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An overview of the state of the HBase 1.0 release. Covers a quick HBase overview, the HBase timeline, new features for 1.0, and the upgrade path.
Citation preview
Licensed under a Crea-ve Commons A2ribu-on-‐ShareAlike 3.0 Unported License.
Apache HBase 1.0 Release Nick Dimiduk, Hortonworks @xefyr n10k.com
Licensed under a Crea-ve Commons A2ribu-on-‐ShareAlike 3.0 Unported License.
Release 1.0
“The theme of (eventual) 1.0 release is to become a stable base for future 1.x series of releases. 1.0
release will aim to achieve at least the same level of stability of 0.98 releases without introducing too many
new features.”
Enis Söztutar HBase 1.0 Release Manager
2014-‐11-‐18 2
Licensed under a Crea-ve Commons A2ribu-on-‐ShareAlike 3.0 Unported License.
Agenda
• A Brief History of HBase • What is HBase • Major Changes for 1.0 • Upgrade Path
2014-‐11-‐18 3
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How we got here"A Brief History of HBase
Licensed under a Crea-ve Commons A2ribu-on-‐ShareAlike 3.0 Unported License.
The Early Years
• 2006: BigTable paper published by Google • 2006: HBase development starts • 2007: HBase added Hadoop contrib • 2007: Release Hadoop 0.15.0 • 2008: Hadoop graduates Incubator • 2008: HBase becomes Hadoop sub-project • 2008: Release HBase 0.18.1 • 2009: Release HBase 0.19.0 • 2009: Release HBase 0.20.0
2014-‐11-‐18 5
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Into Production
• 2010: HBase becomes Apache top-level project • 2011: Release HBase 0.90.0 • 2011: Release HBase 0.92.0 • 2011: HBase: The Definitive Guide published • 2012: Release HBase 0.94.0 • 2012: First HBaseCon • 2012: HBase Administration Cookbook published • 2012: HBase In Action published
2014-‐11-‐18 6
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Modern HBase
• 2013: HBaseCon 2013 • 2013: Release HBase 0.96.0 • 2013: Apache Phoenix enters Incubator • 2014: Release HBase 0.98.0 • 2014: HBaseCon 2014 • 2014: Apache Phoenix graduates Incubator • 2014: Release HBase 1.0 • … • 2015: Release HBase 2.0?
2014-‐11-‐18 7
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HBase architecture in 5 minutes or less "What is HBase
Licensed under a Crea-ve Commons A2ribu-on-‐ShareAlike 3.0 Unported License.
Data Model
1368387247 [3.6 kb png data]"thumb"cf2b
a
cf1
1368394583 71368394261 "hello"
"bar"
1368394583 221368394925 13.61368393847 "world"
"foo"
cf21368387684 "almost the loneliest number"1.0001
1368396302 "fourth of July""2011-07-04"
Table A
rowkey columnfamily
columnqualifier timestamp value
Rows
Column Families
2014-‐11-‐18 9
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Logical Architecure
ab
dc
ef
hg
ij
lk
mn
po
Table A
Region 1
Region 2
Region 3
Region 4
Region Server 7Table A, Region 1Table A, Region 2
Table G, Region 1070Table L, Region 25
Region Server 86Table A, Region 3Table C, Region 30Table F, Region 160Table F, Region 776
Region Server 367Table A, Region 4Table C, Region 17Table E, Region 52
Table P, Region 1116
2014-‐11-‐18 10
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Physical Architecture
63HBase in distributed mode
store/access data on HDFS. The master process does the distribution of regions amongRegionServers, and each RegionServer typically hosts multiple regions.
Given that the underlying data is stored in HDFS, which is available to all clients asa single namespace, all RegionServers have access to the same persisted files in the filesystem and can therefore host any region (figure 3.8). By physically collocating Data-Nodes and RegionServers, you can use the data locality property; that is, RegionServ-ers can theoretically read and write to the local DataNode as the primary DataNode.
You may wonder where the TaskTrackers are in this scheme of things. In someHBase deployments, the MapReduce framework isn’t deployed at all if the workload isprimarily random reads and writes. In other deployments, where the MapReduce pro-cessing is also a part of the workloads, TaskTrackers, DataNodes, and HBase Region-Servers can run together.
