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HBASE-25869 WAL value compression #3244

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merged 12 commits into from
May 21, 2021
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@apurtell apurtell commented May 8, 2021

WAL storage can be expensive, especially if the cell values represented in the edits are large, consisting of blobs or significant lengths of text. Such WALs might need to be kept around for a fairly long time to satisfy replication constraints on a space limited (or space-contended) filesystem.

We have a custom dictionary compression scheme for cell metadata that is engaged when WAL compression is enabled in site configuration. This is fine for that application, where we can expect the universe of values and their lengths in the custom dictionaries to be constrained. For arbitrary cell values it is better to use one of the available compression codecs, which are suitable for arbitrary albeit compressible data.

Microbrenchmark Results

Site configuration used:

<!-- retain all WALs  -->
<property>
  <name>hbase.master.logcleaner.ttl</name>
  <value>604800000</value>
</property>
<!-- enable compression -->
<property>
 <name>hbase.regionserver.wal.enablecompression</name>
 <value>true</value>
</property>
<!-- enable value compression -->
<property>
 <name>hbase.regionserver.wal.value.enablecompression</name>
 <value>true</value>
</property>
<!-- set value compression algorithm —>
<property>
 <name>hbase.regionserver.wal.value.compression.type</name>
 <value>snappy</value>
</property>

Loader: IntegrationTestLoadCommonCrawl

Input: s3n://commoncrawl/crawl-data/CC-MAIN-2021-10/segments/1614178347293.1/warc/CC-MAIN-20210224165708-20210224195708-00000.warc.gz

SNAPPY or ZSTD at level 1 are recommended, all other options provided for comparison.

Microbenchmarks are collected with this change.
Statistics are collected over the lifetime of the regionserver and are dumped at end of test at shutdown. Statistics are updated under synchronization but this is done in a way that excludes that overhead from measurement. The normal patch does not contain either the instrumentation or the synchronization point. Nanoseconds are converted to milliseconds for the table.

Mode WALs aggregate size WALs aggregate size difference WAL writer append time (ms avg)
Default 5,117,369,553 - 0.290 (stdev 0.328)
Compression enabled, value compression not enabled 5,002,683,600 (2.241%) 0.372 (stddev 0.336)
Compression enabled, value compression enabled, v1 patch, Deflate (best speed) 1,209,947,515 (76.4%) 12.694 (stddev 8.48)
Compression enabled, value compression enabled, v2 patch, algorithm=SNAPPY 1,616,387,702 (68.4%) 0.027 (stddev 0.204)
Compression enabled, value compression enabled, v2 patch, algorithm=ZSTD (best speed) 1,149,008,133 (77.55%) 0.043 (stddev 0.195)
Compression enabled, value compression enabled, v2 patch, algorithm=ZSTD (default) 1,089,241,811 (78.7%) 0.056 (stdev 0.310)
Compression enabled, value compression enabled, v2 patch, algorithm=ZSTD (best compression) 941,452,655 (81.2%) 0.231 (stddev 1.11)
Options below not recommended. - - -
Compression enabled, value compression enabled, v2 patch, algorithm=GZ 1,082,414,015 (78.9%) 0.267 (stddev 1.325)
Compression enabled, value compression enabled, v2 patch, algorithm=LZMA (level 1) 1,013,951,637 (80.2%) 2.157 (stddev 3.302)
Compression enabled, value compression enabled, v2 patch, algorithm=LZMA (default) 940,884,618 (81.7%) 4.739 (stdev 8.609)

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apurtell commented May 8, 2021

Clean up the commit message.

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apurtell commented May 8, 2021

I broke trunk on an unrelated issue last night. Rebased. Removed some precommit noise that resulted.

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@apurtell apurtell force-pushed the HBASE-25869 branch 2 times, most recently from ca44233 to b8ff445 Compare May 9, 2021 22:45
@apurtell apurtell changed the title [WIP] HBASE-25869 WAL value compression HBASE-25869 WAL value compression May 9, 2021
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Apache9 commented May 10, 2021

So we will only compress value?

