Status

Current state: [ UNDER DISCUSSION  ACCEPTED ]

Discussion thread: <link to mailing list DISCUSS thread>

JIRA: 

Released: 

Problem:

Samza today provides various api's like high level (fluent/application) for chaining complex jobs, low level (task) with configuration like Samza-Yarn, Samza as a library (Samza standalone) and in coming future Samza will support In-memory system for in memory streams that a user can produce/conusme from. Still Samza users have to get their hands dirty with too much configuration details and dependency on external system(like kafka) to write integration tests. In order to alleviate this we intend to provide a fluent test api to users. This will help in setting-up quick tests against their system without depending on underlying incoming stream from another system e.g Kafka Topic.

The goal of the proposed SEP we will provide a standardized framework to set-up continuous integration test for High Level Api, Low Level Api with Single Container configuration & Multi Container Configuration (Samza standalone using Zookeeper) with ease. Users won’t have to worry about the underlying Samza config details or even understand implementation details. This abstraction will increase developer productivity and help client's make their Samza system robust.

Motivation

Addition of this Continuous Integration Test Framework will alleviate:

1. Lack of a standardized way to set up integration tests for users

2. Lack of brevity in code and much detailed understanding low level config details of the Samza Api to set up samza systems

3. Lack of a pluggable Test System with any Samza single & multi-container configuration

4. Set of robust tests for current Samza api

Assumptions 

Proposed Changes

We propose a three step process for configuration of integration test for your samza job. Three facets are data injection, transformation and validation. Injection phase means configuring the input source for Samza job. Transformation phase means api logic of samza job(low-level/high-level).  Validation phase asserts the expected results to the actual results computed after running a job.

Data Injection:

The initial source of data stream is always supposed to be bounded since this is a test, and that may originate as one of the following sources

• In Memory Data Stream
  Users have ability to produce to & consume from in memory system partitions after SEP-8. In Memory Data system alleviates the need of serialization/deserialization of data since it does not persist data. We take advantage of this and provide very succinct Stream classes to serve as input data sources, which are the following:
  • Collection Stream
    Users can plug a collection (either List or Map) to create an in-memory input stream
    e.g:  CollectionStream.of(...,{1,2,3,4})
  • Event Builder Stream
    Event builder helps a user to mimic runtime samza processing environment in its tests, for example adding an exception in the steam, advancing time for window functions
• File Stream
  Users can create an input stream reading and writing from local file
  e.g:  FileStream.of("/path/to/file")
           
• Local Kafka Stream
  Users can also consume bounded streams from a kafka topic which serves as initial input. Samza already provide api's to consume from and produce to kafka. For kafka streams we will need serde configurations

Data Transformation:

Framework supports Samza job application logic, either using Low level api (StreamTask and AsyncStream Task) & High level api. This is done in the same way user writes their Samza job, but instead of writing verbose configs, they just need to pass class instance implementing this logic to the api.  

Data Validation: 

Data Types & Partitions:

Samza provides complete flexibility in usage of different data types for input steams, this framework will also provide complete flexibility for usage of primitive and derived data types. Test framework will provide api's for initialization of input streams (data injection), read from/write to single partition and multi-partition bounded streams (data transformation) and verification of expected to actual results (data validation)

Flexible Config

Traditionally we ask users to set up config for any samza job, for test purposes we set up basic config boiler plate for users and provide them a flexible option to still add any custom config (rarely needed), api exposes functions to configure single container or multi container mode. It also provides apis to configure concurrency semantics for the job. 

Public Interfaces

Two Apis for writing tests are: Low Level Test Api (TestTask) & High Level Api (TestApplication)

...

 Targeted audience for this SEP are two sets of users

• Samza Job Owners: To make their Samza jobs robust
• Samza Dev Teams: To enable dev teams make their samza infra robust

This SEP targets two phase development of the Framework:

• PHASE I: Testing Samza's public apis targeting Samza job Owners
• PHASE II: Testing various component of Samza targeting testing  Samza Dev team

Terminologies

• p1: refers to apis targeted for development for Phase I
• p2: refers to apis targeted for development for Phase II

Motivation

Addition of this Integration Test Framework will alleviate:

1. Lack of a standardized way to set up integration tests for users

2. Lack of brevity in code and much detailed understanding of low level configs to set up samza systems

3. Lack of a pluggable Test System with any Samza configuration (single & multi-container)

4. Comprehensive set of tests for Samza api

Assumptions 

Design

We propose a three step process for configuration of integration test for your samza job. Three facets are data injection, transformation and validation.

