| ... Current state: [ UNDER DISCUSSION | ACCEPTED | REJECTED ] Discussion thread: <link to mailing list DISCUSS thread>
...
-
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 ... Design 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. ... 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 Public Interfaces Implementation and Test Plan Compatibility, Deprecation, and Migration Plan Rejected Alternatives Architecture
![]() Image Added ![]() Image Added
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
![]() Code Block |
/**
* Sample test case w/ multiple input partition using collection based system
*/
...
...
ImmutableSet<IV> inputA = ...
ImmutableSet<IV> inputB = ...
Set<OV> outputData = ... // mutable
StreamDescriptor.Input<IK,IV> input = StreamDescriptor.<IK,IV>input("test-stream")
.from(ImmutableSet.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
|
Low level application Collection based systems without the support of sharing state across processes are not applicable for low level application since they are fragmented in their nature and run on different process even within the same host. Samza SQL application Users should be able to leverage in-memory collection based system to test Samza SQL application.
Details: TBD |
