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Posted to hdfs-dev@hadoop.apache.org by "Yi Liu (JIRA)" <ji...@apache.org> on 2015/08/05 14:56:04 UTC
[jira] [Created] (HDFS-8859) Improve DataNode (ReplicaMap) memory
footprint to save about 45%
Yi Liu created HDFS-8859:
----------------------------
Summary: Improve DataNode (ReplicaMap) memory footprint to save about 45%
Key: HDFS-8859
URL: https://issues.apache.org/jira/browse/HDFS-8859
Project: Hadoop HDFS
Issue Type: Improvement
Components: datanode
Reporter: Yi Liu
Assignee: Yi Liu
By using following approach we can save about *45%* memory footprint for each block replica in DataNode memory (This JIRA only talks about *ReplicaMap* in DataNode), the details are:
In ReplicaMap,
{code}
private final Map<String, Map<Long, ReplicaInfo>> map =
new HashMap<String, Map<Long, ReplicaInfo>>();
{code}
Currently we use a HashMap {{Map<Long, ReplicaInfo>}} to store the replicas in memory. The key is block id of the block replica which is already included in {{ReplicaInfo}}, so this memory can be saved. Also HashMap Entry has a object overhead. We can implement a lightweight Set which is similar to {{LightWeightGSet}}, but not a fixed size ({{LightWeightGSet}} uses fix size for the entries array, usually it's a big value, an example is {{BlocksMap}}, this can avoid full gc since no need to resize), also we should be able to get Element through key.
Following is comparison of memory footprint If we implement a lightweight set as described:
We can save:
{noformat}
SIZE (bytes) ITEM
20 The Key: Long (12 bytes object overhead + 8 bytes long)
12 HashMap Entry object overhead
4 reference to the key in Entry
4 reference to the value in Entry
4 hash in Entry
{noformat}
Total: -44 bytes
We need to add:
{noformat}
SIZE (bytes) ITEM
4 a reference to next element in ReplicaInfo
{noformat}
Total: +4 bytes
So totally we can save 40bytes for each block replica
And currently one finalized replica needs around 46 bytes (notice: we ignore memory alignment here).
We can save 1 - (4 + 46) / (44 + 46) = *45%* memory for each block replica in DataNode.
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