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From "Andrew Wang (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HDFS-7285) Erasure Coding Support inside HDFS
Date Mon, 10 Aug 2015 22:34:46 GMT

    [ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14680887#comment-14680887
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Andrew Wang commented on HDFS-7285:
-----------------------------------

[~szetszwo]:

* If you have issues with the rebase workflow, let's take it to common-dev. This topic applies
beyond the scope of erasure coding.
* Regarding the refactors, you are talking about an SVN-style merge workflow rather than a
git-style rebase workflow. In the case of BlockInfo, it went through a few mutations on the
EC branch before arriving at the current state. This is because the understanding of BlockInfo+EC
evolved over the course of development. It is prudent to wait until then to do the same refactoring
in trunk, to avoid unnecessary churn on trunk.
* Regarding credit, the original contributor gets credit on the JIRA targeted for the branch,
yes? JIRA assignee is how we credit contributors, and if there are multiple contributors,
a JIRA comment saying as much. If you have examples, let's correct assignees or add comments
to make sure that all contributors are being properly credited.

> 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: Consolidated-20150707.patch, Consolidated-20150806.patch, Consolidated-20150810.patch,
ECAnalyzer.py, ECParser.py, HDFS-7285-initial-PoC.patch, HDFS-7285-merge-consolidated-01.patch,
HDFS-7285-merge-consolidated-trunk-01.patch, HDFS-7285-merge-consolidated.trunk.03.patch,
HDFS-7285-merge-consolidated.trunk.04.patch, HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.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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