[
https://issues.apache.org/jira/browse/HBASE-6361?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Akashnil updated HBASE-6361:
----------------------------
Description:
Currently the compaction requests are submitted to the minor/major compaction
queue of a region-server from every column-family/region belonging to it. The
requests are processed from the queue in FIFO order (First in First out). We
want to make a lazy scheduler in place of the current queue-based one. The idea
of lazy scheduling is that, it is always better to make a decision (compaction
selection) later if the decision is relevant later only. Presently, when the
queue gets bottle-necked, there is a delay between compaction selection of a
request and its execution. Rather than that, we can postpone the compaction
selection until the queue is empty when we will have more information (new
flush files will have affected the state) to make a better decision.
Removing the queue, we propose to implement a round-robin scheduler. All the
column families in their regions will be visited in sequence periodically. In
each visit, if the column family generates a valid compaction request, the
request is executed before moving to the next one. We do not plan to change the
current compaction algorithm for now. We expect that it will automatically make
a better decision when doing just-in-time selection due to the new change. How
do we know that? Let us consider an example.
Note that the presently existing compaction queue is only relevant as a buffer,
when the flushes out-pace the compactions for a period of time, or a relatively
large compaction consumes time to complete, the queue accumulates requests.
Suppose such a short-term bottleneck has occurred. Suppose min-files for
compaction = 4. For an active column-family, when new flushes are written, new
compaction requests, each of size 4, will be added to the queue continuously
until the queue starts processing them.
Now consider a round-robin scheduler. The effect of a bottle-neck due to the IO
rate of compaction results in a longer latency to visit the same column family
again. When the same active column family is visited following a long delay,
suppose 16 new flush files have been written there. The compaction selection
algorithm will select one compaction request of size 16, as opposed to 4
compaction requests of size 4 that would have been generated in the previous
case.
A compaction request with 16 flush files is more IOPs-efficient than the same
set of files being compacted 4 at a time. This is because both consume the same
total amount of reads and writes while producing a file of size 16 compared to
4 files of size 4. So we obtained a free compaction 4*4->16 without paying for
it. In case of the queue, those smaller 4 sized files would have consumed more
IOPs to become bigger later.
I did some experiments on how a bottle-neck of the queue affects the compaction
selections in the simulator. It appears that, a filled up queue actually makes
all future compaction selections less and less efficient in terms of IOPs,
resulting in a runway positive feedback loop which can sometimes explode the
compaction queue. The main effect of this change should be to deal with bursty
loads. When a bottleneck occurs, the compaction selection will become more
IOPs-efficient rather than less efficient, resulting in negative feedback and
restoration to stability more easily. As for monitoring, the compaction queue
size will not be present as a metric. However, the number of files in each
compaction will indicate if a bottleneck has occurred.
was:
Currently the compaction requests are submitted to the minor/major compaction
queue of a region-server from every column-family/region belonging to it. The
queue processes those requests in FIFO order (First in First out). We want to
make a lazy scheduler in place of the current one. The idea of lazy scheduling
is that, it is always better to make a decision (compaction selection) later if
the decision is relevant later only. Currently, if the queue grows large,
currently generated requests are not processed until all the preceding requests
are executed. Rather than that, we can postpone the compaction selection until
the queue is empty when we will have more information (new flush files will
have affected the state) to make a better decision.
Removing the queue, we propose to implement a round-robin scheduler. All the
column families in their regions will be visited in sequence periodically. In
each visit, if the column family generates a valid compaction request, the
request is executed before moving to the next one. We do not plan to change the
current compaction algorithm for now. We expect that it will automatically make
a better decision when doing just-in-time selection due to the new change. How
do we know that? Let us consider an example.
Note that the presently existing compaction queue is only relevant as a buffer,
when the flushes out-pace the compactions for a period of time, or a relatively
large compaction consumes time to complete, the queue accumulates requests.
Suppose such a scenario has occurred. Suppose min-files for compaction = 4. For
an active column-family, new compaction requests, each of size 4 will be added
to the queue continuously until the queue starts processing them.
Now consider a round-robin scheduler. The effect of a bottle-neck due to the IO
rate of compaction results in a longer latency to visit the same column family
again. By this time suppose there are 16 new flush files in this column family.
