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From "Tsz Wo Nicholas Sze (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HDFS-7285) Erasure Coding Support inside HDFS
Date Wed, 01 Apr 2015 19:23:02 GMT

    [ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14391274#comment-14391274

Tsz Wo Nicholas Sze commented on HDFS-7285:

Here is the list we discussed.

h2. Phase 1 – Basic EC features
- Support (6,3)-Reed-Solomon
- Read 
-*  from closed EC files
-*  from files with some missing blocks
- Write
-*  Write to 9 datanodes in parallel
-*  Failure handling: continue writing with the remaining datanodes as long as #existing datanodes
>= 6.
- EC blocks reconstruction 
-*  Scheduled by NN like replication
-*  Datanode executes block group reconstruction
- Block group lease recovery
-*  Datanode executes lease recovery
-*  Truncate at stripe group boundary
- NN changes
-*  EC block group placement
-*  EC zone
-*  Safemode calculation
-*  Quota
-*  Block report processing
-*  Snapshot
-*  Fsck
-*  Editlog/image
-*  Block group support
-*  EC file deletion
-*  Decommission
-*  Corrupted EC blocks
-*  ID collision
- Balancer/Mover
-*  Do not move EC blocks
- Documentation
- Testing

> 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,
> 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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