[
https://issues.apache.org/jira/browse/HDFS-7128?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ming Ma updated HDFS-7128:
--------------------------
Attachment: HDFS-7128.patch
First of all, we need to clarify the replication policy for decomm, finish the
decomm ASAP by spreading the source nodes to all replicas, or let decomm nodes
be the only source nodes for replication?
BlockManager's current replication policy sort of distributes the load to all
replicas for replication. We can argue that is the expected behavior, treat
decomm similar to dead node scenario, replicate blocks ASAP.
The initial patch addresses the issue where decomm nodes' pending replication
queue can be quite large and its impact on certain blocks' replication. It
doesn't change the current ASAP replication policy for decomm scenario.
Appreciate any input on this.
> Decommission slows way down when it gets towards the end
> --------------------------------------------------------
>
> Key: HDFS-7128
> URL: https://issues.apache.org/jira/browse/HDFS-7128
> Project: Hadoop HDFS
> Issue Type: Improvement
> Reporter: Ming Ma
> Attachments: HDFS-7128.patch
>
>
> When we decommission nodes across different racks, the decommission process
> becomes really slow at the end, hardly making any progress. The problem is
> some blocks are on 3 decomm-in-progress DNs and the way how replications are
> scheduled caused unnecessary delay. Here is the analysis.
> When BlockManager schedules the replication work from neededReplication, it
> first needs to pick the source node for replication via chooseSourceDatanode.
> The core policies to pick the source node are:
> 1. Prefer decomm-in-progress node.
> 2. Only pick the nodes whose outstanding replication counts are below
> thresholds dfs.namenode.replication.max-streams or
> dfs.namenode.replication.max-streams-hard-limit, based on the replication
> priority.
> When we decommission nodes,
> 1. All the decommission nodes' blocks will be added to neededReplication.
> 2. BM will pick X number of blocks from neededReplication in each iteration.
> X is based on cluster size and some configurable multiplier. So if the
> cluster has 2000 nodes, X will be around 4000.
> 3. Given these 4000 nodes are on the same decomm-in-progress node A, A end up
> being chosen as the source node of all these 4000 nodes. The reason the
> outstanding replication thresholds don't kick is due to the implementation of
> BlockManager.computeReplicationWorkForBlocks;
> node.getNumberOfBlocksToBeReplicated() remains zero given
> node.addBlockToBeReplicated is called after source node iteration.
> {noformat}
> ...
> synchronized (neededReplications) {
> for (int priority = 0; priority < blocksToReplicate.size();
> priority++) {
> ...
> chooseSourceDatanode
> ...
> }
> for(ReplicationWork rw : work){
> ...
> rw.srcNode.addBlockToBeReplicated(block, targets);
> ...
> }
> {noformat}
>
> 4. So several decomm-in-progress nodes A, B, C end up with 4000
> node.getNumberOfBlocksToBeReplicated().
> 5. If we assume each node can replicate 5 blocks per minutes, it is going to
> take 800 minutes to finish replication of these blocks.
> 6. Pending replication timeout kick in after 5 minutes. The items will be
> removed from the pending replication queue and added back to
> neededReplication. The replications will then be handled by other source
> nodes of these blocks. But the blocks still remain in nodes A, B, C's pending
> replication queue, DatanodeDescriptor.replicateBlocks, so A, B, C continue
> the replications of these blocks, although these blocks might have been
> replicated by other DNs after replication timeout.
> 7. Some block' replicas exist on A, B, C and it is at the end of A's pending
> replication queue. Even though the block's replication timeout, no source
> node can be chosen given A, B, C all have high pending replication count. So
> we have to wait until A drains its pending replication queue. Meanwhile, the
> items in A's pending replication queue have been taken care of by other nodes
> and no longer under replicated.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
