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Karthik Ranganathan commented on HDFS1094:

The issue I see with 1 (which may not be a big deal) is that if more racks are added, then
the placement policy cannot utilize the new, emptier machines more efficiently without rebalancing
data. Would a hybrid help? Replica 1 is chosen at random from (r1) and the next replica using
scheme 2 (excluding the node on rack r1).
@Brian:
I think this change protects against node failures more than rack failures. Lets say the replication
factor is three. If you calculate the probability of data loss if *any* 3 machines fail in
the cluster at the same time, then the placement matters:
1) If the blocks are scattered across all the machines, then any 3 machines failing will contribute
towards data loss.
2) If a given block (by virtue of the new replication scheme) is placed only on a subset of
machines, then the probability of data loss is reduced from any 3 machines failing to 3 machines
in that subset failing. Failures of machines in the distinct subsets don't contribute to data
loss.
> Intelligent block placement policy to decrease probability of block loss
> 
>
> Key: HDFS1094
> URL: https://issues.apache.org/jira/browse/HDFS1094
> Project: Hadoop HDFS
> Issue Type: Improvement
> Components: namenode
> Reporter: dhruba borthakur
> Assignee: dhruba borthakur
>
> The current HDFS implementation specifies that the first replica is local and the other
two replicas are on any two random nodes on a random remote rack. This means that if any three
datanodes die together, then there is a nontrivial probability of losing at least one block
in the cluster. This JIRA is to discuss if there is a better algorithm that can lower probability
of losing a block.

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