Current state: Accepted
Discussion thread: Add in-memory system
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.
- IME - Incoming Message Envelope
- EOS - End of Stream
With in-memory system, we will alleviate the following pain points.
Dependency on Kafka for intermediate streams for testing
Running time for tests (time spent on setting up and tearing down)
Ease of testing
Lack of collection based input for testing (this SEP addresses this problem partly)
In-memory system is applicable only for jobs in local execution environment. Remote execution environment isn’t supported.
The scope of in-memory system and the data it handles are limited to a container. I.e. there is no support for process to process interaction or sharing.
Checkpointing is not supported and consumers always start from the beginning in case of restart.
In-memory system doesn’t support persistence and is not the source of truth for the data. The data in the queue is lost when the job restarts or shutdowns unexpectedly.
Data Source & Sink
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.
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.
- 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.
- Type of IME: In-memory system acts as a pass through system consumer, passing the actual message envelope to the task without any processing.
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 partitioned and how 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.
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.
In order to exploit the parallelism that the Samza framework offers and to enable users test their job with multiple tasks, we need to support multiple partitioning.
Partitioning at source
In this approach, we push the partitioning to the source. For e.g. we can read of a `Collection<Collection<T>>` and have each collection within the collection assigned to one partition. This is surprisingly simple yet powerful since it eliminates the need for repartitioning phase and allows the user to group the data at his/her whim. The downside w/ this approach is the input collections can be skewed and Samza don’t control the evenness in the distribution of the data. Since the primary use case is testing, the skew should have negligible impact.
End of Stream
In-memory system will leverage the EOS feature introduced in SEP-6 to mark the end of stream for bounded sources.
- 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.
I am leaning towards Approach C as its simpler, not tied to BlockingEnvelopeMap and has separation of concerns.
High level stateless application
Low level stateless application
Low level application with custom IME
Unsupported Use cases
For V1, we will not support the following use cases since it has a depdencies.
- High level application with 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.
Samza SQL application
Users should be able to leverage in-memory collection based system to test Samza SQL application provided Samza SQL integrates with SEP-2.