As we will do batching when writing WAL entries out, is it possible to compress when flushing? The data will be larger and compress may perform better. The structure of a WAL file will be multiple compressed blocks.

I think for AsyncFSWAL this is easy as we will buffer all the entries in memory and when flush is called we flush all the in memory data out. For FSHLog it will be a bit hard as we rely on the DFSOutputStream to do flush if the data is too big before we actually call hflush.

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apurtell commented May 10, 2021

So we will only compress value?

This is an enhancement to existing WAL compression. As you know the existing WAL compression already compresses other aspects of WAL entries except for the value. This patch adds support for compressing values too.

As we will do batching when writing WAL entries out, is it possible to compress when flushing? The data will be larger and compress may perform better. The structure of a WAL file will be multiple compressed blocks.

This is not possible for two reasons:

  1. WALCellCodec does not compress the WAL file in blocks. The design is edit by edit. I want to introduce value compression without re-engineering the whole WAL format. Perhaps our WAL file format is due for a redesign, but I would like to see that be a different issue.

  2. We flush the compressor at the end of every value to ensure each WALedit record persists all of the value data into the expected place. Otherwise the compressor would put some of the unflushed output of the previous value into the next/current value. But, we are not resetting the compressor. (That would be FULL_FLUSH. We are using SYNC_FLUSH.) By using the same Deflater instance for the whole WAL we already get the benefit you are thinking of. The (re-used) Deflater is able to build its dictionary across the contents of all of the values in the file, not just each value considered in isolation (that was the original patch but I pushed an improvement that aligns with this suggestion later), achieving a better compression.

Way back in the distant past our WAL format was based on Hadoop's SequenceFile, which supported both record-by-record and block based compression, where the blocks would contain multiple records. I don't remember why we moved away from it but I imagine it was because if there are corruptions of the WAL, a record by record codec is able to skip over the corrupt record and we lose only the record (or as many records as are actually corrupt), but with a block format we would lose the whole block and all of the edits contained within that block, especially if compression or encryption is enabled.

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apurtell commented May 19, 2021

@bharathv I wrote a simple bounded delegating input stream impl to avoid the unnecessary copy at decompression time. Rebased on master.

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@apurtell
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apurtell commented May 19, 2021

I am redoing microbenchmarks with the latest patch and will update here soon. Improvements have unlocked IO performance improvement from the compression.

Algorithm Avg WAL append in ms - Before Avg WAL append in ms - Now
None 0.290 (stdev 0.328) unchanged
Compression, but no value compression 0.372 (stddev 0.336) unchanged
Compression, value compression=SNAPPY 2.971 (stddev 2.029) 0.0272 (stddev 0.204)

Before, SNAPPY+copy had a 10x loss, now SNAPPY+no-copy has a 10x gain. I double checked these findings and it seems correct. I will re-run the benchmark when measuring for the other codec types for comparison.

Microbenchmarks are collected with this change.
Statistics are collected over the lifetime of the regionserver and are dumped at end of test at shutdown. Statistics are updated under synchronization but this is done in a way that excludes that overhead from measurement. The normal patch does not contain either the instrumentation or the synchronization point. Nanoseconds are converted to milliseconds for the table.

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🎊 +1 overall

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SNAPPY or ZSTD are recommended, all other options provided for comparison. (LZMA is included as a sanity check that indeed an expensive algorithm really is expensive.)

When using SNAPPY or ZSTD we derive a performance benefit due to reduced IO for the large values in the test case.

Microbenchmarks are collected with this change.
Statistics are collected over the lifetime of the regionserver and are dumped at end of test at shutdown. Statistics are updated under synchronization but this is done in a way that excludes that overhead from measurement. The normal patch does not contain either the instrumentation or the synchronization point. Nanoseconds are converted to milliseconds for the table.

