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From "Zhe Zhang (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HDFS-7285) Erasure Coding Support inside HDFS
Date Tue, 03 Feb 2015 00:51:40 GMT

    [ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14302591#comment-14302591
] 

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

bq. I think it's incorrect. For example, we have a file, and it's length is 128M. If we use
6+3 schema, and ec stripe cell size is 64K, then we need (128*1024K)/(6*64K) = 342 block groups.

Aah I see where the confusion came from. Sorry that the design doc didn't explain clearly
the different parameters. When the client writes to a striped file, the following 3 events
happen:
# Once the client accumulates 6*64KB data, it does _not_ flush the data to the DNs. The client
buffers the data and starts buffering the next 6*64KB stripe.
# Once the client accumulates {{1024 / 64 = 16}} stripes -- that is 1MB for each DN -- it
flushes out the data to DNs.
# Once the data flushed to each DN reaches 128MB -- that is {{128MB * 6 = 768MB}} data overall
-- it allocates a *new block group* from NN.

Section 2.1 of the QFS [paper | http://www.vldb.org/pvldb/vol6/p1092-ovsiannikov.pdf] has
a pretty detailed explanation too. 

> 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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