Status

Current state: UNDER DISCUSSION

Discussion thread: 

JIRA: SAMZA-1293

Released: <Samza version>

Problem

Right now, Samza does not allow partitions of the input stream to increase after a stateful job is created. This causes problem when Kafka is used as the input system, because we need to expand partitions of an existing topic as the byte-in-rate of the topic increases over time in order to limit the size of the maximum partition in Kafka. Kafka broker may have performance issue if the size of a given partition is too large.

Motivation

This design doc provides a solution to increase partition number of the input streams of a stateful Samza job while still ensuring the correctness of Samze job output. The solution should work for all input systems that satisfy the operation requirement described below, i.e. hash algorithm should be implemented in the form of hash(key) % partitionNum and partitionNum should be multiplied by power of two when we increase it. In the rest of the documentation we will assume Kafka as the input system. The motivation of increasing partition number of Kafka topic is 1) increase performance of Kafka broker and 2) increase throughput of Kafka consumer in the Samza container.

Proposed Changes

Kafka Operational Requirement

1) Partitioning algorithms in producer

The partitioning algorithm should be implemented in the form of hash(key) % partitionNum. User is free to choose hash function but the hashed result must be mod by the partitionNum. This requirement is satisfied by the default partitioning algorithms in Kafka. Samza needs to repartition the user’s topic if user uses a customized partitioning algorithm in their producer implementation that doesn’t meet this requirement.

2) Topic management 

The partition number of any keyed topic that may be consumed by Samza should always be multiplied by power of two when we increase it. This guideline should be enforced by whoever may increase partition number of the Kafka topic.

Samza Implementation Change

1) Store SystemStreamPartition-to-Task assignment in the coordinator stream

Create classes SystemStreamPartitionAssignmentManager and SetSSPTaskMapping to read and write SystemStreamPartition-to-Task assignment in the coordinator stream. This will be done similar to how ChangelogPartitionManager and SetChangelogMapping are used to read and write Task-to-ChangeLogPartition assignment in the coordinator stream. The assignment should be written to the coordinator stream every time the job model is initialized.

2) Change interface SystemStreamPartitionGrouper 

With the proposal in this doc, we should deprecate the existing method group(Set<SystemStreamPartition> ssps) of the interface SystemStreamPartitionGrouper and replace it with the method group(Map<SystemStreamPartition, String> previousSystemStreamPartitionMapping, Set<SystemStreamPartition> ssps). The new method takes the SystemStreamPartition-to-Task assignment from the previous job model which can be read from the coordinator stream.

3) Create class GroupByPartitionFixedTaskNum and GroupBySystemStreamPartitionFixedTaskNum

We should create two new classes GroupByPartitionFixedTaskNum and GroupBySystemStreamPartitionFixedTaskNum which implements the interface SystemStreamPartitionGrouper. GroupByPartitionFixedTaskNum (or GroupBySystemStreamPartitionFixedTaskNum) should group system-stream-partitions in the same way as GroupByPartition (or GroupBySystemStreamPartition) if previousSystemStreamPartitionMapping is empty (i.e. the job is run for the first time) or if partition of those streams has not changed since the job is created. Otherwise, GroupByPartitionFixedTaskNum should group partitions in such a way that 1) the number of tasks consuming from any given stream does not change before and after the partition expansion; and 2) messages with the same key in the same stream will be consumed by the same task before and after the expansion.

