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From "Orange (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HBASE-10742) Data temperature aware compaction policy
Date Tue, 12 Jan 2016 08:01:39 GMT

    [ https://issues.apache.org/jira/browse/HBASE-10742?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15093497#comment-15093497

Orange commented on HBASE-10742:

Whether hbase can determine data temperature now?

> Data temperature aware compaction policy
> ----------------------------------------
>                 Key: HBASE-10742
>                 URL: https://issues.apache.org/jira/browse/HBASE-10742
>             Project: HBase
>          Issue Type: Brainstorming
>            Reporter: Andrew Purtell
> Reading "Identifying Hot and Cold Data in Main-Memory Databases" (Levandoski, Larson,
and Stoica), it occurred to me that some of the motivation applies to HBase and some of the
results can inform a data temperature aware compaction policy implementation.
> We also wish to optimize retention of cells in the working set in memory, in blockcache.

> We can also consider further and related performance optimizations in HBase that awareness
of hot and cold data can enable, even for cases where the working set does not fit in memory.
If we could partition HFiles into hot and cold (cold+lukewarm) and move cells between them
at compaction time, then we could:
> - Migrate hot HFiles onto alternate storage tiers with improved read latency and throughput
characteristics. This has been discussed before on HBASE-6572. Or, migrate cold HFiles to
an archival tier.
> - Preload hot HFiles into blockcache to increase cache hit rates, especially when regions
are first brought online. And/or add another LRU priority to increase the likelihood of retention
of blocks in hot HFiles. This could be sufficiently different from ARC to avoid issues there.

> - Reduce the compaction priorities of cold HFiles, with proportional reduction in priority
IO and write amplification, since cold files would less frequently participate in reads.
> Levandoski et. al. describe determining data temperature with low overhead using an out
of band estimation process running in the background over an access log. We could consider
logging reads along with mutations and similarly process the result in the background. The
WAL could be overloaded to carry access log records, or we could follow the approach described
in the paper and maintain an in memory access log only. 
> {quote}
> We chose the offline approach for several reasons. First, as mentioned earlier, the overhead
of even the simplest caching scheme is very high. Second, the offline approach is generic
and requires minimum changes to the database engine. Third, logging imposes very little overhead
during normal operation. Finally, it allows flexibility in when, where, and how to analyze
the log and estimate access frequencies. For instance, the analysis can be done on a separate
machine, thus reducing overhead on the system running the transactional workloads.
> {quote}
> Importantly, they only log a sample of all accesses.
> {quote}
> To implement sampling, we have each worker thread flip a biased coin before starting
a new query (where bias correlates with sample rate). The thread records its accesses in log
buffers (or not) based on the outcome of the coin flip. In Section V, we report experimental
results showing that sampling 10% of the accesses reduces the accuracy by only 2.5%,
> {quote}
> Likewise we would only record a subset of all accesses to limit overheads.
> The offline process estimates access frequencies over discrete time slices using exponential
smoothing. (Markers representing time slice boundaries are interleaved with access records
in the log.) Forward and backward classification algorithms are presented. The forward algorithm
requires a full scan over the log and storage proportional to the number of unique cell addresses,
while the backward algorithm requires reading a least the tail of the log in reverse order.
> If we overload the WAL to carry the access log, offline data temperature estimation can
piggyback as a WAL listener. The forward algorithm would then be a natural choice. The HBase
master is fairly idle most of the time and less memory hungry as a regionserver, at least
in today's architecture. We could probably get away with considering only row+family as a
unique coordinate to minimize space overhead.  Or if instead we maintain the access logs in
memory at the RegionServer, then there is a parallel formulation and we could benefit from
the reverse algorithm's ability to terminate early once confidence bounds are reached and
backwards scanning IO wouldn't be a concern. This handwaves over a lot of details.

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