• Data Injection phase means configuring the input source for Samza job.
• Data Transformation phase means api logic of samza job(low-level/high-level). 
• Data Validation phase asserts the expected results to the actual results computed after running a Samza job.

Data Injection:

In the context of this Test framework an input stream means a stream that Samza job may consume from and an output stream means a stream that Samza job can produce to. The initial source of data stream is always supposed to be bounded since this is a test, and that may originate as one of the following sources listed below.

• In Memory Data Stream
  Users have ability to produce to & consume from in memory system partitions after SEP-8. In Memory Data system alleviates the need of serialization/deserialization of data since it does not persist data. We take advantage of this and provide very succinct Stream classes to serve as input data sources, which are the following:
  • Collection Stream (p1)
    Users can plug a collection (either List or Map) to create an in-memory input stream
    e.g:  CollectionStream.of(...,{1,2,3,4})
  • Event Builder Stream (p2)
    Event builder helps a user to mimic runtime samza processing environment in its tests, for example adding an exception in the steam, advancing time for window functions.
• File Stream
  Users can create an input stream reading and writing from local file
  e.g:  FileStream.of("/path/to/file")
• Local Kafka Stream
  Users can also consume bounded streams from a kafka topic which serves as initial input. Samza already provide api's to consume from and produce to kafka. For kafka streams we will need serde configurations, however implementation of bounded streams in kafka (using Latche Message or EndOfStreamMessage) is beyond scope of this SEP

For in memory streams the api actually initializes the stream by spinning up a Samza producer using an InMemorySystemProducer to write the stream, this is how a collection of data or events gets initialized as a steam. It also configures any output stream if the user has added any.

Data Transformation:

This is the Samza job you write, this can be either done using Low Level Api or the fluent High Level Api. Test frameworks provides api to set up test for both the cases. Test framework supports both the apis with Single container and Multi-container mode. Users implement StreamTask/Async Stream/Stream Application in the same way, as they do for their Samza job (or pass an instance of Samza job).

Data Validation: 

Data Types & Partitions:

The framework will provide complete flexibility for usage of primitive and derived data types. Serdes are required for local kafka stream and file stream but in memory streams dont require any Serde configuration. Serdes will be also required if users want to maintain State. Test framework will provide api's for initialization of input streams (both single and multi-partition), and also data validation on single partition and multi-partition of the bounded streams 

Running Config

Traditionally we ask users to set up config for any samza job, for test purposes we set up basic config boiler plate for users and provide them a flexible option to still add any custom config (rarely needed), api exposes functions to configure single container or multi container mode (using Zookeeper). It also provides functions to configure concurrency semantics for their Samza job. 

Stateful & Stateless Testing:

Test framework will support stateful and stateless testing, satateful testing can be either done using RocksDB or maintaining state in memory

Future Changes with Stream Descriptors:

The test framework is designed in a way which asks users to do none or minimal Samza configs, in future we intend to use StreamDescriptors in the test framework to do Samza configs. With StreamDescriptor and High Level api, another wrapper can be provided over the job to run the configuration in a test environment

Test Matrix (Plan)

Shown below is a targeted test matrix plan to test various components of Samza in p2.

n = number of threads

x = number of partitions 

m = number of containers

Public Interfaces

/**
* TestRunner provides static factory for quick setup of Samza environment for testing Low level api and High level api , users can configure input streams 
* they consume from and output streams they produce to, users pass in their Samza job api logic via StreamTask/AsyncStreamTask/StreamApplication
*/ 

public class TestRunner {
  
  // Static Factory to config & create runner for low level api 
  public static TestRunner of(Class taskClass) {...}
  // Static Factory to config & create runner for high level api
  public static TestRunner of(StreamApplication app) {...}
  
  // Add/Ovveride any custom configs
  public TestRunner addConfigs(Config configs) {...}
  public TestRunner addOverrideConfig(String key, String val){...}
 