Mode WALs aggregate size WALs aggregate size difference WAL writer append time (ms avg)
Default 5,117,369,553 - 0.290 (stdev 0.328)
Compression enabled, value compression not enabled 5,002,683,600 (2.241%) 0.372 (stddev 0.336)
Compression enabled, value compression enabled, v1 patch, Deflate (best speed) 1,209,947,515 (76.4%) 12.694 (stddev 8.48)
Compression enabled, value compression enabled, v2 patch, algorithm=SNAPPY 1,616,387,702 (68.4%) 0.027 (stddev 0.204)
Compression enabled, value compression enabled, v2 patch, algorithm=ZSTD (best speed) 1,149,008,133 (77.55%) 0.043 (stddev 0.195)
Compression enabled, value compression enabled, v2 patch, algorithm=ZSTD (default) 1,089,241,811 (78.7%) 0.056 (stdev 0.310)
Compression enabled, value compression enabled, v2 patch, algorithm=ZSTD (best compression) 941,452,655 (81.2%) 0.231 (stddev 1.11)
Options below not recommended. - - -
Compression enabled, value compression enabled, v2 patch, algorithm=GZ 1,082,414,015 (78.9%) 0.267 (stddev 1.325)
Compression enabled, value compression enabled, v2 patch, algorithm=LZMA (level 1) 1,013,951,637 (80.2%) 2.157 (stddev 3.302)
Compression enabled, value compression enabled, v2 patch, algorithm=LZMA (default) 940,884,618 (81.7%) 4.739 (stdev 8.609)

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bharathv commented May 20, 2021

SNAPPY+copy had a 10x loss, now SNAPPY+no-copy has a 10x gain. I double checked these findings and it seems correct. I will re-run the benchmark when measuring for the other codec types for comparison.

Holy guacamole! Is this because of the reduced disk IO with compressed values? I'm glad we regained all the lost performance by eliding the copy.

Edit: Just saw your last comment "When using SNAPPY or ZSTD we derive a performance benefit due to reduced IO for the large values in the test case." Sweeeeet!

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+1 with one pending suggestion and green QA.

* is finished.
*/
@InterfaceAudience.Private
public class BoundedDelegatingInputStream extends DelegatingInputStream {
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We can merge this and DelegatingInputStream? Don't think we need both of them separately.

public class BoundedDelegatingInputStream extends FilterInputStream {
 ....   < bounding methods>...
 setDelegate() {}
}

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I thought we could keep them both.

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🎊 +1 overall

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💔 -1 overall

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@apurtell
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Unit test failures are not related. It looks like master is recently unstable, related to RS groups.

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Unit test failures are not related. It looks like master is recently unstable, related to RS groups.

That failure looks suspicious. Let me grab the logs before you merge/close the PR.

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I have what's available.

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@ndimiduk

That failure looks suspicious. Let me grab the logs before you merge/close the PR.

How can it be related? That test neither enables WAL compression nor WAL value compression.

org.junit.runners.model.TestTimedOutException: test timed out after 780 seconds
    at org.apache.hadoop.hbase.master.TestMergeTableRegionsWhileRSCrash.test(TestMergeTableRegionsWhileRSCrash.java:112)

Test log is full of:

java.net.ConnectException: Call to address=33ea6bbd4f8c:42363 null failed on connection exception:
org.apache.hbase.thirdparty.io.netty.channel.AbstractChannel$AnnotatedConnectException:
finishConnect(..) failed: Connection refused: 33ea6bbd4f8c/172.17.0.2:42363

Passes on my dev branch:

[INFO] -------------------------------------------------------
[INFO]  T E S T S
[INFO] -------------------------------------------------------
[INFO] Running org.apache.hadoop.hbase.master.TestMergeTableRegionsWhileRSCrash
[INFO] Tests run: 1, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 38.5 s - in org.apache.hadoop.hbase.master.TestMergeTableRegionsWhileRSCrash
[INFO] 
[INFO] Results:
[INFO] 
[INFO] Tests run: 1, Failures: 0, Errors: 0, Skipped: 0

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Pushed a fix for whitespace and javadoc issues introduced in last change. No additional changes anticipated from this point.

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🎊 +1 overall

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@ndimiduk

That failure looks suspicious. Let me grab the logs before you merge/close the PR.

