[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Tsz-wo Sze updated HDFS-7285: - Attachment: 1619363340018.png > 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 >Priority: Major > Fix For: 3.0.0-alpha1 > > Attachments: 1619363340018.png, Compare-consolidated-20150824.diff, > Consolidated-20150707.patch, Consolidated-20150806.patch, > Consolidated-20150810.patch, ECAnalyzer.py, ECParser.py, > HDFS-7285-Consolidated-20150911.patch, HDFS-7285-initial-PoC.patch, > HDFS-7285-merge-consolidated-01.patch, > HDFS-7285-merge-consolidated-trunk-01.patch, > HDFS-7285-merge-consolidated.trunk.03.patch, > HDFS-7285-merge-consolidated.trunk.04.patch, > HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.patch, > HDFS-bistriped.patch, HDFSErasureCodingDesign-20141028.pdf, > HDFSErasureCodingDesign-20141217.pdf, HDFSErasureCodingDesign-20150204.pdf, > HDFSErasureCodingDesign-20150206.pdf, HDFSErasureCodingPhaseITestPlan.pdf, > HDFSErasureCodingSystemTestPlan-20150824.pdf, > HDFSErasureCodingSystemTestReport-20150826.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 (v8.3.4#803005) - To unsubscribe, e-mail: hdfs-issues-unsubscr...@hadoop.apache.org For additional commands, e-mail: hdfs-issues-h...@hadoop.apache.org
[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[
https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Andrew Wang updated HDFS-7285:
--
Release Note:
HDFS now provides native support for erasure coding (EC) to store data more
efficiently. Each individual directory can be configured with an EC policy with
command `hdfs erasurecode -setPolicy`. When a file is created, it will inherit
the EC policy from its nearest ancestor directory to determine how its blocks
are stored. Compared to 3-way replication, the default EC policy saves 50% of
storage space while also tolerating more storage failures.
To support small files, the currently phase of HDFS-EC stores blocks in
_striped_ layout, where a logical file block is divided into small units (64KB
by default) and distributed to a set of DataNodes. This enables parallel I/O
but also decreases data locality. Therefore, the cluster environment and I/O
workloads should be considered before configuring EC policies.
was:
HDFS now provides native support for erasure coding (EC) to store data more
efficiently. Each individual directory can be configured with an EC policy with
command {{hdfs erasurecode -setPolicy}}. When a file is created, it will
inherit the EC policy from its nearest ancestor to determine how its blocks are
stored. Compared with 3-way replication, the default EC policy saves 50% of
storage space for configured directories, while tolerating more storage
failures.
To support small files, the currently phase of HDFS-EC stores blocks in
_striped_ layout, where a logical file block is divided into small units (64KB
by default) and distributed to a set of {{DataNodes}}. This enables parallel
I/O but also decreases data locality. Therefore, the cluster environment and
I/O workloads should be considered before configuring EC 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
> Fix For: 3.0.0
>
> Attachments: Compare-consolidated-20150824.diff,
> Consolidated-20150707.patch, Consolidated-20150806.patch,
> Consolidated-20150810.patch, ECAnalyzer.py, ECParser.py,
> HDFS-7285-Consolidated-20150911.patch, HDFS-7285-initial-PoC.patch,
> HDFS-7285-merge-consolidated-01.patch,
> HDFS-7285-merge-consolidated-trunk-01.patch,
> HDFS-7285-merge-consolidated.trunk.03.patch,
> HDFS-7285-merge-consolidated.trunk.04.patch,
> HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.patch,
> HDFS-bistriped.patch, HDFSErasureCodingDesign-20141028.pdf,
> HDFSErasureCodingDesign-20141217.pdf, HDFSErasureCodingDesign-20150204.pdf,
> HDFSErasureCodingDesign-20150206.pdf, HDFSErasureCodingPhaseITestPlan.pdf,
> HDFSErasureCodingSystemTestPlan-20150824.pdf,
> HDFSErasureCodingSystemTestReport-20150826.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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[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[
https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Zhe Zhang updated HDFS-7285:
Release Note:
HDFS now provides native support for erasure coding (EC) to store data more
efficiently. Each individual directory can be configured with an EC policy with
command {{hdfs erasurecode -setPolicy}}. When a file is created, it will
inherit the EC policy from its nearest ancestor to determine how its blocks are
stored. Compared with 3-way replication, the default EC policy saves 50% of
storage space for configured directories, while tolerating more storage
failures.
