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From "Zhe Zhang (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HDFS-7285) Erasure Coding Support inside HDFS
Date Tue, 27 Jan 2015 01:09:40 GMT

    [ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14292775#comment-14292775
] 

Zhe Zhang commented on HDFS-7285:
---------------------------------

I created a Google [doc | https://docs.google.com/document/d/12YbFDFQGJkx9aCPmtJVslgCIWC6ULnmJQxrIm-e8XFs/edit?usp=sharing]
which is editable. If possible please login so I know who's making each comment and update.

[~szetszwo] The doc was last updated in mid December and doesn't contain some of the latest
updates (mainly from HDFS-7339). If you don't mind the wait I plan to finish updating it before
Wednesday. You can also go ahead with your updates assuming the HDFS-7339 discussions were
incorporated.

Package naming is an interesting topic. *Erasure* doesn't sound very appropriate because it
literally means "the act of erasing something" and is a bit ambiguous itself.  Actually erasure
coding is [a type of error correction codes | http://en.wikipedia.org/wiki/Forward_error_correction#List_of_error-correcting_codes]
so we don't need to worry about the conflict with "error correction". The only way to decrease
ambiguity in general is to enlengthen the abbreviation. Two potential candidates came to mind:
*ecc* standing for "error correction codes"; or *erc* "standing for "erasure coding" more
specifically. Thoughts?

> 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, HDFSErasureCodingDesign-20141028.pdf,
HDFSErasureCodingDesign-20141217.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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