hadoop-hdfs-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Zhe Zhang (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HDFS-7285) Erasure Coding Support inside HDFS
Date Tue, 12 May 2015 16:58:09 GMT

    [ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14540208#comment-14540208
] 

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

{quote}
Thanks for your great job about making erasure code native in HDFS. 
I am working on proactive data protection in HDFS by incorporating hard drive failure detection
method based on collected SMART attributes into HDFS kernel and scheduling disk warning process
in advance and want to have erasure code native supported by HDFS kernel instead of HDFS-RAID.
I have some questions below, but I don't know how to consult them , so I just list my questions
here and hope it won't bother you so much.
1, I am wonderring whether and where i can download the project source code you are working
on.
2, When this project will be accomplished, will it take a long time ?
3, Whether guys like me can join your group?
{quote}
Copying [comments | https://issues.apache.org/jira/browse/HDFS-8193?focusedCommentId=14539778&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14539778]
from [~lpstudy] over here and please find my answers below:
# Erasure coding has been developed under the HDFS-7285 branch and the code can be accessed
on github: https://github.com/apache/hadoop/tree/HDFS-7285
# We haven't explicitly discussed the target Hadoop release of the erasure coding feature.
The plan will be discussed here.
# Sure! Contributions are always very welcome. Please feel free to file JIRAs on issues you
see or take existing ones after checking with original assignee.

> 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, HDFSErasureCodingDesign-20141028.pdf,
HDFSErasureCodingDesign-20141217.pdf, HDFSErasureCodingDesign-20150204.pdf, HDFSErasureCodingDesign-20150206.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. 



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

Mime
View raw message