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https://issues.apache.org/jira/browse/HBASE-22301?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Andrew Purtell updated HBASE-22301:
-----------------------------------
Description:
Consider the case when a subset of the HDFS fleet is unhealthy but suffering a
gray failure not an outright outage. HDFS operations, notably syncs, are
abnormally slow on pipelines which include this subset of hosts. If the
regionserver's WAL is backed by an impacted pipeline, all WAL handlers can be
consumed waiting for acks from the datanodes in the pipeline (recall that some
of them are sick). Imagine a write heavy application distributing load
uniformly over the cluster at a fairly high rate. With the WAL subsystem slowed
by HDFS level issues, all handlers can be blocked waiting to append to the WAL.
Once all handlers are blocked, the application will experience backpressure.
All (HBase) clients eventually have too many outstanding writes and block.
Because the application is distributing writes near uniformly in the keyspace,
the probability any given service endpoint will dispatch a request to an
impacted regionserver, even a single regionserver, approaches 1.0. So the
probability that all service endpoints will be affected approaches 1.0.
In order to break the logjam, we need to remove the slow datanodes. Although
there is HDFS level monitoring, mechanisms, and procedures for this, we should
also attempt to take mitigating action at the HBase layer as soon as we find
ourselves in trouble. It would be enough to remove the affected datanodes from
the writer pipelines. A super simple strategy that can be effective is
described below:
This is with branch-1 code. I think branch-2's async WAL can mitigate but still
can be susceptible. branch-2 sync WAL is susceptible.
We already roll the WAL writer if the pipeline suffers the failure of a
datanode and the replication factor on the pipeline is too low. We should also
consider how much time it took for the write pipeline to complete a sync the
last time we measured it, or the max over the interval from now to the last
time we checked. If the sync time exceeds a configured threshold, roll the log
writer then too. Fortunately we don't need to know which datanode is making the
WAL write pipeline slow, only that syncs on the pipeline are too slow and
exceeding a threshold. This is enough information to know when to roll it. Once
we roll it, we will get three new randomly selected datanodes. On most clusters
the probability the new pipeline includes the slow datanode will be low. (And
if for some reason it does end up with a problematic datanode again, we roll
again.)
This is not a silver bullet but this can be a reasonably effective mitigation.
Provide a metric for tracking when log roll is requested (and for what reason).
Emit a log line at log roll time that includes datanode pipeline details for
further debugging and analysis, similar to the existing slow FSHLog sync log
line.
If we roll too many times within a short interval of time this probably means
there is a widespread problem with the fleet and so our mitigation is not
helping and may be exacerbating those problems or operator difficulties. Ensure
log roll requests triggered by this new feature happen infrequently enough to
not cause difficulties under either normal or abnormal conditions. A very
simple strategy that could work well under both normal and abnormal conditions
is to define a fairly lengthy interval, default 5 minutes, and then insure we
do not roll more than once during this interval for this reason.
was:
Consider the case when a subset of the HDFS fleet is unhealthy but suffering a
gray failure not an outright outage. HDFS operations, notably syncs, are
abnormally slow on pipelines which include this subset of hosts. If the
regionserver's WAL is backed by an impacted pipeline, all WAL handlers can be
consumed waiting for acks from the datanodes in the pipeline (recall that some
of them are sick). Imagine a write heavy application distributing load
uniformly over the cluster at a fairly high rate. With the WAL subsystem slowed
by HDFS level issues, all handlers can be blocked waiting to append to the WAL.
Once all handlers are blocked, the application will experience backpressure.
All (HBase) clients eventually have too many outstanding writes and block.
Because the application is distributing writes near uniformly in the keyspace,
the probability any given service endpoint will dispatch a request to an
impacted regionserver, even a single regionserver, approaches 1.0. So the
probability that all service endpoints will be affected approaches 1.0.
In order to break the logjam, we need to remove the slow datanodes. Although
there is HDFS level monitoring, mechanisms, and procedures for this, we should
also attempt to take mitigating action at the HBase layer as soon as we find
ourselves in trouble.
This is with branch-1 code. I think branch-2's async WAL can mitigate but still
can be susceptible. branch-2 sync WAL is susceptible.
