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From "yucai (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-12196) Store blocks in storage devices with hierarchy way
Date Thu, 10 Dec 2015 02:24:10 GMT

     [ https://issues.apache.org/jira/browse/SPARK-12196?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

yucai updated SPARK-12196:
--------------------------
    Description: 
*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.

  was:
*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. Configure 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. Configuration the "nvm", "ssd" location in local dir, 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.


> Store blocks in storage devices with 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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