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https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14680972#comment-14680972
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Zhe Zhang commented on HDFS-7285:
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[~szetszwo]: Thanks for the question.
I posted my planned rebase workflow above: [link 1|
https://issues.apache.org/jira/browse/HDFS-7285?focusedCommentId=14593827&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14593827],
[link 2 |
https://issues.apache.org/jira/browse/HDFS-7285?focusedCommentId=14600282&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14600282],
and a few other places. Based on that plan I already finished rebasing the
branch with 14 squashed sub-patches. Then before pushing to HDFS-7285 branch we
started revisiting HDFS-8499 and the rebase workflow.
The new branch was created simply because we haven't reached an agreement on
the rebase workflow. It enables us to commit pending patches regardless of the
final conclusion on rebase workflow.
Please let me know if that answers your question.
> 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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