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https://issues.apache.org/jira/browse/HDFS-9053?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14740370#comment-14740370
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Yi Liu commented on HDFS-9053:
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Current patch only includes my implementation of B-Tree, I will post the patch
integration with {{INodeDirectory}} later.
Code logic of B-Tree is more complex than other normal data structure, the most
challenged part is remove/merge, insert/split, iteration. I refer to google
implementation of B-Tree in Go language (https://github.com/google/btree), and
borrow their merge, split logic. So the correctness of that part should be
already verified in practice. I checked the B-Tree implementation carefully
according to B-Tree definition, also did many tests to make sure B-Tree
implementation correct. Later, I will give some performance data of
insertion/deletion/searching comparing between B-Tree and ArrayList.
The implementation in patch is low memory footprint, if the elements size is
not large (less than the maximum degree of B-Tree node), the B-Tree will only
have one root node and contains an array for the elements, and the children
array is null. I use java array for the elements and children, and control the
expand in the code myself to save the object overhead and can use fewer memory
compared to use ArrayList.
> 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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