The compaction selection algorithm will select a compaction request of size 16,
as opposed to 4 compaction requests of size 4 that would have been generated in
the previous case.
A compaction request with 16 flush files is more IOPs-efficient than the same
set of files being compacted 4 at a time. This is because both consume the same
total amount of reads, total writes, and IOPs/sec while producing a file of
size 16 compared to 4 files of size 4. So we obtained a free compaction from
those 4*4->16 without paying for it. In case of the queue, those smaller files
would have consumed more IOPs to become bigger later.
In case of uniform steady-state load this change should not make a difference,
because the compaction queue would have been empty anyway. However in case of
bursty load, it automatically adapts itself to consume less IOPs in times of
high flush rate. This negative feedback should mainly improve
faliure-resistence of the system. In case something goes wrong, monitoring
should still give feedback, not in the form of queue size, but the number of
files in each compaction, which will go up when the bottle-neck occurs. If
there is no important down-sides, this should be a very good change since this
should apply to all use-cases.
> Change the compaction queue to a round robin scheduler
> ------------------------------------------------------
>
> Key: HBASE-6361
> URL: https://issues.apache.org/jira/browse/HBASE-6361
> Project: HBase
> Issue Type: Improvement
> Reporter: Akashnil
>
> Currently the compaction requests are submitted to the minor/major compaction
> queue of a region-server from every column-family/region belonging to it. The
> requests are processed from the queue in FIFO order (First in First out). We
> want to make a lazy scheduler in place of the current queue-based one. The
> idea of lazy scheduling is that, it is always better to make a decision
> (compaction selection) later if the decision is relevant later only.
> Presently, when the queue gets bottle-necked, there is a delay between
> compaction selection of a request and its execution. Rather than that, we can
> postpone the compaction selection until the queue is empty when we will have
> more information (new flush files will have affected the state) to make a
> better decision.
> Removing the queue, we propose to implement a round-robin scheduler. All the
> column families in their regions will be visited in sequence periodically. In
> each visit, if the column family generates a valid compaction request, the
> request is executed before moving to the next one. We do not plan to change
> the current compaction algorithm for now. We expect that it will
> automatically make a better decision when doing just-in-time selection due to
> the new change. How do we know that? Let us consider an example.
> Note that the presently existing compaction queue is only relevant as a
> buffer, when the flushes out-pace the compactions for a period of time, or a
> relatively large compaction consumes time to complete, the queue accumulates
> requests. Suppose such a short-term bottleneck has occurred. Suppose
> min-files for compaction = 4. For an active column-family, when new flushes
> are written, new compaction requests, each of size 4, will be added to the
> queue continuously until the queue starts processing them.
> Now consider a round-robin scheduler. The effect of a bottle-neck due to the
> IO rate of compaction results in a longer latency to visit the same column
> family again. When the same active column family is visited following a long
> delay, suppose 16 new flush files have been written there. The compaction
> selection algorithm will select one compaction request of size 16, as opposed
> to 4 compaction requests of size 4 that would have been generated in the
> previous case.
> A compaction request with 16 flush files is more IOPs-efficient than the same
> set of files being compacted 4 at a time. This is because both consume the
> same total amount of reads and writes while producing a file of size 16
> compared to 4 files of size 4. So we obtained a free compaction 4*4->16
> without paying for it. In case of the queue, those smaller 4 sized files
> would have consumed more IOPs to become bigger later.
> I did some experiments on how a bottle-neck of the queue affects the
> compaction selections in the simulator. It appears that, a filled up queue
> actually makes all future compaction selections less and less efficient in
> terms of IOPs, resulting in a runway positive feedback loop which can
> sometimes explode the compaction queue. The main effect of this change should
> be to deal with bursty loads. When a bottleneck occurs, the compaction
> selection will become more IOPs-efficient rather than less efficient,
> resulting in negative feedback and restoration to stability more easily. As
> for monitoring, the compaction queue size will not be present as a metric.
> However, the number of files in each compaction will indicate if a bottleneck
> has occurred.
--
This message is automatically generated by JIRA.
If you think it was sent incorrectly, please contact your JIRA administrators:
https://issues.apache.org/jira/secure/ContactAdministrators!default.jspa
For more information on JIRA, see: http://www.atlassian.com/software/jira
