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https://issues.apache.org/jira/browse/KAFKA-691?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Jun Rao resolved KAFKA-691.
---------------------------

       Resolution: Fixed
    Fix Version/s: 0.8
         Assignee: Maxime Brugidou

Thanks for patch v2. Committed to 0.8 by renaming lastTopicMetadataRefresh to 
lastTopicMetadataRefreshTime and removing an unused comment.

3.1 Rebalance happens during consumer initialization. It only needs the 
partition data to be in ZK and doesn't require all brokers to be up. Of course, 
if a broker is not up, the consumer may not be able to consume data from it. 
ConsumerFetcherManager is responsible for checking if a partition becomes 
available again.

3.2 If the partition path changes in ZK, a rebalance will be triggered.
                
> 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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