sense that includes side effects, etc. {{< highlight class="no-toggle" >}} message FunctionSpec { string urn; bytes payload; } {{< /highlight >}} A `FunctionSpec` includes a URN identifying the function as well as an arbitrary fixed parameter. For example the (hypothetical) "max" CombineFn might have the URN `beam:combinefn:max:0.1` 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 `beam:dofn:javasdk:0.1` or `beam:dofn:pythonsdk:0.1`. The parameter will contain serialized code, such as a Java-serialized `DoFn` or a Python pickled `DoFn`. A `FunctionSpec` 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 `PTransform`. But when used in a `PTransform` it describes a function from `PCollection` to `PCollection` and cannot be specific to an SDK because the runner is in charge of evaluating transforms and producing `PCollections`. It goes without saying that not every environment will be able to deserialize every function spec. For this reason `PTransform`s have an `environment_id` parameter that indicates at least one environment that is capable of interpreting the contained URNs. This is a reference to an environment in the environments map of the Pipeline proto and is typically defined by a docker image (possibly with some extra dependencies). There may be other environments that are also capable of doing so, and a runner is free to use them if it has this knowledge. 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

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. A `ParDo` transform carries its `DoFn` in an `SdkFunctionSpec` and then provides language-independent specifications for its other features - side inputs, state declarations, timer declarations, etc. {{< highlight class="no-toggle" >}} message ParDoPayload { FunctionSpec do_fn; map<string, SideInput> side_inputs; map<string, StateSpec> state_specs; map<string, TimerSpec> timer_specs; ... } {{< /highlight >}} `Combine` is not a primitive. But non-primitives are perfectly able to carry additional information for better optimization. The most important thing that a `Combine` transform carries is the `CombineFn` in an `SdkFunctionSpec` 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. {{< highlight class="no-toggle" >}} message CombinePayload { FunctionSpec combine_fn; string accumulatorcoderid; ... } {{< /highlight >}} A `PTransform` is a function from `PCollection` to `PCollection`. This is represented in the proto using a FunctionSpec. {{< highlight class="no-toggle" >}} message PTransform { FunctionSpec spec; repeated string subtransforms; // Maps from local string names to PCollection ids map<string, bytes> inputs; map<string, bytes> outputs; ... } {{< /highlight >}} A `PTransform` may have subtransforms if it is a composite, in which case the `FunctionSpec` may be omitted since the subtransforms define its behavior. The input and output `PCollections` 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. A runner that understands the specification of a given `PTransform` (whether primitive or composite), as defined by its `FunctionSpec`, is free to substitute it with another `PTransform` (or set thereof) that has identical semantics. This is typically how `CombinePerKey` is handled, but many other substitutions can be done as well. A `PCollection` just stores a coder, windowing strategy, and whether or not it is bounded. {{< highlight class="no-toggle" >}} message PCollection { string coder_id; IsBounded is_bounded; string windowingstrategyid; ... } {{< /highlight >}} This is a very interesting proto. A coder is a parameterized function that may only be understood by a particular SDK, hence an `FunctionSpec`, but also may have component coders that fully define it. For example, a `ListCoder` is only a meta-format, while `ListCoder(VarIntCoder)` is a fully specified format. {{< highlight class="no-toggle" >}} message Coder { FunctionSpec spec; repeated string componentcoderids; } {{< /highlight >}} There are a large number of understood by most, if not all, SDKs. Using these allows for cross-language transforms. While your language's SDK will may insulate you from touching the Runner API protos directly, you may need to implement adapters for your runner, to expose it to another language. This allows a Python SDK to invoke a Java runner or vice versa. A typical implementation of this can be found in which is used directly to front several Python-implemented runners. 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. A key piece of this is the , which allows a Runner to fetch and deploy binary artifacts (such as jars, pypi packages, etc.) that are listed as dependencies in the various environments, and may have various representations. This is invoked after a pipeline is submitted, but before it is executed. The SDK submitting a pipeline acts as an artifact server to the runner receiving the request, and in turn the runner then acts as an artifact server to the workers (environments) hosting the users UDFs.