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  1. Hadoop Common
  2. HADOOP-572

Chain reaction in a big cluster caused by simultaneous failure of only a few data-nodes.

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    • Bug
    • Status: Closed
    • Major
    • Resolution: Fixed
    • 0.6.2
    • 0.8.0
    • None
    • None
    • Large dfs cluster


      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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            sameerp Sameer Paranjpye
            shv Konstantin Shvachko
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