To support small files, the currently phase of HDFS-EC stores blocks in
_striped_ layout, where a logical file block is divided into small units (64KB
by default) and distributed to a set of {{DataNodes}}. This enables parallel
I/O but also decreases data locality. Therefore, the cluster environment and
I/O workloads should be considered before configuring EC 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
> Fix For: 3.0.0
>
> Attachments: Compare-consolidated-20150824.diff,
> Consolidated-20150707.patch, Consolidated-20150806.patch,
> Consolidated-20150810.patch, ECAnalyzer.py, ECParser.py,
> HDFS-7285-Consolidated-20150911.patch, HDFS-7285-initial-PoC.patch,
> HDFS-7285-merge-consolidated-01.patch,
> HDFS-7285-merge-consolidated-trunk-01.patch,
> HDFS-7285-merge-consolidated.trunk.03.patch,
> HDFS-7285-merge-consolidated.trunk.04.patch,
> HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.patch,
> HDFS-bistriped.patch, HDFSErasureCodingDesign-20141028.pdf,
> HDFSErasureCodingDesign-20141217.pdf, HDFSErasureCodingDesign-20150204.pdf,
> HDFSErasureCodingDesign-20150206.pdf, HDFSErasureCodingPhaseITestPlan.pdf,
> HDFSErasureCodingSystemTestPlan-20150824.pdf,
> HDFSErasureCodingSystemTestReport-20150826.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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(v6.3.4#6332)
[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Yi Liu updated HDFS-7285: - Assignee: Zhe Zhang (was: Yi Liu) > 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: Compare-consolidated-20150824.diff, > Consolidated-20150707.patch, Consolidated-20150806.patch, > Consolidated-20150810.patch, ECAnalyzer.py, ECParser.py, > HDFS-7285-Consolidated-20150911.patch, HDFS-7285-initial-PoC.patch, > HDFS-7285-merge-consolidated-01.patch, > HDFS-7285-merge-consolidated-trunk-01.patch, > HDFS-7285-merge-consolidated.trunk.03.patch, > HDFS-7285-merge-consolidated.trunk.04.patch, > HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.patch, > HDFS-bistriped.patch, HDFSErasureCodingDesign-20141028.pdf, > HDFSErasureCodingDesign-20141217.pdf, HDFSErasureCodingDesign-20150204.pdf, > HDFSErasureCodingDesign-20150206.pdf, HDFSErasureCodingPhaseITestPlan.pdf, > HDFSErasureCodingSystemTestPlan-20150824.pdf, > HDFSErasureCodingSystemTestReport-20150826.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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[
https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Zhe Zhang updated HDFS-7285:
Attachment: HDFS-7285-Consolidated-20150911.patch
With HDFS-8833 committed, all remaining subtasks are about write pipeline error
handling (with very little change to existing trunk code) and minor
improvements. I suggest we start reviewing the feature branch in the context of
merging to trunk.
I'm attaching the current consolidated patch
({{HDFS-7285-Consolidated-20150911.patch}}). Feedbacks are very welcome!
> 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: Compare-consolidated-20150824.diff,
> Consolidated-20150707.patch, Consolidated-20150806.patch,
> Consolidated-20150810.patch, ECAnalyzer.py, ECParser.py,
> HDFS-7285-Consolidated-20150911.patch, HDFS-7285-initial-PoC.patch,
> HDFS-7285-merge-consolidated-01.patch,
> HDFS-7285-merge-consolidated-trunk-01.patch,
> HDFS-7285-merge-consolidated.trunk.03.patch,
> HDFS-7285-merge-consolidated.trunk.04.patch,
> HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.patch,
> HDFS-bistriped.patch, HDFSErasureCodingDesign-20141028.pdf,
> HDFSErasureCodingDesign-20141217.pdf, HDFSErasureCodingDesign-20150204.pdf,
> HDFSErasureCodingDesign-20150206.pdf, HDFSErasureCodingPhaseITestPlan.pdf,
> HDFSErasureCodingSystemTestPlan-20150824.pdf,
> HDFSErasureCodingSystemTestReport-20150826.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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[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] GAO Rui updated HDFS-7285: -- Attachment: HDFSErasureCodingSystemTestReport-20150826.pdf Based on latest version of branch HDFS-7285, we implemented system test according to the test plan [^HDFSErasureCodingSystemTestPlan-20150824.pdf]. We failed to test some scenarios in EC file writing/reading test case because of problems which is not related to HDFS, but ssh issues of the test script. We will figure out the problem, and implement remaining test scenarios ASAP. Thanks [~jingzhao] and [~szetszwo]] for help [~tfukudom] and our team during the whole process of test planning and implementation. 