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https://issues.apache.org/jira/browse/HBASE-17215?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16539318#comment-16539318
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Hudson commented on HBASE-17215:
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Results for branch branch-1.4
[build #381 on
builds.a.o|https://builds.apache.org/job/HBase%20Nightly/job/branch-1.4/381/]:
(x) *{color:red}-1 overall{color}*
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details (if available):
(x) {color:red}-1 general checks{color}
-- For more information [see general
report|https://builds.apache.org/job/HBase%20Nightly/job/branch-1.4/381//General_Nightly_Build_Report/]
(x) {color:red}-1 jdk7 checks{color}
-- For more information [see jdk7
report|https://builds.apache.org/job/HBase%20Nightly/job/branch-1.4/381//JDK7_Nightly_Build_Report/]
(x) {color:red}-1 jdk8 hadoop2 checks{color}
-- For more information [see jdk8 (hadoop2)
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(x) {color:red}-1 source release artifact{color}
-- See build output for details.
> Separate small/large file delete threads in HFileCleaner to accelerate
> archived hfile cleanup speed
> ---------------------------------------------------------------------------------------------------
>
> Key: HBASE-17215
> URL: https://issues.apache.org/jira/browse/HBASE-17215
> Project: HBase
> Issue Type: Improvement
> Reporter: Yu Li
> Assignee: Yu Li
> Priority: Major
> Fix For: 2.0.0
>
> Attachments: HBASE-17215.patch, HBASE-17215.v2.patch,
> HBASE-17215.v3.patch
>
>
> When using PCIe-SSD the flush speed will be really quick, and although we
> have per CF flush, we still have the
> {{hbase.regionserver.optionalcacheflushinterval}} setting and some other
> mechanism to avoid data kept in memory for too long to flush small hfiles. In
> our online environment we found the single thread cleaner kept cleaning
> earlier flushed small files while large files got no chance, which caused
> disk full then many other problems.
> Deleting hfiles in parallel with too many threads will also increase the
> workload of namenode, so here we propose to separate large/small hfile
> cleaner threads just like we do for compaction, and it turned out to work
> well in our cluster.
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