[jira] [Commented] (SPARK-12196) Store blocks in different speed storage devices by hierarchy way

["Zhang, Liye (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-12196?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15071354#comment-15071354 ] 

Zhang, Liye commented on SPARK-12196:
-------------------------------------

I am out of office with limited email access from 12/21/2015 to 12/25/2015. Sorry for slow email response. Any emergency, contact my manager (Cheng, Hao hao.cheng@intel.com). Thanks


> Store blocks in different speed storage devices by hierarchy way
> ----------------------------------------------------------------
>
>                 Key: SPARK-12196
>                 URL: https://issues.apache.org/jira/browse/SPARK-12196
>             Project: Spark
>          Issue Type: New Feature
>          Components: Spark Core
>            Reporter: yucai
>
> *Problem*
> Nowadays, users have both SSDs and HDDs. 
> SSDs have great performance, but capacity is small. HDDs have good capacity, but x2-x3 lower than SSDs.
> How can we get both good?
> *Solution*
> Our idea is to build hierarchy store: use SSDs as cache and HDDs as backup storage. 
> When Spark core allocates blocks for RDD (either shuffle or RDD cache), it gets blocks from SSDs first, and when SSD’s useable space is less than some threshold, getting blocks from HDDs.
> In our implementation, we actually go further. We support a way to build any level hierarchy store access all storage medias (NVM, SSD, HDD etc.).
> *Performance*
> 1. At the best case, our solution performs the same as all SSDs.
> 2. At the worst case, like all data are spilled to HDDs, no performance regression.
> 3. Compared with all HDDs, hierarchy store improves more than *_x1.86_* (it could be higher, CPU reaches bottleneck in our test environment).
> 4. Compared with Tachyon, our hierarchy store still *_x1.3_* faster. Because we support both RDD cache and shuffle and no extra inter process communication.
> *Usage*
> 1. Set the priority and threshold for each layer in spark.storage.hierarchyStore.
> {code}
> spark.storage.hierarchyStore='nvm 50GB,ssd 80GB'
> {code}
> It builds a 3 layers hierarchy store: the 1st is "nvm", the 2nd is "sdd", all the rest form the last layer.
> 2. Configure each layer's location, user just needs put the keyword like "nvm", "ssd", which are specified in step 1, into local dirs, like spark.local.dir or yarn.nodemanager.local-dirs.
> {code}
> spark.local.dir=/mnt/nvm1,/mnt/ssd1,/mnt/ssd2,/mnt/ssd3,/mnt/disk1,/mnt/disk2,/mnt/disk3,/mnt/disk4,/mnt/others
> {code}
> After then, restart your Spark application, it will allocate blocks from nvm first.
> When nvm's usable space is less than 50GB, it starts to allocate from ssd.
> When ssd's usable space is less than 80GB, it starts to allocate from the last layer.



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