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: Compare-consolidated-20150824.diff, Consolidated-20150707.patch, Consolidated-20150806.patch, Consolidated-20150810.patch, ECAnalyzer.py, ECParser.py, HDFS-7285-initial-PoC.patch, HDFS-7285-merge-consolidated-01.patch, HDFS-7285-merge-consolidated-trunk-01.patch, HDFS-7285-merge-consolidated.trunk.03.patch, HDFS-7285-merge-consolidated.trunk.04.patch, HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.patch, HDFS-bistriped.patch, HDFSErasureCodingDesign-20141028.pdf, HDFSErasureCodingDesign-20141217.pdf, HDFSErasureCodingDesign-20150204.pdf, HDFSErasureCodingDesign-20150206.pdf, HDFSErasureCodingPhaseITestPlan.pdf, HDFSErasureCodingSystemTestPlan-20150824.pdf, HDFSErasureCodingSystemTestReport-20150826.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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Zhe Zhang updated HDFS-7285: Status: Open (was: Patch Available) 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: Consolidated-20150707.patch, Consolidated-20150806.patch, Consolidated-20150810.patch, ECAnalyzer.py, ECParser.py, HDFS-7285-initial-PoC.patch, HDFS-7285-merge-consolidated-01.patch, HDFS-7285-merge-consolidated-trunk-01.patch, HDFS-7285-merge-consolidated.trunk.03.patch, HDFS-7285-merge-consolidated.trunk.04.patch, HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.patch, HDFS-bistriped.patch, HDFSErasureCodingDesign-20141028.pdf, HDFSErasureCodingDesign-20141217.pdf, HDFSErasureCodingDesign-20150204.pdf, HDFSErasureCodingDesign-20150206.pdf, HDFSErasureCodingPhaseITestPlan.pdf, HDFSErasureCodingSystemTestPlan-20150824.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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[
https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Zhe Zhang updated HDFS-7285:
Attachment: Compare-consolidated-20150824.diff
I have rebased the {{HDFS-7285-REBASE}} branch that [~vinayrpet] created on top
of HDFS-6407, the same starting point as {{HDFS-7285-merge}}. I then compared
the HEAD of these 2 branches. As can be seen from the attached
{{Compare-consolidated-20150824.diff}} file, most differences are cosmetic.
I've incorporated necessary changes and updated the {{HDFS-7285-merge}} branch
and started a Jenkins [job |
https://builds.apache.org/job/Hadoop-HDFS-7285-Merge/102/] for verification.
From the past several Jenkins runs, {{HDFS-7285-merge}} is not introducing new
failures compared to {{HDFS-7285}}.
Once verified by Jenkins I'll push {{HDFS-7285-merge}} as the main
{{HDFS-7285}} branch and move the current {{HDFS-7285-merge}} branch as a
back-up.
Please let me know if you see any issues with the plan. 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: Compare-consolidated-20150824.diff,
Consolidated-20150707.patch, Consolidated-20150806.patch,
Consolidated-20150810.patch, ECAnalyzer.py, ECParser.py,
HDFS-7285-initial-PoC.patch, HDFS-7285-merge-consolidated-01.patch,
HDFS-7285-merge-consolidated-trunk-01.patch,
HDFS-7285-merge-consolidated.trunk.03.patch,
HDFS-7285-merge-consolidated.trunk.04.patch,
HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.patch,
HDFS-bistriped.patch, HDFSErasureCodingDesign-20141028.pdf,
HDFSErasureCodingDesign-20141217.pdf, HDFSErasureCodingDesign-20150204.pdf,
HDFSErasureCodingDesign-20150206.pdf, HDFSErasureCodingPhaseITestPlan.pdf,
HDFSErasureCodingSystemTestPlan-20150824.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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[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] GAO Rui updated HDFS-7285: -- Attachment: HDFSErasureCodingSystemTestPlan-20150824.pdf Inspired by [^HDFSErasureCodingPhaseITestPlan.pdf] created by [~zhz]. We has implemented system tests(Functional Tests) according to this test plan [^HDFSErasureCodingSystemTestPlan-20150824.pdf]. Latest test report will be posted separately soon.Please feel free to discuss and add more test cases or scenarios to this system test plan. 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: Consolidated-20150707.patch, Consolidated-20150806.patch, Consolidated-20150810.patch, ECAnalyzer.py, ECParser.py, HDFS-7285-initial-PoC.patch, HDFS-7285-merge-consolidated-01.patch, HDFS-7285-merge-consolidated-trunk-01.patch, HDFS-7285-merge-consolidated.trunk.03.patch, HDFS-7285-merge-consolidated.trunk.04.patch, HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.patch, HDFS-bistriped.patch, HDFSErasureCodingDesign-20141028.pdf, HDFSErasureCodingDesign-20141217.pdf, HDFSErasureCodingDesign-20150204.pdf, HDFSErasureCodingDesign-20150206.pdf, HDFSErasureCodingPhaseITestPlan.pdf, HDFSErasureCodingSystemTestPlan-20150824.