You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Zhang, Liye (JIRA)" <ji...@apache.org> on 2016/04/11 07:03:25 UTC
[jira] [Comment Edited] (SPARK-13352) BlockFetch does not scale
well on large block
[ https://issues.apache.org/jira/browse/SPARK-13352?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15234488#comment-15234488 ]
Zhang, Liye edited comment on SPARK-13352 at 4/11/16 5:02 AM:
--------------------------------------------------------------
Hi [~davies], I think this JIRA is related with [SPARK-14242|https://issues.apache.org/jira/browse/SPARK-142242] and [SPARK-14290|https://issues.apache.org/jira/browse/SPARK-14290], can you test with spark master branch again to see if this issue still exists?
was (Author: liyezhang556520):
Hi [~davies], I think this JIRA is related with [SPARK-14242|https://issues.apache.org/jira/browse/SPARK-142242] and [SPARK-14290|https://issues.apache.org/jira/browse/SPARK-14290], can you test with spark master again to see if this issue still exists?
> BlockFetch does not scale well on large block
> ---------------------------------------------
>
> Key: SPARK-13352
> URL: https://issues.apache.org/jira/browse/SPARK-13352
> Project: Spark
> Issue Type: Bug
> Components: Block Manager, Spark Core
> Reporter: Davies Liu
> Priority: Critical
>
> BlockManager.getRemoteBytes() perform poorly on large block
> {code}
> test("block manager") {
> val N = 500 << 20
> val bm = sc.env.blockManager
> val blockId = TaskResultBlockId(0)
> val buffer = ByteBuffer.allocate(N)
> buffer.limit(N)
> bm.putBytes(blockId, buffer, StorageLevel.MEMORY_AND_DISK_SER)
> val result = bm.getRemoteBytes(blockId)
> assert(result.isDefined)
> assert(result.get.limit() === (N))
> }
> {code}
> Here are runtime for different block sizes:
> {code}
> 50M 3 seconds
> 100M 7 seconds
> 250M 33 seconds
> 500M 2 min
> {code}
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org