We already roll the WAL writer if the pipeline suffers the failure of a
datanode and the replication factor on the pipeline is too low. We should also
consider how much time it took for the write pipeline to complete a sync the
last time we measured it, or the max over the interval from now to the last
time we checked. If the sync time exceeds a configured threshold, roll the log
writer then too. Fortunately we don't need to know which datanode is making the
WAL write pipeline slow, only that syncs on the pipeline are too slow and
exceeding a threshold. This is enough information to know when to roll it. Once
we roll it, we will get three new randomly selected datanodes. On most clusters
the probability the new pipeline includes the slow datanode will be low. (And
if for some reason it does end up with a problematic datanode again, we roll
again.)
This is not a silver bullet but this can be a reasonably effective mitigation.
Provide a metric for tracking when log roll is requested (and for what reason).
Emit a log line at log roll time that includes datanode pipeline details for
further debugging and analysis, similar to the existing slow FSHLog sync log
line.
If we roll too many times within a short interval of time this probably means
there is a widespread problem with the fleet and so our mitigation is not
helping and may be exacerbating those problems or operator difficulties. Ensure
log roll requests triggered by this new feature happen infrequently enough to
not cause difficulties under either normal or abnormal conditions. A very
simple strategy that could work well under both normal and abnormal conditions
is to define a fairly lengthy interval, default 5 minutes, and then insure we
do not roll more than once during this interval for this reason.
> Consider rolling the WAL if the HDFS write pipeline is slow
> -----------------------------------------------------------
>
> Key: HBASE-22301
> URL: https://issues.apache.org/jira/browse/HBASE-22301
> Project: HBase
> Issue Type: Improvement
> Components: wal
> Reporter: Andrew Purtell
> Assignee: Andrew Purtell
> Priority: Minor
> Fix For: 3.0.0, 1.5.0, 2.3.0
>
> Attachments: HBASE-22301-branch-1.patch
>
>
> Consider the case when a subset of the HDFS fleet is unhealthy but suffering
> a gray failure not an outright outage. HDFS operations, notably syncs, are
> abnormally slow on pipelines which include this subset of hosts. If the
> regionserver's WAL is backed by an impacted pipeline, all WAL handlers can be
> consumed waiting for acks from the datanodes in the pipeline (recall that
> some of them are sick). Imagine a write heavy application distributing load
> uniformly over the cluster at a fairly high rate. With the WAL subsystem
> slowed by HDFS level issues, all handlers can be blocked waiting to append to
> the WAL. Once all handlers are blocked, the application will experience
> backpressure. All (HBase) clients eventually have too many outstanding writes
> and block.
> Because the application is distributing writes near uniformly in the
> keyspace, the probability any given service endpoint will dispatch a request
> to an impacted regionserver, even a single regionserver, approaches 1.0. So
> the probability that all service endpoints will be affected approaches 1.0.
> In order to break the logjam, we need to remove the slow datanodes. Although
> there is HDFS level monitoring, mechanisms, and procedures for this, we
> should also attempt to take mitigating action at the HBase layer as soon as
> we find ourselves in trouble. It would be enough to remove the affected
> datanodes from the writer pipelines. A super simple strategy that can be
> effective is described below:
> This is with branch-1 code. I think branch-2's async WAL can mitigate but
> still can be susceptible. branch-2 sync WAL is susceptible.
> We already roll the WAL writer if the pipeline suffers the failure of a
> datanode and the replication factor on the pipeline is too low. We should
> also consider how much time it took for the write pipeline to complete a sync
> the last time we measured it, or the max over the interval from now to the
> last time we checked. If the sync time exceeds a configured threshold, roll
> the log writer then too. Fortunately we don't need to know which datanode is
> making the WAL write pipeline slow, only that syncs on the pipeline are too
> slow and exceeding a threshold. This is enough information to know when to
> roll it. Once we roll it, we will get three new randomly selected datanodes.
> On most clusters the probability the new pipeline includes the slow datanode
> will be low. (And if for some reason it does end up with a problematic
> datanode again, we roll again.)
> This is not a silver bullet but this can be a reasonably effective mitigation.
> Provide a metric for tracking when log roll is requested (and for what
> reason).
> Emit a log line at log roll time that includes datanode pipeline details for
> further debugging and analysis, similar to the existing slow FSHLog sync log
> line.
> If we roll too many times within a short interval of time this probably means
> there is a widespread problem with the fleet and so our mitigation is not
> helping and may be exacerbating those problems or operator difficulties.
> Ensure log roll requests triggered by this new feature happen infrequently
> enough to not cause difficulties under either normal or abnormal conditions.
> A very simple strategy that could work well under both normal and abnormal
> conditions is to define a fairly lengthy interval, default 5 minutes, and
> then insure we do not roll more than once during this interval for this
> reason.
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