  // Configure state for application
  public TestRunner addState(String storeName) {...}  
  
  // Configure an input stream for samza system, that job can consume from
  public TestRunner addInputStream(CollectionStream stream) {...}
  // Configures an output stream for samza system, that job can producer to
  public TestRunner addOutputStream(CollectionStream stream) {...}
 
  // Consume messages from a Stream
  public static <T> Map<Integer, List<T>> consumeStream(CollectionStream stream, Integer timeout) {...}
  
  // Run the TestRunner
  public void run() {...}
}

 

Types of Input Streams

CollectionStream:

/**
* CollectionStream acts a descriptor that can be used to build an in memory input stream (single/multiple partition) of java collections
*/
 
public class CollectionStream<T> { 
  // Create an empty stream with single partition that a Samza job can produce to
  public static <T> CollectionStream<T> empty(String systemName, String streamName) {...}
  
  // Create an empty stream with multiple partitions that a Samza job can produce to
  public static <T> CollectionStream<T> empty(String systemName, String streamName, Integer partitionCount) {...}
  
  // Create a stream of messages from input list with single partition
  public static <T> CollectionStream<T> of(String systemName, String streamName, Iterable<T> collection){...}
  
  // Create a stream of messages from input list with multiple partition, key of partitions map is partitionId
  public static <T> CollectionStream<T> of(String systemName, String streamName, Map<Integer,? extends Iterable<T>> partitions){...}
 
}

/**
* CollectionStreamSystem provides utilities to create and initialize an in memory input stream.
*/
 
public class CollectionStreamSystem { 

 // Create a CollectionStreamSystem 
 public static CollectionStreamSystem create(String name) {...}  
 
}

FileStream:

/**
* FileStream provides utilities to build a stream of messages from a file on disk
*/
 
public class FileStream<T> {
  public static <T> FileStream<T> of(String fileUri) {...}
}
 
public class FileStreamSystem {
  public static FileStreamSystem create(String name) {...}
}

Examples Usages of Test Api:

/**
* Simple Test case using a collection as an input for a low level application. It demonstrates set up and comparison of a test with 
* minimal(eg. here none) configs and it reads an input stream of integers and multiplies each integer with 10
*/
 
// Create a StreamTask
MyStreamTestTask myTask = new StreamTask() {
  @Override
  public void process(IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) throws Exception {
    Integer obj = (Integer) envelope.getMessage();
    collector.send(new OutgoingMessageEnvelope(new SystemStream("test","output"), obj*10));
  }
};
 

CollectionStream<Integer> input = CollectionStream
	.of("test", "input", {1,2,3,4})

CollectionStream output = CollectionStream
	.empty("test", "output")

TestRunner
    .of(MyStreamTestTask.class)
    .addInputStream(input)
    .addOutputStream(output)
    .run();
 
 
// Assertions on the outputs
Assert.assertThat(TestRunner.consumeStream(output), IsIterableContainingInOrder.contains({10,20,30,40})));

...

public class TestApplication<T> {
  
  // Configure an input stream for samza system, that app can use high order functions on
  public <T> MessageStream<T> getInputStream(EventStream<T> stream) {return null;}
  public <T> MessageStream<T> getInputStream(CollectionStream<T> stream) {return null;}
  public <T> MessageStream<T> getInputStream(FileStream<T> stream) {return null;}
  
  // Run the app
  public void run() { };
}

Types of Input Streams

/**
* Simple Test case using a collection as an input for a low level application in the async mode
*/

// 
public class MyAsyncStreamTask implements AsyncStreamTask, InitableTask, ClosableTask {
  private Client client;
  private WebTarget target; 

  @Override
  public void init(Config config, TaskContext taskContext) throws Exception {
    // Your initialization of web client code goes here
    client = ClientBuilder.newClient();
    target = client.target("http://example.com/resource/").path("hello");
  }

  @Override
  public void processAsync(IncomingMessageEnvelope envelope, MessageCollector collector,
      TaskCoordinator coordinator, final TaskCallback callback) {
    target.request().async().get(new InvocationCallback<Response>() {
      @Override
      public void completed(Response response

