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From "Walter Su (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HDFS-7285) Erasure Coding Support inside HDFS
Date Thu, 25 Jun 2015 03:49:05 GMT

    [ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14600631#comment-14600631
] 

Walter Su commented on HDFS-7285:
---------------------------------

bq. INodeFile#blocks and FileWithStripedBlocksFeature#blocks can no longer use BlockInfoContiguous
and BlockInfoStriped types because they contain both complete and UC blocks.
We can use BlockInfo as an abstraction for complete and UC blocks. We need to change FileWithStripedBlocksFeature#blocks
to BlockInfo as well.

It's doable, because currently in trunk, INodeFile#blocks is BlockInfo and it works fine.
Usually we don't cast BlockInfo to BlockInfoContiguous(or UC). (I didn't find such casting
in trunk code, if we have we should worry type safety)
I saw you cast BlockInfo to BlockInfoStriped multiple times in BlockManager in github branch.
They can be eliminated.

HDFS-8058 is irrelevant because in trunk INodeFile#blocks is already BlockInfo. HDFS-8058
is to reduce memory usage.

> Erasure Coding Support inside HDFS
> ----------------------------------
>
>                 Key: HDFS-7285
>                 URL: https://issues.apache.org/jira/browse/HDFS-7285
>             Project: Hadoop HDFS
>          Issue Type: New Feature
>            Reporter: Weihua Jiang
>            Assignee: Zhe Zhang
>         Attachments: ECAnalyzer.py, ECParser.py, HDFS-7285-initial-PoC.patch, HDFS-EC-Merge-PoC-20150624.patch,
HDFS-bistriped.patch, HDFSErasureCodingDesign-20141028.pdf, HDFSErasureCodingDesign-20141217.pdf,
HDFSErasureCodingDesign-20150204.pdf, HDFSErasureCodingDesign-20150206.pdf, HDFSErasureCodingPhaseITestPlan.pdf,
fsimage-analysis-20150105.pdf
>
>
> Erasure Coding (EC) can greatly reduce the storage overhead without sacrifice of data
reliability, comparing to the existing HDFS 3-replica approach. For example, if we use a 10+4
Reed Solomon coding, we can allow loss of 4 blocks, with storage overhead only being 40%.
This makes EC a quite attractive alternative for big data storage, particularly for cold data.

> Facebook had a related open source project called HDFS-RAID. It used to be one of the
contribute packages in HDFS but had been removed since Hadoop 2.0 for maintain reason. The
drawbacks are: 1) it is on top of HDFS and depends on MapReduce to do encoding and decoding
tasks; 2) it can only be used for cold files that are intended not to be appended anymore;
3) the pure Java EC coding implementation is extremely slow in practical use. Due to these,
it might not be a good idea to just bring HDFS-RAID back.
> We (Intel and Cloudera) are working on a design to build EC into HDFS that gets rid of
any external dependencies, makes it self-contained and independently maintained. This design
lays the EC feature on the storage type support and considers compatible with existing HDFS
features like caching, snapshot, encryption, high availability and etc. This design will also
support different EC coding schemes, implementations and policies for different deployment
scenarios. By utilizing advanced libraries (e.g. Intel ISA-L library), an implementation can
greatly improve the performance of EC encoding/decoding and makes the EC solution even more
attractive. We will post the design document soon. 



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