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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Yi Liu updated HDFS-7285: - Assignee: Zhe Zhang (was: qi jun wang) 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: Consolidated-20150707.patch, Consolidated-20150806.patch, Consolidated-20150810.patch, ECAnalyzer.py, ECParser.py, HDFS-7285-initial-PoC.patch, HDFS-7285-merge-consolidated-01.patch, HDFS-7285-merge-consolidated-trunk-01.patch, HDFS-7285-merge-consolidated.trunk.03.patch, HDFS-7285-merge-consolidated.trunk.04.patch, HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Zhe Zhang updated HDFS-7285: Attachment: Consolidated-20150810.patch Updating the patch to fix unit test failures. Now the rebased patch doesn't cause any additional failures compared with the current HDFS-7285 branch. 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: Consolidated-20150707.patch, Consolidated-20150806.patch, Consolidated-20150810.patch, ECAnalyzer.py, ECParser.py, HDFS-7285-initial-PoC.patch, HDFS-7285-merge-consolidated-01.patch, HDFS-7285-merge-consolidated-trunk-01.patch, HDFS-7285-merge-consolidated.trunk.03.patch, HDFS-7285-merge-consolidated.trunk.04.patch, HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[
https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Zhe Zhang updated HDFS-7285:
Attachment: Consolidated-20150806.patch
We have revisited the {{BlockInfo}} structure and reverted HDFS-8499. I just
rebased the consolidated patch.
I've also pushed the patch to my personal github repo and manually started a
Jenkins [job | https://builds.apache.org/job/Hadoop-HDFS-7285-Merge/].
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: Consolidated-20150707.patch,
Consolidated-20150806.patch, ECAnalyzer.py, ECParser.py,
HDFS-7285-initial-PoC.patch, HDFS-7285-merge-consolidated-01.patch,
HDFS-7285-merge-consolidated-trunk-01.patch,
HDFS-7285-merge-consolidated.trunk.03.patch,
HDFS-7285-merge-consolidated.trunk.04.patch,
HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.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.
--
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[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[
https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Zhe Zhang updated HDFS-7285:
Attachment: Consolidated-20150707.patch
I finished a complete pass of the patch (both {{main}} and {{test}} codes) and
pushed to my github [repo |
https://github.com/zhe-thoughts/hadoop/tree/HDFS-EC-Merge]. Also uploading the
consolidated patch (renaming it again and hoping Jenkins applies it against
trunk instead of our branch).
I made some minor adjustments to the list of sub patches:
{code}
1. HADOOP-COMMON side support for codec calculations
2. Support Erasure Coding Zones.
3. Extend BlockInfo to handle striped block groups.
4. Allocate and manage striped blocks in NameNode blockmanagement module.
5. BlockPlacementPolicies for erasure coding
6. Create LocatedStripedBlock abstraction to represent striped block groups.
7. Distribute recovery work for striped blocks to DataNode.
8. Add striped block support in INodeFile.
9. Client side support
10. Datanode support
11. Change fsck to support EC files
12. Support striped block groups in fsimage and edit logs
13. Balancer and mover support for striped block groups
14. Additional unit tests for erasure coding
{code}
I also cherry-picked the 2 new patches from HDFS-7285 branch.
I created separate Jenkins builds for sub patches 1~4. Most tests passed. The
remaining handful of failures seem to be resulted from the patch splitting
(they pass in the final consolidated patch).
So *sub patches 1~4* are ready for reviews. Any comments and suggestions are
much appreciated. In particular, sub patch 3 has the new implementation of
{{BlockInfoStriped}} which has not been reviewed in the branch.
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: Consolidated-20150707.patch, ECAnalyzer.py, ECParser.py,
HDFS-7285-initial-PoC.patch, HDFS-7285-merge-consolidated-01.patch,
HDFS-7285-merge-consolidated-trunk-01.patch,
HDFS-7285-merge-consolidated.trunk.03.patch,
HDFS-7285-merge-consolidated.trunk.04.patch,
HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.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.
