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https://issues.apache.org/jira/browse/HDFS-9053?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14934634#comment-14934634
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Yi Liu edited comment on HDFS-9053 at 9/29/15 5:15 AM:
-------------------------------------------------------
Thanks for the comments, Nicholas.
{quote}
Where are the numbers, especially the 4s, from? Do we assume a 32-bit world?
{quote}
{code}
public class BTree<K, E extends BTree.Element<K>> implements Iterable<E> {
...
private final int degree;
private Node root;
private int size;
private transient int modCount = 0;
...
}
private final class Node {
static final int DEFAULT_CAPACITY = 5;
private Object[] elements;
private int elementsSize;
private Object[] children;
private int childrenSize;
...
}
{code}
Sorry, I should use 64-bits system/JVM to describe, and details are:
Compared to ArrayList, we increases following things:
private final int degree; <--------- 4 bytes Integer
private Node root; <--------- reference, 4 bytes on 32-bits
system/JVM, 8 bytes on 64-bits system/JVM
private int size; <--------- 4 bytes Integer
{{Node}} object overhead <---------- 12 bytes
private Object[] children; <--------- null reference, 4 bytes on
32-bits system/JVM, 8 bytes on 64-bits system/JVM
private int childrenSize; <--------- 4 bytes Integer.
So totally 12+4+4+4+4+4+4 = 32 bytes on 32-bits system/JVM, and 12+4+8+4+8+4 =
40 bytes on 64-bits system/JVM. (I have not counted object alignment)
was (Author: hitliuyi):
Thanks for the comments, Nicholas.
{quote}
Where are the numbers, especially the 4s, from? Do we assume a 32-bit world?
{quote}
{code}
public class BTree<K, E extends BTree.Element<K>> implements Iterable<E> {
...
private final int degree;
private Node root;
private int size;
private transient int modCount = 0;
...
}
private final class Node {
static final int DEFAULT_CAPACITY = 5;
private Object[] elements;
private int elementsSize;
private Object[] children;
private int childrenSize;
...
}
{code}
Sorry, I should use 64-bits system/JVM, and details are:
Compared to ArrayList, we increases following things:
private final int degree; <--------- 4 bytes Integer
private Node root; <--------- reference, 4 bytes on 32-bits
system/JVM, 8 bytes on 64-bits system/JVM
private int size; <--------- 4 bytes Integer
{{Node}} object overhead <---------- 12 bytes
private Object[] children; <--------- null reference, 4 bytes on
32-bits system/JVM, 8 bytes on 64-bits system/JVM
private int childrenSize; <--------- 4 bytes Integer.
So totally 12+4+4+4+4+4+4 = 32 bytes on 32-bits system/JVM, and 12+4+8+4+8+4 =
40 bytes on 64-bits system/JVM. (I have not counted object alignment)
> 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 with simple benchmark).patch, HDFS-9053
> (BTree).patch, HDFS-9053.001.patch, HDFS-9053.002.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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