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https://issues.apache.org/jira/browse/KAFKA-691?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13551978#comment-13551978
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Maxime Brugidou commented on KAFKA-691:
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Should i make another patch? I'll try on Monday.
1. It would probably require yet another config variable like
"producer.metadata.request.batch.size" or something like that.
2. Should it be batched for every updateInfo() or just during the metadata
refresh? It could help if we do the former because failing messages from many
different topics could probably never go through if the metadata request
timeouts.
3. Isn'it getting a little convoluted? Maybe i am missing something but the
producer side is getting trickier.
4. Please note that I also opened KAFKA-693 about the consumer side. And I'd
love to submit a patch but the rebalance logic seems complex so I'd prefer to
have some insights first before going in the wrong direction.
> Fault tolerance broken with replication factor 1
> ------------------------------------------------
>
> Key: KAFKA-691
> URL: https://issues.apache.org/jira/browse/KAFKA-691
> Project: Kafka
> Issue Type: Bug
> Affects Versions: 0.8
> Reporter: Jay Kreps
> Assignee: Maxime Brugidou
> Fix For: 0.8
>
> Attachments: KAFKA-691-v1.patch, KAFKA-691-v2.patch
>
>
> In 0.7 if a partition was down we would just send the message elsewhere. This
> meant that the partitioning was really more of a "stickiness" then a hard
> guarantee. This made it impossible to depend on it for partitioned, stateful
> processing.
> In 0.8 when running with replication this should not be a problem generally
> as the partitions are now highly available and fail over to other replicas.
> However in the case of replication factor = 1 no longer really works for most
> cases as now a dead broker will give errors for that broker.
> I am not sure of the best fix. Intuitively I think this is something that
> should be handled by the Partitioner interface. However currently the
> partitioner has no knowledge of which nodes are available. So you could use a
> random partitioner, but that would keep going back to the down node.
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