Martin Kleppmann created SAMZA-489:
--------------------------------------
Summary: Support Amazon Kinesis
Key: SAMZA-489
URL: https://issues.apache.org/jira/browse/SAMZA-489
Project: Samza
Issue Type: New Feature
Reporter: Martin Kleppmann
[AWS Kinesis|http://aws.amazon.com/kinesis/] is a publish-subscribe message
broker service quite similar to Kafka, provided as a hosted service by Amazon.
I have spoken to a few people who are interested in using Kinesis with Samza,
since the options for stateful stream processing with Kinesis are currently
quite limited. Samza's local state support would be great for Kinesis users.
I've looked a little into what it would take to support Kinesis in Samza.
Useful resources:
* [Kinesis Client Library for
Java|https://github.com/awslabs/amazon-kinesis-client]
* [Kinesis developer
guide|http://docs.aws.amazon.com/kinesis/latest/dev/introduction.html]
* [Description of
resharding|http://docs.aws.amazon.com/kinesis/latest/dev/kinesis-using-api-java.html#kinesis-using-api-java-resharding]
Kinesis is similar to Kafka in that it has total ordering of messages within a
partition (which Kinesis calls a "shard"). The most notable differences I
noticed are:
* Kinesis does not support compaction by key, and only keeps messages for 24
hours (the "trim horizon"). Thus it cannot be used for checkpointing and state
store changelogging. Another service must be used for durable storage (Amazon
recommends DynamoDB).
* It is common for the number of shards in a stream to change ("resharding"),
because a Kinesis shard is a unit of resourcing, not a logical grouping. A
Kinesis shard is more like a Kafka broker node, not like a Kafka partition.
The second point suggests that Kinesis shards should not be mapped 1:1 to Samza
StreamTasks like we do for Kafka, because whenever the number of shards
changes, any state associated with a StreamTask would no longer be in the right
place.
Kinesis assigns a message to a shard based on the MD5 hash of the message's
partition key (so all messages with the same partition key are guaranteed to be
in the same shard). Each shard owns a continuous range of the MD5 hash space.
When the number of shards is increased by one, a shard's hash range is
subdivided into two sub-ranges. When the number of shards is decreased by one,
two adjacent shards' hash ranges are merged into a single range.
I think the nicest way of modelling this in Samza would be to create a fixed
number of StreamTasks (e.g. 256, but make it configurable), and to assign each
task a fixed slice of this MD5 hash space. Each Kinesis shard then corresponds
to a subset of these StreamTasks, and the SystemConsumer implementation routes
messages from a shard to the appropriate StreamTask based on the hash of the
message's partition key. This implies that all the StreamTasks for a particular
Kinesis shard should be processed within the same container. This is not unlike
the Kafka consumer in Samza, which fetches messages for all of a container's
Kafka partitions in one go.
This solves removes the semantic problem of resharding: we can ensure that
messages with the same partition key are always routed to the same StreamTask,
even across shard splits and merges.
However, there are still some tricky edge cases to handle. For example, if
Kinesis decides to merge two shards that are currently processed by two
different Samza containers, what should Samza do? A simple (but perhaps a bit
wasteful) solution would be for both containers to continue consuming the
merged shard. Alternatively, Samza could reassign some StreamTasks from one
container to another, but that would require any state to be moved or rebuilt.
Probably double-consuming would make most sense for a first implementation.
In summary, it looks like Kinesis support is feasible, and would be a fun
challenge for someone to take on. Contributions welcome :)
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
