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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:
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[~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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