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From "Manoj Govindassamy (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (HDFS-11881) NameNode consumes a lot of memory for snapshot diff report generation
Date Sat, 24 Jun 2017 02:44:00 GMT

     [ https://issues.apache.org/jira/browse/HDFS-11881?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel

Manoj Govindassamy updated HDFS-11881:
    Attachment: 2_ArrayList_SnapshotDiffReport.png

[~jojochuang] / [~yzhangal],
  Wrote a test to have 500K files in the snaphot diff report and run the snapshot diff shell
command for 100+ times to see how the heap gets fragmented and the FullGC frequencies. Attached
heap graph for both ArrayList and ChunkedArrayList based implementations of SnapshotDiffReport.
The ArrayList needs quite a frequent LongGC to clear up the heap and to make room for the
new report. Whereas, ChunkedArrayList based SansphotDiffReport needed only less number of
FullGCs for the same test. If we can scale this test to have 10G+ SnapshotDiffReport, then
the differences in heap usages and FullGCs requirement for ArrayList based approach will be
of order of magnitude higher compared to ChunkedArrayList.

  Tried to do similar ChunkedArrayList approach for DirDiff, but soon realized that DirDiff
uses far more functionality in the diff list like add by index, remove by index, set by index
etc. All these index based operations are currently not supported in ChunkedArrayList. So,
will take up this bugger task in a separate jira. 

   Can you please review the patch v01 in the context of FileDiff improvements alone for SnapshotDiffReport

> NameNode consumes a lot of memory for snapshot diff report generation
> ---------------------------------------------------------------------
>                 Key: HDFS-11881
>                 URL: https://issues.apache.org/jira/browse/HDFS-11881
>             Project: Hadoop HDFS
>          Issue Type: Improvement
>          Components: hdfs, snapshots
>    Affects Versions: 3.0.0-alpha1
>            Reporter: Manoj Govindassamy
>            Assignee: Manoj Govindassamy
>         Attachments: 1_ChunkedArrayList_SnapshotDiffReport.png, 2_ArrayList_SnapshotDiffReport.png,
> *Problem:*
> HDFS supports a snapshot diff tool which can generate a [detailed report | https://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/HdfsSnapshots.html#Get_Snapshots_Difference_Report]
of modified, created, deleted and renamed files between any 2 snapshots.
> {noformat}
> hdfs snapshotDiff <path> <fromSnapshot> <toSnapshot>
> {noformat}
> However, if the diff list between 2 snapshots happens to be huge, in the order of millions,
then NameNode can consume a lot of memory while generating the huge diff report. In a few
cases, we are seeing NameNode getting into a long GC lasting for few minutes to make room
for this burst in memory requirement during snapshot diff report generation.
> *RootCause:*
> * NameNode tries to generate the diff report with all diff entries at once which puts
undue pressure 
> * Each diff report entry has the diff type (enum), source path byte array, and destination
path byte array to the minimum. Let's take file deletions use case. For file deletions, there
would be only source or destination paths in the diff report entry. Let's assume these deleted
files on average take 128Bytes for the path. 4 million file deletion captured in diff report
will thus need 512MB of memory 
> * The snapshot diff report uses simple java ArrayList which tries to double its backing
contiguous memory chunk every time the usage factor crosses the capacity threshold. So, a
512MB memory requirement might be internally asking for a much larger contiguous memory chunk
> *Proposal:*
> * Make NameNode snapshot diff report service follow the batch model (like directory listing
service). Clients (hdfs snapshotDiff command) will then receive  diff report in small batches,
and need to iterate several times to get the full list.
> * Additionally, snap diff report service in the NameNode can make use of ChunkedArrayList
data structure instead of the current ArrayList so as to avoid the curse of fragmentation
and large contiguous memory requirement.

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