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https://issues.apache.org/jira/browse/HDFS-16303?focusedWorklogId=699038&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-699038
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ASF GitHub Bot logged work on HDFS-16303:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 20/Dec/21 23:46
Start Date: 20/Dec/21 23:46
Worklog Time Spent: 10m
Work Description: KevinWikant edited a comment on pull request #3675:
URL: https://github.com/apache/hadoop/pull/3675#issuecomment-998300466
@virajjasani, please see my response to your comments below
> hence if few nodes are really in bad state (hardware/network issues), the
plan is to keep re-queueing them until more nodes are getting decommissioned
than max tracked nodes right?
It's the opposite, the unhealthy nodes will only be re-queued when there are
more nodes being decommissioned than max tracked nodes. Otherwise, if there are
fewer nodes being decommissioned than max tracked nodes, then the unhealthy
nodes will not be re-queued because they do not risk blocking the
decommissioning of queued healthy nodes (i.e. because the queue is empty).
One potential performance impact that comes to mind is that if there are say
200 unhealthy decommissioning nodes & max tracked nodes = 100, then this may
cause some churn in the queueing/de-queueing process because each
DatanodeAdminMonitor tick all 100 tracked nodes will be re-queued & then 100
queued nodes will be de-queued/tracked. Note that this churn (and any
associated performance impact) will only take effect when:
- there are more nodes being decommissioned than max tracked nodes
- AND either:
- number of healthy decommissioning nodes < max tracked nodes
- number of unhealthy decommissioning nodes > max tracked nodes
The amount of re-queued/de-queued nodes per tick can be quantified as:
`numRequeue = numDecommissioning <= numTracked ? 0 : numDeadDecommissioning
- (numDecommissioning - numTracked)`
This churn of queueing/de-queueing will not occur at all under typical
decommissioning scenarios (i.e. where there isn't a large number of dead
decommissioning nodes).
One idea to mitigate this is to have DatanodeAdminMonitor maintain counters
used to track the number of healthy nodes in the pendingNodes queue; then this
count can be used to make an improved re-queue decision. In particular,
unhealthy nodes are only re-queued if there are healthy nodes in the
pendingNodes queue. But this approach has some flaws, for example an unhealthy
node in the queue could come alive again, but then an unhealthy node in the
tracked set wouldn't be re-queued because the healthy queued node count hasn't
been updated. To solve this, we would need to scan the pendingNodes queue to
update the healthy/unhealthy node counts periodically, this scan could prove
expensive.
> Since unhealthy node getting decommissioned might anyways require some
sort of retry, shall we requeue them even if the condition is not met (i.e.
total no of decomm in progress < max tracked nodes) as a limited retries?
If there are fewer nodes being decommissioned than max tracked nodes, then
there are no nodes in the pendingNodes queue & all nodes are being tracked for
decommissioning. Therefore, there is no possibility that any healthy nodes are
blocked in the pendingNodes queue (preventing them from being decommissioned) &
so in my opinion there is no benefit to re-queueing the unhealthy nodes in this
case. Furthermore, this will negatively impact performance through frequent
re-queueing & de-queueing.
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Issue Time Tracking
-------------------
Worklog Id: (was: 699038)
Time Spent: 10h 20m (was: 10h 10m)
> Losing over 100 datanodes in state decommissioning results in full blockage
> of all datanode decommissioning
> -----------------------------------------------------------------------------------------------------------
>
> Key: HDFS-16303
> URL: https://issues.apache.org/jira/browse/HDFS-16303
> Project: Hadoop HDFS
> Issue Type: Bug
> Affects Versions: 2.10.1, 3.3.1
> Reporter: Kevin Wikant
> Assignee: Kevin Wikant
> Priority: Major
> Labels: pull-request-available
> Time Spent: 10h 20m
> Remaining Estimate: 0h
>
> h2. Impact
> HDFS datanode decommissioning does not make any forward progress. For
> example, the user adds X datanodes to the "dfs.hosts.exclude" file and all X
> of those datanodes remain in state decommissioning forever without making any
> forward progress towards being decommissioned.
