Status
Current state: [ UNDER DISCUSSION ]
Discussion thread: Add in-memory system
JIRA: SAMZA-TBD
Released:
Problem
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.
Motivation
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)
Assumptions
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.
Design
Data Source
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. We will introduce a configuration to tune memory pool for the intermediate queue.
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 to SystemStreamPartition in Samza.
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.
Multiple Partition
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. There are couple of ways to support multiple partitions.
A. 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.
B. Partitioning within Samza
Takes a collection from the user and applies a partitioning strategy. The strategy could be as simple as a round robin strategy or random assignment strategy. How do we determine the partition count? We can either have the user specify the number of partitions (introduces new configuration in Samza). Alternatively, we can also automatically come up with partition number based on the input data source. TBD
C. Partitioning within Samza w/ configurable strategy
We follow a similar strategy as “Partitioning within Samza” with the additional optional of supporting user specified groupers. With this approach, we sign up for introducing a public interface that user has to implement and pass it to Samza using config. Downside being it introduces additional configurations and also add on to our existing class loading approach using reflection.
I am leaning towards approach ‘A’ - partitioning at source.
End of Stream
In-memory system will leverage the EOS feature introduced in SEP-6 to mark the end of stream for bounded sources.
Proposed Changes
Architecture
Implementation
- 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.
Test Plan
High level application
High level application with durable state
Low level application
Low level application with durable state
Low level application with manual checkpoint
Samza SQL application
Users should be able to leverage in-memory collection based system to test Samza SQL application.
Details: TBD