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https://issues.apache.org/jira/browse/KAFKA-1736?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14188637#comment-14188637
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Gwen Shapira commented on KAFKA-1736:
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Agree.
What I meant is - we want to add "rack aware" replica assignment in the very
near future.
We need to think about how those features will play together.
How about leaving the Kafka server alone for now, and implementing these
policies as "usability features" in the partition assignment tool? This will
allow users to experiment with different policies without having to change core
functionality, and will allow us to figure out what makes sense for our users.
> Improve parition-broker assignment strategy for better availaility in
> majority durability modes
> -----------------------------------------------------------------------------------------------
>
> Key: KAFKA-1736
> URL: https://issues.apache.org/jira/browse/KAFKA-1736
> Project: Kafka
> Issue Type: Improvement
> Affects Versions: 0.8.1.1
> Reporter: Kyle Banker
> Priority: Minor
> Attachments: Partitioner.scala
>
>
> The current random strategy of partition-to-broker distribution combined with
> a fairly typical use of min.isr and request.acks results in a suboptimal
> level of availability.
> Specifically, if all of your topics have a replication factor of 3, and you
> use min.isr=2 and required.acks=all, then regardless of the number of the
> brokers in the cluster, you can safely lose only 1 node. Losing more than 1
> node will, 95% of the time, result in the inability to write to at least one
> partition, thus rendering the cluster unavailable. As the total number of
> partitions increases, so does this probability.
> On the other hand, if partitions are distributed so that brokers are
> effectively replicas of each other, then the probability of unavailability
> when two nodes are lost is significantly decreased. This probability
> continues to decrease as the size of the cluster increases and, more
> significantly, this probability is constant with respect to the total number
> of partitions. The only requirement for getting these numbers with this
> strategy is that the number of brokers be a multiple of the replication
> factor.
> Here are of the results of some simulations I've run:
> With Random Partition Assignment
> Number of Brokers / Number of Partitions / Replication Factor / Probability
> that two randomly selected nodes will contain at least 1 of the same
> partitions
> 6 / 54 / 3 / .999
> 9 / 54 / 3 / .986
> 12 / 54 / 3 / .894
> Broker-Replica-Style Partitioning
> Number of Brokers / Number of Partitions / Replication Factor / Probability
> that two randomly selected nodes will contain at least 1 of the same
> partitions
> 6 / 54 / 3 / .424
> 9 / 54 / 3 / .228
> 12 / 54 / 3 / .168
> Adopting this strategy will greatly increase availability for users wanting
> majority-style durability and should not change current behavior as long as
> leader partitions are assigned evenly. I don't know of any negative impact
> for other use cases, as in these cases, the distribution will still be
> effectively random.
> Let me know if you'd like to see simulation code and whether a patch would be
> welcome.
> EDIT: Just to clarify, here's how the current partition assigner would assign
> 9 partitions with 3 replicas each to a 9-node cluster (broker number -> set
> of replicas).
> 0 = Some(List(2, 3, 4))
> 1 = Some(List(3, 4, 5))
> 2 = Some(List(4, 5, 6))
> 3 = Some(List(5, 6, 7))
> 4 = Some(List(6, 7, 8))
> 5 = Some(List(7, 8, 9))
> 6 = Some(List(8, 9, 1))
> 7 = Some(List(9, 1, 2))
> 8 = Some(List(1, 2, 3))
> Here's how I'm proposing they be assigned:
> 0 = Some(ArrayBuffer(8, 5, 2))
> 1 = Some(ArrayBuffer(8, 5, 2))
> 2 = Some(ArrayBuffer(8, 5, 2))
> 3 = Some(ArrayBuffer(7, 4, 1))
> 4 = Some(ArrayBuffer(7, 4, 1))
> 5 = Some(ArrayBuffer(7, 4, 1))
> 6 = Some(ArrayBuffer(6, 3, 0))
> 7 = Some(ArrayBuffer(6, 3, 0))
> 8 = Some(ArrayBuffer(6, 3, 0))
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