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From "Liyin Tang (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (HBASE-6361) Change the compaction queue to a round robin scheduler
Date Tue, 10 Jul 2012 18:44:35 GMT

     [ https://issues.apache.org/jira/browse/HBASE-6361?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Liyin Tang reassigned HBASE-6361:
---------------------------------

    Assignee: Akashnil
    
> 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
>            Assignee: 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 and choices (new flush files will have arrived
by then) 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.
> Suppose there is a short term bottleneck in the queue so that it is blocked for a period
of time. (Let the 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, in the second
case, 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.
> On my simulator, I did some experiments on how a bottle-neck of the queue affects the
compaction selections in the current system. 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 potentially explode the compaction queue. (This was
also observed in production recently). 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.

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