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Auto-Re: [jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
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[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] hewenyu updated HDFS-7285: -- Assignee: Zhe Zhang (was: hewenyu) 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-7285-merge-consolidated-01.patch, HDFS-7285-merge-consolidated-trunk-01.patch, HDFS-7285-merge-consolidated.trunk.03.patch, HDFS-7285-merge-consolidated.trunk.04.patch, HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Vinayakumar B updated HDFS-7285: Attachment: HDFS-7285-merge-consolidated.trunk.04.patch 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-7285-merge-consolidated-01.patch, HDFS-7285-merge-consolidated-trunk-01.patch, HDFS-7285-merge-consolidated.trunk.03.patch, HDFS-7285-merge-consolidated.trunk.04.patch, HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Vinayakumar B updated HDFS-7285: Attachment: HDFS-7285-merge-consolidated.trunk.03.patch Rebased patch. 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-7285-merge-consolidated-01.patch, HDFS-7285-merge-consolidated-trunk-01.patch, HDFS-7285-merge-consolidated.trunk.03.patch, HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Vinayakumar B updated HDFS-7285: Attachment: HDFS-7285-merge-consolidated-trunk-01.patch Attaching the same patch, with 'trunk' in the name. 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-7285-merge-consolidated-01.patch, HDFS-7285-merge-consolidated-trunk-01.patch, HDFS-EC-Merge-PoC-20150624.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)
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[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Vinayakumar B updated HDFS-7285: Target Version/s: 3.0.0 (was: HDFS-7285) Status: Patch Available (was: Open) 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-7285-merge-consolidated-01.patch, HDFS-EC-Merge-PoC-20150624.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)
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[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Vinayakumar B updated HDFS-7285: Attachment: HDFS-7285-merge-consolidated-01.patch Attaching the consolidated-merge-patch. 1. Merged the latest trunk code to HDFS-7285 branch. 2. Taken diff against trunk. Includes all changes done till date in the branch. Thanks to [~zhz]. Most complicated BlockInfo heirarchy conflicts were resolved using the POC patch posted earlier. Patch is pretty big. But also Intended to run jenkins. 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-7285-merge-consolidated-01.patch, HDFS-EC-Merge-PoC-20150624.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)
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[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Zhe Zhang updated HDFS-7285: Attachment: HDFS-EC-merge-consolidated-01.patch Updating the same patch Vinay generated with a different name. Hope Jenkins tries it with trunk this time. 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-7285-merge-consolidated-01.patch, HDFS-7285-merge-consolidated-trunk-01.patch, HDFS-EC-Merge-PoC-20150624.patch, HDFS-EC-merge-consolidated-01.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)
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[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[
https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Zhe Zhang updated HDFS-7285:
Attachment: HDFS-EC-Merge-PoC-20150624.patch
I just finished a pass rebasing all non-test changes. Attached is the
consolidated PoC patch. The aforementioned github [repo |
https://github.com/zhe-thoughts/hadoop/tree/HDFS-EC-Merge] also has the same
changes. We have roughly:
# 5,600 LoC in {{hadoop-common}}, which is entirely new code
# 2,000 LoC in {{blockmanagement}}. This is mainly to support block-level
management of striping. Aside from a few new classes, most changes are in
{{BlockManager}}
# 1,600 LoC in {{namenode}}. This is mainly to add striped blocks support in
{{INodeFile}}, and fsimage/editlog. Biggest changes are on {{INodeFile}},
{{FSNameSystem}}, and {{FSDirWriteFileOp}}
# 1,100 LoC in {{datanode}}, 2,500 in client, and 1,000 in util. This is mainly
new code.
Next I plan to make another pass and divide the consolidated patch into
functional pieces (while doing that, add associated tests to each piece).
Meanwhile, any comments / questions / suggestions on the patch are very
welcome.
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-EC-Merge-PoC-20150624.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.
--
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[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Zhe Zhang updated HDFS-7285: Attachment: (was: HDFS-EC-Merge-PoC-20150624.patch) 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-EC-Merge-PoC-20150624.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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Zhe Zhang updated HDFS-7285: Attachment: HDFS-EC-Merge-PoC-20150624.patch 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-EC-Merge-PoC-20150624.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)
Auto-Re: [jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
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Auto-Re: [jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
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[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[
https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Zhe Zhang updated HDFS-7285:
Attachment: HDFS-bistriped.patch
Proposed structure for {{BlockInfoStriped}} and {{BlockInfoUCStriped}}. It's a
little outdated. Please refer to my github branch for the latest proposed code.
