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From "Yi Liu (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HDFS-9053) Support large directories efficiently using B-Tree
Date Fri, 11 Sep 2015 13:39:45 GMT

    [ https://issues.apache.org/jira/browse/HDFS-9053?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14740814#comment-14740814

Yi Liu commented on HDFS-9053:

*1. Performance*
I choose 1024 as the B-Tree degree. For ArrayList in tests, the elements are in order, so
for insertion/deletion/getting, we get the index using binary search first and do insertion/deletion/getting,
this is the same behavior as in INodeDirectory.

>From the performance data below, B-Tree is much more better on insertion/deletion, and
almost same for get, for iteration, it's a bit worse (since it's not to iterate continuous
memory), but the iterations are fast enough, so we can ignore.

-- *Insert (random)* (in milliseconds)
||Data Size||B-Tree||ArrayList||

-- *Delete (random)* (in milliseconds)
||Data Size||B-Tree||ArrayList||

-- *Get (random)* (in milliseconds)
||Data Size||B-Tree||ArrayList||

-- *Iteration* (in milliseconds)
||Data Size||B-Tree||ArrayList||

*2. Memory*
As stated in the description, B-Tree is very good because it solves #1, #3 issues from memory
B-Tree may have few object overhead and additional array to store references to sub-trees.
The overhead is relatively very small for large directories; for small directories, the overhead
is small itself. Furthermore it's directory, so few overhead is acceptable, besides, I already
try best effort to reduce the overhead while implementing.

> Support large directories efficiently using B-Tree
> --------------------------------------------------
>                 Key: HDFS-9053
>                 URL: https://issues.apache.org/jira/browse/HDFS-9053
>             Project: Hadoop HDFS
>          Issue Type: Improvement
>          Components: namenode
>            Reporter: Yi Liu
>            Assignee: Yi Liu
>            Priority: Critical
>         Attachments: HDFS-9053 (BTree).patch
> This is a long standing issue, we were trying to improve this in the past.  Currently
we use an ArrayList for the children under a directory, and the children are ordered in the
list, for insert/delete/search, the time complexity is O(log n), but insertion/deleting causes
re-allocations and copies of big arrays, so the operations are costly.  For example, if the
children grow to 1M size, the ArrayList will resize to > 1M capacity, so need > 1M *
4bytes = 4M continuous heap memory, it easily causes full GC in HDFS cluster where namenode
heap memory is already highly used.  I recap the 3 main issues:
> # Insertion/deletion operations in large directories are expensive because re-allocations
and copies of big arrays.
> # Dynamically allocate several MB continuous heap memory which will be long-lived can
easily cause full GC problem.
> # Even most children are removed later, but the directory INode still occupies same size
heap memory, since the ArrayList will never shrink.
> This JIRA is similar to HDFS-7174 created by [~kihwal], but use B-Tree to solve the problem
suggested by [~shv]. 
> So the target of this JIRA is to implement a low memory footprint B-Tree and use it to
replace ArrayList. 
> If the elements size is not large (less than the maximum degree of B-Tree node), the
B-Tree only has one root node which contains an array for the elements. And if the size grows
large enough, it will split automatically, and if elements are removed, then B-Tree nodes
can merge automatically (see more: https://en.wikipedia.org/wiki/B-tree).  It will solve the
above 3 issues.

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