> h2. Root Cause
> The HDFS Namenode class "DatanodeAdminManager" is responsible for
> decommissioning datanodes.
> As per this "hdfs-site" configuration:
> {quote}Config = dfs.namenode.decommission.max.concurrent.tracked.nodes
> Default Value = 100
> The maximum number of decommission-in-progress datanodes nodes that will be
> tracked at one time by the namenode. Tracking a decommission-in-progress
> datanode consumes additional NN memory proportional to the number of blocks
> on the datnode. Having a conservative limit reduces the potential impact of
> decomissioning a large number of nodes at once. A value of 0 means no limit
> will be enforced.
> {quote}
> The Namenode will only actively track up to 100 datanodes for decommissioning
> at any given time, as to avoid Namenode memory pressure.
> Looking into the "DatanodeAdminManager" code:
> * a new datanode is only removed from the "tracked.nodes" set when it
> finishes decommissioning
> * a new datanode is only added to the "tracked.nodes" set if there is fewer
> than 100 datanodes being tracked
> So in the event that there are more than 100 datanodes being decommissioned
> at a given time, some of those datanodes will not be in the "tracked.nodes"
> set until 1 or more datanodes in the "tracked.nodes" finishes
> decommissioning. This is generally not a problem because the datanodes in
> "tracked.nodes" will eventually finish decommissioning, but there is an edge
> case where this logic prevents the namenode from making any forward progress
> towards decommissioning.
> If all 100 datanodes in the "tracked.nodes" are unable to finish
> decommissioning, then other datanodes (which may be able to be
> decommissioned) will never get added to "tracked.nodes" and therefore will
> never get the opportunity to be decommissioned.
> This can occur due the following issue:
> {quote}2021-10-21 12:39:24,048 WARN
> org.apache.hadoop.hdfs.server.blockmanagement.BlockManager
> (DatanodeAdminMonitor-0): Node W.X.Y.Z:50010 is dead while in Decommission In
> Progress. Cannot be safely decommissioned or be in maintenance since there is
> risk of reduced data durability or data loss. Either restart the failed node
> or force decommissioning or maintenance by removing, calling refreshNodes,
> then re-adding to the excludes or host config files.
> {quote}
> If a Datanode is lost while decommissioning (for example if the underlying
> hardware fails or is lost), then it will remain in state decommissioning
> forever.
> If 100 or more Datanodes are lost while decommissioning over the Hadoop
> cluster lifetime, then this is enough to completely fill up the
> "tracked.nodes" set. With the entire "tracked.nodes" set filled with
> datanodes that can never finish decommissioning, any datanodes added after
> this point will never be able to be decommissioned because they will never be
> added to the "tracked.nodes" set.
> In this scenario:
> * the "tracked.nodes" set is filled with datanodes which are lost & cannot
> be recovered (and can never finish decommissioning so they will never be
> removed from the set)
> * the actual live datanodes being decommissioned are enqueued waiting to
> enter the "tracked.nodes" set (and are stuck waiting indefinitely)
> This means that no progress towards decommissioning the live datanodes will
> be made unless the user takes the following action:
> {quote}Either restart the failed node or force decommissioning or maintenance
> by removing, calling refreshNodes, then re-adding to the excludes or host
> config files.
> {quote}
> Ideally, the Namenode should be able to gracefully handle scenarios where the
> datanodes in the "tracked.nodes" set are not making forward progress towards
> decommissioning while the enqueued datanodes may be able to make forward
> progress.
> h2. Reproduce Steps
> * create a Hadoop cluster
> * lose (i.e. terminate the host/process forever) over 100 datanodes while
> the datanodes are in state decommissioning
> * add additional datanodes to the cluster
> * attempt to decommission those new datanodes & observe that they are stuck
> in state decommissioning forever
> Note that in this example each datanode, over the full history of the
> cluster, has a unique IP address
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