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From "Zhe Zhang (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HDFS-7285) Erasure Coding Support inside HDFS
Date Thu, 19 Mar 2015 23:12:45 GMT

    [ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14370307#comment-14370307

Zhe Zhang commented on HDFS-7285:

I had another offline discussion with [~andrew.wang] around the storagePolicy vs. zone topic.
We agreed that it's a difficult decision because it requires prediction of production usage
patterns. The desired EC setup might not always align with directories. E.g., it is possible
for a directory to contain both big files (suitable for striping) and small ones (will cause
heavy NN overhead under striping). In this case, we can keep the directory policy to be non-EC,
so only big files need to carry the EC policy in their XAttr -- it is a small NN overhead
since only a small fraction of files are big. As a follow on optimization we can even setup
a size-based policy for automatic conversion. I'll look at a few applications like HBase /
Hive to get a better understanding.

I think we can follow an incremental development plan:
# We can start with a simple zone-like policy as Jing [proposed | https://issues.apache.org/jira/browse/HDFS-7285?focusedCommentId=14366293&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14366293]
above. In this step we don't even need to fully implement the enforcement of zone constraints
(empty directory, no nesting etc.).
# After collecting potential usage patterns (in terms of directory structure), we'll decide
whether the use case of per-file and nested EC configuration is important enough. Based on
that, we'll either fully implement zone constraints or implement fine grained EC policies.
# We'll finally decide whether and how to integrate with other storage policies


> 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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