How can it be related? That test neither enables WAL compression nor WAL value compression.

Parton Andrew. I did not mean for "suspicious" to imply "caused by this changeset," merely that I was interested in looking at it further. All the PR build artifacts are purged from Jenkins after the PR is closed.

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🎊 +1 overall

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🎊 +1 overall

Vote Subsystem Runtime Comment
+0 🆗 reexec 1m 16s Docker mode activated.
-0 ⚠️ yetus 0m 2s Unprocessed flag(s): --brief-report-file --spotbugs-strict-precheck --whitespace-eol-ignore-list --whitespace-tabs-ignore-list --quick-hadoopcheck
_ Prechecks _
_ master Compile Tests _
+0 🆗 mvndep 0m 29s Maven dependency ordering for branch
+1 💚 mvninstall 4m 9s master passed
+1 💚 compile 2m 15s master passed
+1 💚 shadedjars 9m 31s branch has no errors when building our shaded downstream artifacts.
+1 💚 javadoc 1m 22s master passed
_ Patch Compile Tests _
+0 🆗 mvndep 0m 16s Maven dependency ordering for patch
+1 💚 mvninstall 4m 23s the patch passed
+1 💚 compile 2m 14s the patch passed
+1 💚 javac 2m 14s the patch passed
+1 💚 shadedjars 9m 2s patch has no errors when building our shaded downstream artifacts.
+1 💚 javadoc 1m 24s the patch passed
_ Other Tests _
+1 💚 unit 0m 54s hbase-protocol-shaded in the patch passed.
+1 💚 unit 2m 17s hbase-common in the patch passed.
+1 💚 unit 225m 19s hbase-server in the patch passed.
267m 14s
Subsystem Report/Notes
Docker ClientAPI=1.41 ServerAPI=1.41 base: https://ci-hadoop.apache.org/job/HBase/job/HBase-PreCommit-GitHub-PR/job/PR-3244/17/artifact/yetus-jdk8-hadoop3-check/output/Dockerfile
GITHUB PR #3244
Optional Tests javac javadoc unit shadedjars compile
uname Linux bf3385fe68e2 4.15.0-142-generic #146-Ubuntu SMP Tue Apr 13 01:11:19 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux
Build tool maven
Personality dev-support/hbase-personality.sh
git revision master / 7c24ed4
Default Java AdoptOpenJDK-1.8.0_282-b08
Test Results https://ci-hadoop.apache.org/job/HBase/job/HBase-PreCommit-GitHub-PR/job/PR-3244/17/testReport/
Max. process+thread count 2571 (vs. ulimit of 30000)
modules C: hbase-protocol-shaded hbase-common hbase-server U: .
Console output https://ci-hadoop.apache.org/job/HBase/job/HBase-PreCommit-GitHub-PR/job/PR-3244/17/console
versions git=2.17.1 maven=3.6.3
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@apurtell
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Thanks. Unless objection I will merge this tonight or tomorrow morning, to master, and then to branch-2 (for future 2.5.0)

@apurtell apurtell merged commit 8ec6fd9 into apache:master May 21, 2021
@apurtell apurtell deleted the HBASE-25869 branch May 21, 2021 18:05
asfgit pushed a commit that referenced this pull request May 21, 2021
WAL storage can be expensive, especially if the cell values
represented in the edits are large, consisting of blobs or
significant lengths of text. Such WALs might need to be kept around
for a fairly long time to satisfy replication constraints on a space
limited (or space-contended) filesystem.

We have a custom dictionary compression scheme for cell metadata that
is engaged when WAL compression is enabled in site configuration.
This is fine for that application, where we can expect the universe
of values and their lengths in the custom dictionaries to be
constrained. For arbitrary cell values it is better to use one of the
available compression codecs, which are suitable for arbitrary albeit
compressible data.

Signed-off-by: Bharath Vissapragada <bharathv@apache.org>
Signed-off-by: Duo Zhang <zhangduo@apache.org>
Signed-off-by: Nick Dimiduk <ndimiduk@apache.org>
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6 participants