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https://issues.apache.org/jira/browse/FLINK-31762?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17856514#comment-17856514
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Lorenzo Affetti commented on FLINK-31762:
-----------------------------------------
Thank you [~jingge] !
This would be my first contribution to the Kafka connector and I am currently
digging in the codebase to frame the problem, bear with me :)
I confirm that the logic above applies to the V2 APIs in the `getSplitOwner`
method of SourceEnumerator .
For example, with two topics with 2 partitions and parallelism 12 I get:
16:33:44,934 INFO
org.apache.flink.connector.kafka.source.enumerator.KafkaSourceEnumerator [] -
Assigning splits to readers {
0=[[Partition: test-topic-1-0, StartingOffset:-2,
StoppingOffset:-9223372036854775808], [Partition: test-topic-2-1,
StartingOffset:-2, StoppingOffset:-9223372036854775808]],
1=[[Partition: test-topic-1-1, StartingOffset:-2,
StoppingOffset:-9223372036854775808]],
3=[[Partition: test-topic-2-0, StartingOffset:-2,
StoppingOffset:-9223372036854775808]]
}
As stated, the assignment is suboptimal as task 2 would be idle.
I still need to verify where `KafkaTopicPartitionAssigner` code gets used to
avoid inconsistencies.
Or, at least, is this part of this issue or only the KafkaSource matters?
Also, [~tzulitai] changing the logic to evenly distribute the partitions would
probably lead to a non-deterministic assignment (especially conjuncted with
partition discovery) would this cause any issue in your opinion? I see you
mentioned above about state restores, can we expand on that?
For example, in the `KafkaTopicPartitionAssigner` we can read as javadoc for
the `assign` method:
{code:java}
/**
* Returns the index of the target subtask that a specific Kafka partition
should be assigned
* to.
*
* <p>The resulting distribution of partitions of a single topic has the
following contract:
*
* <ul>
* <li>1. Uniformly distributed across subtasks
* <li>2. Partitions are round-robin distributed (strictly clockwise w.r.t.
ascending subtask
* indices) by using the partition id as the offset from a starting index (i.e.,
the index
* of the subtask which partition 0 of the topic will be assigned to, determined
using the
* topic name).
* </ul>
*
* <p>The above contract is crucial and cannot be broken. Consumer subtasks rely
on this
* contract to locally filter out partitions that it should not subscribe to,
guaranteeing that
* all partitions of a single topic will always be assigned to some subtask in a
uniformly
* distributed manner.
*
* @param partition the Kafka partition
* @param numParallelSubtasks total number of parallel subtasks
* @return index of the target subtask that the Kafka partition should be
assigned to.
*/{code}
Or this is something not be concerned about as this logic is not used in
KafkaSource?
Thank you!
> Subscribe to multiple Kafka topics may cause partition assignment skew
> ----------------------------------------------------------------------
>
> Key: FLINK-31762
> URL: https://issues.apache.org/jira/browse/FLINK-31762
> Project: Flink
> Issue Type: Improvement
> Components: Connectors / Kafka
> Affects Versions: 1.13.0, 1.18.0
> Reporter: Liam
> Assignee: Lorenzo Affetti
> Priority: Major
> Attachments: image-2023-04-11-08-00-16-054.png,
> image-2023-04-11-08-12-24-115.png
>
>
> To simplify the demonstration, let us assume that there are two topics, and
> each topic has four partitions. We have set the parallelism to eight to
> consume these two topics. However, the current partition assignment method
> may lead to some subtasks being assigned two partitions while others are left
> with none.
> !image-2023-04-11-08-00-16-054.png|width=500,height=143!
> In my case, the situation is even worse as I have ten topics, each with 100
> partitions. If I set the parallelism to 1000, some slots may be assigned
> seven partitions while others remain unassigned.
> To address this issue, I propose a new partition assignment solution. In this
> approach, round-robin assignment takes place between all topics, not just one.
> For example, the ideal assignment for the case mentioned above is presented
> below:
>
> !https://imgr.whimsical.com/object/A4jSJwgQNrc5mgpGddhghq|width=513,height=134!
> This new solution can also handle cases where each topic has more partitions.
> !image-2023-04-11-08-12-24-115.png|width=444,height=127!
> Let us work together to reach a consensus on this proposal. Thank you!
>
> FYI: how the partition be assigned currently
> {code:java}
> public class KafkaTopicPartitionAssigner {
> public static int assign(KafkaTopicPartition partition, int
> numParallelSubtasks) {
> return assign(partition.getTopic(), partition.getPartition(),
> numParallelSubtasks);
> } public static int assign(String topic, int partition, int
> numParallelSubtasks) {
> int startIndex = ((topic.hashCode() * 31) & 0x7FFFFFFF) %
> numParallelSubtasks; // here, the assumption is that the id of Kafka
> partitions are always ascending
> // starting from 0, and therefore can be used directly as the offset
> clockwise from the
> // start index
> return (startIndex + partition) % numParallelSubtasks;
> }
> {code}
> for Kafka Source, it's implemented in the KafkaSourceEnumerator as below
> {code:java}
> static int getSplitOwner(TopicPartition tp, int numReaders) {
> int startIndex = ((tp.topic().hashCode() * 31) & 0x7FFFFFFF) %
> numReaders; // here, the assumption is that the id of Kafka partitions
> are always ascending
> // starting from 0, and therefore can be used directly as the offset
> clockwise from the
> // start index
> return (startIndex + tp.partition()) % numReaders;
> } {code}
>
>
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