Status

Current state: [ UNDER DISCUSSION ]Accepted

Discussion thread: Add in-memory system

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With 0.13 release, the rich high level APIs allows users to chain complex processing logic as one coherent and fluent application. With so much power, there is a need for inherent support for ease of testing. Currently, the users will have get their hands dirty and understand some implementation details of Samza to write exhaustive integration tests. We want to tackle this problem in steps and this SEP, will take us one step closer towards the goal by introducing an in-memory system in Samza.

Terminologies

• IME - Incoming Message Envelope
• EOS - End of Stream

Motivation

With in-memory system, we will alleviate the following pain points.

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The input data source for the in-memory system can be broadly classified as bounded and unbounded data. We are limiting the scope of this SEP to only bounded data source that is immutable as the input source. It simplifies the view of the data and also the initialization step for the consumers. However, in-memory system for intermediate streams supports both bounded and unbounded data. The sink a.k.a output source is modeled to be mutable.

Data Type

Samza has a pluggable system design allowing users to implement their own system consumers & producers. Typically, consumers consume raw message and wrap them in an IME. However, it is possible for some systems to introduce subclass of IME and pass them to the tasks instead. For this reason, we need to support different data types within in-memory collection.

1. Raw messages: In-memory system will behave like a typical consumer and wrap the raw message in an IME. The offset and key fields for the message are populated by the in-memory system. Note, the offset is defined as the position of the data in the collection and the key is the hash code of the raw message. If the user needs fine grained control on these fields, they should construct their own IME.
2. Type of IME:  In-memory system acts as a pass through system consumer, passing the actual message envelope to the task without any processing.

Data Partitioning

Samza is a distributed stream processing framework that achieves parallelism with partitioned data. With a bounded data source, we need to think about how the data is going to be partitioned and how do we map data it is mapped to SystemStreamPartition in Samza.  Partitioning is only interesting in the case when the input source is raw message. With IME, partitioning information is already part of it and in-memory system will respect the partition information within the IME.

Single Partition

We can use a trivial and simpler approach of associating all of our data source to a single partition. It is not a bad strategy since the primary use case for in-memory systems is testing and the volume of data is negligible that we can barely notice the effects of parallelism. Although it does come w/ a downside that it constraints the users to only test their job with only one task. It might not be a desirable and exhaustive testing strategy from a user’s perspective.

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• Approach A - Use existing BlockingEnvelopeMap and have one common class that shares the responsibility of consumer as well as producer. The class will be responsible for handling both producing and consuming messages off the same queue.
• Approach B - Have separate producer and consumer. Tie up the consumer with the producer so that producer has hooks to produce to the same underlying `BlockingEnvelopeMap` that consumer uses.
• Approach C - Have separate consumer and producer. Introduce a custom queue that are shared between consumer and producer. The queue lifecycle is managed by the SystemAdmin.

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• .

I am leaning towards Approach C as its simpler, not tied to BlockingEnvelopeMap and has separation of concerns.

Example Usages

High level stateless application

/**
 * Sample test case w/ multiple input partition using collection based system.
 */
 
...
...
 
ImmutableList<IV> inputA = ...
ImmutableList<IV> inputB = ...
 
List<OV> outputData = ...   // mutable
 
StreamDescriptor.Input<IK,IV> input = StreamDescriptor.<IK,IV>input("test-stream")
       .from(ImmutableList.of(inputA, inputB));
 
StreamDescriptor.Output<OK,OV> output = StreamDescriptor.<OK,OV>output("output-test-stream")
       .from(outputData);
 
// application logic
StreamApplication app = StreamApplication.create(...);
app.from(input)
   .map(...)
   .sendTo(output);
 
app.run();
app.waitForFinish();
 
// assertions on outputData

High level application with durable state

/**
 * Sample test case using collection based system for high level application with durable state.
 * Note: We don't provide the ability to recover data/bootstrap data for store using in-memory system. It should work seamlessly once we have StoreDecriptor.
 */
 
...
...
 
ImmutableList<IV> inputA = ...
 
List<OV> outputData = ...   // mutable
 
StreamDescriptor.Input<IK,IV> input = StreamDescriptor.<IK,IV>input("test-stream")
       .from(ImmutableList.of(inputA));
 
StreamDescriptor.Output<OK,OV> output = StreamDescriptor.<OK,OV>output("output-test-stream")
       .from(outputData);
 
// application logic
StreamApplication app = StreamApplication.create(...);
app.from(input)
   .map(...)
   .sendTo(output);
 
app.run();
app.waitForFinish();
 
// assertions on outputData
 

Low level stateless application

/**
 * Sample test case using collection based system for low level application.
 */

...
...

ImmutableList<IV> inputA = ...

List<OV> outputData = ... // mutable

StreamDescriptor.Input<IK,IV> input = StreamDescriptor.<IK,IV>input("input-stream-low-level-app")
	.from(ImmutableList.of(inputA));
StreamDescriptor.Output<OK,OV> output = StreamDescriptor.<OK,OV>output("output-stream-low-level-app")
	.from(outputData);

// application logic
StreamTaskApplication app = StreamTaskApplication.create(config, new MyTaskFactory())
	.addInputs(Collections.singletonList(input))
	.addOutputs(Collections.singletonList(output));

app.run();
app.waitForFinish();

// assertions on outputData

Low level application with durable statecustom IME

/**
 * Sample test case using collection based system for low level application withusing durablecustom stateIME.
 * Note:It Wedemonstrates don'tthe provideuse theof abilityIME toas recovera data/bootstrap datasource foras storeopposed usingto in-memoryraw systemmessage. It should work seamlessly once we have StoreDecriptor.
 */
The users are responsible for creating a 
 * complete IME object with partition information.
 */
 
...
...
 
ImmutableList<IV>ImmutableList<MyIME> inputAinputData = Utils.createMyIME(...);
 
List<OV> outputData = ... // mutable
 
StreamDescriptor.Input<IKInput<Object,IV> Object> input = StreamDescriptor.<IK,IV>input<>input("input-stream-low-level-app")
	.from(ImmutableList.of(inputAinputData));
StreamDescriptor.Output<OK,OV> output = StreamDescriptor.<OK,OV>output("output-stream-low-level-app")
	.from(outputData);
 
// application logic
StreamTaskApplication app = StreamTaskApplication.create(config, new MyTaskFactory())
	.addInputs(Collections.singletonList(input, changelog))
	.addOutputs(Collections.singletonList(output));

app.run();
app.waitForFinish();

// assertions on outputData

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Unsupported Use cases

For V1, we will not support the following use cases since it has a depdencies.

• High level application with

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• durable state
• Low level application with durable state
• Application with manual checkpoint. Note. Manual checkpointing will result in a no-op and might not result in desired behaviour.

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Samza SQL application

Users should be able to leverage in-memory collection based system to test Samza SQL application .
Details: TBDprovided Samza SQL integrates with SEP-2.