More specifically, GroupByPartitionFixedTaskNum will map a given SystemStreamPartition ssp to the taskName which is determined using the following algorithm:

- If previousSystemStreamPartitionMapping is empty, return GroupByPartition.group(ssps).get(ssp), where ssps represents to entire set of SystemStreamPartition to be grouped.
- Calculate from previous assignment previousSystemStreamPartitionMapping the total number of tasks that are consuming from partitions of the stream ssp.getStream(). Denote this as taskNumForStream.
- return previousSystemStreamPartitionMapping.get(new SystemStreamPartition(ssp.getSystem(), ssp.getStream(), ssp.getPartition() % taskNumForStream))

Similarly, GroupBySystemStreamPartitionFixedTaskNum will map a given SystemStreamPartition ssp to the taskName which is determined using the following algorithm:

- If previousSystemStreamPartitionMapping is empty, return GroupBySystemStreamPartition.group(ssps).get(ssp), where ssps represents to entire set of SystemStreamPartition to be grouped.
- Calculate from previous assignment previousSystemStreamPartitionMapping the total number of tasks that are consuming from partitions of the stream ssp.getStream(). Denote this as taskNumForStream.
- return previousSystemStreamPartitionMapping.get(new SystemStreamPartition(ssp.getSystem(), ssp.getStream(), ssp.getPartition() % taskNumForStream))

Stateful Samza job which is using GroupByPartition (or GroupBySystemStreamPartition) as grouper class should be configured to use GroupByPartitionFixedTaskNum (or GroupBySystemStreamPartitionFixedTaskNum) in order to allow partition expansion. Note that GroupByPartitionFixedTaskNum (or GroupBySystemStreamPartitionFixedTaskNum) is backward compatible with GroupByPartition (or GroupBySystemStreamPartition) because they return the same partition-to-task assignment if partition doesn't expand. Thus user's job should not need to bootstrap key/value store from the changelog topic.

To help understand this algorithm, the idea is to split partitions into disjoint buckets (or groups) of partitions where the union of those buckets equals the original set of partitions. The partition-to-bucket assignment ensure that messages with the same key will be produced to the partitions of the same bucket. Then partitions in the same bucket will be assigned to the same task to ensure that messages with the same key will go to the same task. 

For example, suppose partition is increased from 2 to 4 and we use GroupByPartitionFixedTaskNum as grouper, partitions 0 and 2 should be mapped to the same task and partitions 1 and 3 should be mapped to the same task. The figure below shows the relation between partitions, buckets and tasks after we increase partition from 2 to 4.

3) Handle partition expansion while tasks are running

JobCoordinator is already monitoring partition expansion of input streams as of current Samza implementation. And it already has logic to restart container in case of container failure. All we need to do is to let JobCoordinator re-calculate JobModel and restart container using the new JobModel when partition expansion is detected.

Public Interfaces

1) Deprecate the method Map<TaskName, Set<SystemStreamPartition>> group(Set<SystemStreamPartition> ssps) of the interface SystemStreamPartitionGrouper

2) Add method Map<TaskName, Set<SystemStreamPartition>> group(Map<SystemStreamPartition, String> previousSystemStreamPartitionMapping, Set<SystemStreamPartition> ssps) in the interface SystemStreamPartitionGrouper.

3) Add class GroupByPartitionFixedTaskNum which implements the interface SystemStreamPartitionGrouper

4) Add class GroupBySystemStreamPartitionFixedTaskNum which implements the interface SystemStreamPartitionGrouper

Test Plan

To be added

Compatibility, Deprecation, and Migration Plan

- Deprecate the method Map<TaskName, Set<SystemStreamPartition>> group(Set<SystemStreamPartition> ssps) of the interface SystemStreamPartitionGrouper.
- Users need to implement the newly-added method if they have a custom implementation of the interface SystemStreamPartitionGrouper.
- Users need to use the newly-added GroupByPartitionFixedTaskNum as the grouper class in order to deal with possible partition number change of their input streams.

Rejected Alternatives

1. Allow task number to increase instead of creating a new grouper class.

Allowing task number to increase is useful since it increases the performance of a given Samza job. However, this feature alone does not solve the problem of allowing partition expansion. For example, say we have a job that joins two streams both of which have 3 partitions. If partition number of one stream increases from 3 to 6, we still want the task number to remain 3 to make sure that messages with the same key from both streams will be handled by the same task. This needs to be done with the new grouper class we proposed in this doc.