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From "Konstantin Shvachko (JIRA)" <j...@apache.org>
Subject [jira] Created: (HADOOP-572) Chain reaction in a big cluster caused by simultaneous failure of only a few data-nodes.
Date Tue, 03 Oct 2006 01:55:19 GMT
Chain reaction in a big cluster caused by simultaneous failure of only a few data-nodes.
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                 Key: HADOOP-572
                 URL: http://issues.apache.org/jira/browse/HADOOP-572
             Project: Hadoop
          Issue Type: Bug
    Affects Versions: 0.6.2
         Environment: Large dfs cluster
            Reporter: Konstantin Shvachko


I've observed a cluster crash caused by simultaneous failure of only 3 data-nodes.
The crash is reproducable. In order to reproduce it you need a rather large cluster.
To simplify calculations I'll consider a 600 node cluster as an example.
The cluster should also contain a substantial amount of data.
We will need at least 3 data-nodes containing 10,000+ blocks each.
Now suppose that these 3 data-nodes fail at the same time, and the name-node
started replicating all missing blocks belonging to the nodes.
The name-node can replicate 50 blocks per second on average based on experimental data.
Meaning, it will take more than 10 minutes, which is the heartbeat expiration interval,
to replicates all 30,000+ blocks.

With the 3 second heartbeat interval there are 600 / 3 = 200 heartbeats hitting the name-node
every second.
Under heavy replication load the name-node accepts about 50 heartbeats per second.
So at most 3/4 of all heartbeats remain unserved.

Each node SHOULD send 200 heartbeats during the 10 minute interval, and every time the probability
of the heartbeat being unserved is 3/4 or less.
So the probability of failing of all 200 heartbeats is (3/4) ** 200 = 0 from the practical
standpoint.

IN FACT since current implementation sets the rpc timeout to 1 minute, a failed heartbeat
takes
1 minute and 8 seconds to complete, and under this circumstances each data-node can send only
9 heartbeats during the 10 minute interval. Thus, the probability of failing of all 9 of them
is 0.075,
which means that we will loose 45 nodes out of 600 at the end of the 10 minute interval.
>From this point the name-node will be constantly replicating blocks and loosing more nodes,
and
becomes effectively dysfunctional.

A map-reduce framework running on top of it makes things deteriorate even faster, because
failing
tasks and jobs are trying to remove files and re-create them again increasing the overall
load on
the name-node.

I see at least 2 problems that contribute to the chain reaction described above.
1. A heartbeat failure takes too long (1'8").
2. Name-node synchronized operations should be fine-grained.


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