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From "Rodrigo Schmidt (JIRA)" <j...@apache.org>
Subject [jira] Commented: (HDFS-1094) Intelligent block placement policy to decrease probability of block loss
Date Mon, 12 Apr 2010 23:22:51 GMT

    [ https://issues.apache.org/jira/browse/HDFS-1094?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12856219#action_12856219

Rodrigo Schmidt commented on HDFS-1094:

I think Brian is right. Dhruba's proposal doesn't change the probability of data loss given
that F machines fail.

Where you place blocks will only impact the failure probability of that block, and only if
nodes have different probabilities of failure per machine. Placing blocks on a circular arrangement
(Dhruba's proposal) or randomly doesn't affect the expected number of missing blocks given
F failures because they are both uniform placement strategies. And if we go for a non-uniform
placement strategy, we will only make the worst case worse. What I mean is that the current
block placement strategy is already optimal and we can't make the expected number of missing
blocks better by changing just block placement. We can only improve it by changing the redundancy
scheme that we use (e.g., adding correction codes or more replicas).

If you are wondering what is the expected number of blocks that go missing given F failures,
here is the math:

Consider a r-way replication scheme and the following variables....

r = number of replicas used
F = number of machines that fail concurrently
N = number of machines
B = number of blocks (without counting replication)

I use the prefix notation C(N,a) to denote the number of ways you can pick a elements out
of N.

With N machines, we have C(N,r) possible replica placements for a single block.  Given that
F machines fail, we have C(F,r) possible placements within those F failed machines.

If the block placement strategy is uniform (i.e., it doesn't favor some specific nodes or
racks more than others), the probability of a block having all its replicas within the failed
machines is

C(F,r) / C(N,r) =

(F) * (F-1) * (F-2) * ... * (F - r + 1)
(N) * (N-1) * (N-2) * ... * (N - r + 1)

The expected number of missing blocks when F machines fail should be

B * [formula above]

If B=30M, N = 1000 and F = 3, r=3, the expected number of missing blocks would be about 0.18

Please someone correct if my math or rationale is wrong.

> Intelligent block placement policy to decrease probability of block loss
> ------------------------------------------------------------------------
>                 Key: HDFS-1094
>                 URL: https://issues.apache.org/jira/browse/HDFS-1094
>             Project: Hadoop HDFS
>          Issue Type: Improvement
>          Components: name-node
>            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 non-trivial 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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