[jira] [Commented] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14597792#comment-14597792 ] Vincent.Wei commented on HDFS-7285: --- I am on will out of office for biz trip form 6.23-6.26, I may reply e-mail slowly, please call me 13764370648 when there are urgent mater. 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, HDFS-bistriped.patch, HDFSErasureCodingDesign-20141028.pdf, HDFSErasureCodingDesign-20141217.pdf, HDFSErasureCodingDesign-20150204.pdf, HDFSErasureCodingDesign-20150206.pdf, HDFSErasureCodingPhaseITestPlan.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)
[jira] [Commented] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14576695#comment-14576695 ] Vincent.Wei commented on HDFS-7285: --- Hi all I am a new comer , I want to know if I can add the HDFS-7285-initial-PoC.patch on the hadoop v2.2.0 ? Thanks . 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)
[jira] [Commented] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14323141#comment-14323141 ] Vincent.Wei commented on HDFS-7285: --- I am will out of office for CN New Year from 2.15-2.26 , I may reply e-mail slowly, please call me 13764370648 when there are urgent mater. 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, 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)
[jira] [Commented] (HDFS-503) Implement erasure coding as a layer on HDFS
[ https://issues.apache.org/jira/browse/HDFS-503?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14224107#comment-14224107 ] Vincent.Wei commented on HDFS-503: -- Is anybody know how to build this patch on Hadoop v2.2.0 ? Implement erasure coding as a layer on HDFS --- Key: HDFS-503 URL: https://issues.apache.org/jira/browse/HDFS-503 Project: Hadoop HDFS Issue Type: New Feature Components: contrib/raid Reporter: dhruba borthakur Assignee: dhruba borthakur Fix For: 0.21.0 Attachments: raid1.txt, raid2.txt The goal of this JIRA is to discuss how the cost of raw storage for a HDFS file system can be reduced. Keeping three copies of the same data is very costly, especially when the size of storage is huge. One idea is to reduce the replication factor and do erasure coding of a set of blocks so that the over probability of failure of a block remains the same as before. Many forms of error-correcting codes are available, see http://en.wikipedia.org/wiki/Erasure_code. Also, recent research from CMU has described DiskReduce https://opencirrus.org/system/files/Gibson-OpenCirrus-June9-09.ppt. My opinion is to discuss implementation strategies that are not part of base HDFS, but is a layer on top of HDFS. -- This message was sent by Atlassian JIRA (v6.3.4#6332)