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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Zhe Zhang updated HDFS-7285: Attachment: HDFSErasureCodingPhaseITestPlan.pdf Uploading a test plan for phase I of the feature (thanks [~drankye] for filling in the details on codec testing). Any questions and comments are very welcome. When we move on to follow-on optimizations (HDFS-8031) and phase II of the erasure coding feature (HDFS-8030) I will post additional test plans. 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, 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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Arpit Agarwal updated HDFS-7285: Assignee: Zhe Zhang (was: Arpit Agarwal) 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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Zhe Zhang updated HDFS-7285: Attachment: HDFS-7285-initial-PoC.patch This is the patch from trunk that was used in the PoC test. It demonstrates the changes we have made to support basic I/O in striping layout. 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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Tsz Wo Nicholas Sze updated HDFS-7285: -- Attachment: HDFSErasureCodingDesign-20150206.pdf ... I see you are still editing so will hold on to my updates for now. Yes, just found some note which not yet added to the doc. HDFSErasureCodingDesign-20150206.pdf: - adds new sections for -* Datanode Decommission, and -* Appendix: 3-replication vs (6,3)-Reed-Solomon. 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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Tsz Wo Nicholas Sze updated HDFS-7285: -- Attachment: HDFSErasureCodingDesign-20150204.pdf Revised the design doc as follows: - Revised the sections for -* Saving I/O bandwidth -* BlockGroup, -* ErasureCodec, -* ECClient, and -* NameNode Memory Usage Reduction. - Added new sections for -* EC Writer, -* Handling Datanode Failure during Write, -* Reading a Closed File, -* Reading a Being Written File, -* Hflush, -* Hsync, -* Append, -* Truncate, -* BlockGroup States, -* Generation Stamp, -* BlockGroup Recovery, -* EC Block Reconstruction, -* Collision with Random Block ID. 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, 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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Zhe Zhang updated HDFS-7285: Attachment: ECParser.py ECAnalyzer.py Attaching the Python scripts used to generate the fsimage analysis report. 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Yi Liu updated HDFS-7285: - Assignee: Zhe Zhang (was: Fei Hu) 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: 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Zhe Zhang updated HDFS-7285: Attachment: fsimage-analysis-20150105.pdf To motivate and guide the design (especially data striping), we have analyzed several production clusters and generated what-if scenarios with different policies. Please refer to the attached report for full details. 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: 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[
https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Zhe Zhang updated HDFS-7285:
Attachment: HDFSErasureCodingDesign-20141217.pdf
Based on feedbacks from the meetup and a deeper study of file size distribution
(detailed report to be posted later), *Data Striping* is added to this updated
design, mainly to support EC on small files. A few highlights compared to the
first version:
# _Client_: extended with striping and codec logic
# _NameNode_: {{INodeFile}} extended to store both block and {{BlockGroup}}
information; optimizations are proposed to reduce memory usage caused by
striping and parity data
# _DataNode_ remains mostly unchanged from the original EC design
# Prioritizing _EC with striping_ as the focus of the initial phase, and
putting _EC with contiguous (non-striping) layout_ to a 2nd phase
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: HDFSErasureCodingDesign-20141028.pdf,
HDFSErasureCodingDesign-20141217.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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Kai Zheng updated HDFS-7285: Fix Version/s: HDFS-EC 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 Fix For: HDFS-EC Attachments: HDFSErasureCodingDesign-20141028.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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Yi Liu updated HDFS-7285: - Target Version/s: HDFS-EC Fix Version/s: (was: HDFS-EC) 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: HDFSErasureCodingDesign-20141028.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] [Updated] (HDFS-7285) Erasure Coding Support inside HDFS
[ https://issues.apache.org/jira/browse/HDFS-7285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Zhe Zhang updated HDFS-7285: Attachment: HDFSErasureCodingDesign-20141028.pdf Thanks [~whjiang] for reporting this JIRA. I'm attaching the first draft of the design doc and will be more than happy to incorporate feedbacks under this JIRA. I'd also like to invite the community to a meeting at Cloudera headquarter (1001 Page Mill Road, Palo Alto) to discuss the design in more detail. My tentative plan is to hold it in the morning of the coming Friday (Oct. 31st). Please let me know if you are interested and if so, your availability. After soliciting feedbacks I will confirm logistic details, including the remote meeting URL. 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: HDFSErasureCodingDesign-20141028.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)