/**
* CollectionStream provides utilities to build a in memory input stream of collections(list, map). It also supports initialization of 
* partitions  
*/
 
public class CollectionStream<T> {
  private CollectionStream(String systemStream, List<T> collection, Integer partitionCount) {...}
  private CollectionStream(String systemStream) {...}
  
  public static <T> CollectionStream<T> empty(String systemStream) {
    return new CollectionStream<>(systemStream);
  }
Integer obj 
=  public static <T> CollectionStream<T> of(String systemStream, List<T> collection){
    return new CollectionStream<>(systemStream, collection, 1(Integer) envelope.getMessage();
        collector.send(new OutgoingMessageEnvelope(new SystemStream("test","output"), obj*10));
  }
  
  public static <T> CollectionStream<T> ofPartitions(String systemStream, List<List<T>> collection){
 callback.complete();
      }

 return new CollectionStream<>(systemStream, collection, collection.size());  @Override
  }
  
  public static <K, V> CollectionStream<KV<K, V>> of(String systemStream, Map<K, V> elems) {...}
}

public class EventStream<T> {
  public static abstract class Builder<T> {
    public abstract Builder addElement();
    public abstract Builder addExceptionvoid failed(Throwable throwable) {
        System.out.println("Invocation failed.");
        callback.failure(throwable);
      }
    });
  }

  @Override
  public void close() throws Exception {
    client.close();
  }
}

CollectionStream<Integer> input public abstract Builder advanceTimeTo(long time= CollectionStream
	.of("test", "input", {1,2,3,4});
CollectionStream output   public abstract EventStream<T> build(); 
 }

 public static <T> Builder<T> builder() {...}
}

public class FileStream<T> {
  public static <T> FileStream<T> of(String fileUri) {...}
}

A Sample Test:

= CollectionStream
	.empty("test", "output");

TestRunner
    .of(MyAsyncStreamTask.class)
    .addInputStream(input)
    .addOutputStream(output)
    .run();
// Assertions on the outputs
StreamAssert.that(output).containsInAnyOrder({10,20,30,40});

/**
* Sample Test case using a collection as an input for a low level application. It demonstrates set up and comparison of a test with 
* minimal(eg. here none) configs and it readsSimple Test case using a collection as an input streamfor ofa integersHigh andlevel multiplies application
* each integer with 10
*/
 
// Create a StreamTask
StreamTask myTask = new StreamTask() {
  @Override
  public void process(IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) throws Exception {
    Integer obj = (Integer) envelope.getMessage();
    collector.send(new OutgoingMessageEnvelope(new SystemStream("test-samza","output"), obj*10));
  }
};
/
 
public class MyStreamApplication implements StreamApplication {
  @Override
  public void init(StreamGraph graph, Config config) {
    MessageStream<Integer> pageViews = graph.getInputStream(“test.page-views”);
    pageViews.map(s -> "processed " + s)
             .sendTo(graph.getOutputStream(“test.output”));
 }
}
 
CollectionStream<Integer> input = CollectionStream
	.of("test", "input", {1,2,3,4});

CollectionStream output = CollectionStream
	.empty("test", "output");
 
// Initialize and run the test framework
TestTask
    .create(systemName: "test-samza", myTask, config)TestRunner
	.of(new MyStreamApplication());
    .setJobContainerThreadPoolSizeaddInputStream(4input)
    .addInputStream(CollectionStream.of(streamName: "input", {1,2,3,4})addOutputStream(output)
    .addOutputStream(CollectionStream.empty(streamName: "output"))addOverrideConfig("job.default.system", "test") 
    .run();
 
// Assertions on the outputs
TaskAssert.that("test-samzaStreamAssert.that(output).contains({"processed 1", "processed 2", "Outputprocessed 4").containsInAnyOrder({10,30,20,40});

...

});

Implementation and Test Plan

• Introduce the new interface and api for test framework
• Use the same api to write comprehensive integration test for: 
  • Samza Low level Async Api (Single and Multi-Container Mode)
  • Samza Low level Synchronous Api (Single and Multi-Container Mode)
  • Samza High Level Api (Single and Multi-Container Mode)

Compatibility, Deprecation, and Migration Plan

 As this is a new feature, no plans are required for compatibility, deprecation and migration.

Rejected Alternatives