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-998800005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-443200009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-9988
00005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-4432
00009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
Full table T1
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-9988
00005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-4432
00009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
Table T1 split into 3 regions - R1, R2, and R3
T1R1
T1R2
T1R3
Figure 3.6 A table consists of multiple smaller chunks called regions.
DataNode RegionServer DataNode RegionServer DataNode RegionServer
Figure 3.7 HBase RegionServer and HDFS DataNode processes are typically collocated on the same host.
Licensed to Nick Dimiduk <[email protected]>
63HBase in distributed mode
store/access data on HDFS. The master process does the distribution of regions amongRegionServers, and each RegionServer typically hosts multiple regions.
Given that the underlying data is stored in HDFS, which is available to all clients asa single namespace, all RegionServers have access to the same persisted files in the filesystem and can therefore host any region (figure 3.8). By physically collocating Data-Nodes and RegionServers, you can use the data locality property; that is, RegionServ-ers can theoretically read and write to the local DataNode as the primary DataNode.
You may wonder where the TaskTrackers are in this scheme of things. In someHBase deployments, the MapReduce framework isn’t deployed at all if the workload isprimarily random reads and writes. In other deployments, where the MapReduce pro-cessing is also a part of the workloads, TaskTrackers, DataNodes, and HBase Region-Servers can run together.
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-998800005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-443200009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-9988
00005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-4432
00009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
Full table T1
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-9988
00005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-4432
00009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
Table T1 split into 3 regions - R1, R2, and R3
T1R1
T1R2
T1R3
Figure 3.6 A table consists of multiple smaller chunks called regions.
DataNode RegionServer DataNode RegionServer DataNode RegionServer
Figure 3.7 HBase RegionServer and HDFS DataNode processes are typically collocated on the same host.
Licensed to Nick Dimiduk <[email protected]>
63HBase in distributed mode
store/access data on HDFS. The master process does the distribution of regions amongRegionServers, and each RegionServer typically hosts multiple regions.
Given that the underlying data is stored in HDFS, which is available to all clients asa single namespace, all RegionServers have access to the same persisted files in the filesystem and can therefore host any region (figure 3.8). By physically collocating Data-Nodes and RegionServers, you can use the data locality property; that is, RegionServ-ers can theoretically read and write to the local DataNode as the primary DataNode.
You may wonder where the TaskTrackers are in this scheme of things. In someHBase deployments, the MapReduce framework isn’t deployed at all if the workload isprimarily random reads and writes. In other deployments, where the MapReduce pro-cessing is also a part of the workloads, TaskTrackers, DataNodes, and HBase Region-Servers can run together.
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-998800005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-443200009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-9988
00005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-4432
00009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
Full table T1
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-9988
00005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-4432
00009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
Table T1 split into 3 regions - R1, R2, and R3
T1R1
T1R2
T1R3
Figure 3.6 A table consists of multiple smaller chunks called regions.
DataNode RegionServer DataNode RegionServer DataNode RegionServer
Figure 3.7 HBase RegionServer and HDFS DataNode processes are typically collocated on the same host.
Licensed to Nick Dimiduk <[email protected]>
63HBase in distributed mode
store/access data on HDFS. The master process does the distribution of regions amongRegionServers, and each RegionServer typically hosts multiple regions.
Given that the underlying data is stored in HDFS, which is available to all clients asa single namespace, all RegionServers have access to the same persisted files in the filesystem and can therefore host any region (figure 3.8). By physically collocating Data-Nodes and RegionServers, you can use the data locality property; that is, RegionServ-ers can theoretically read and write to the local DataNode as the primary DataNode.
You may wonder where the TaskTrackers are in this scheme of things. In someHBase deployments, the MapReduce framework isn’t deployed at all if the workload isprimarily random reads and writes. In other deployments, where the MapReduce pro-cessing is also a part of the workloads, TaskTrackers, DataNodes, and HBase Region-Servers can run together.
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-998800005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-443200009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-9988
00005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-4432
00009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
Full table T1
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-9988
00005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-4432
00009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
Table T1 split into 3 regions - R1, R2, and R3
T1R1
T1R2
T1R3
Figure 3.6 A table consists of multiple smaller chunks called regions.
DataNode RegionServer DataNode RegionServer DataNode RegionServer
Figure 3.7 HBase RegionServer and HDFS DataNode processes are typically collocated on the same host.
Licensed to Nick Dimiduk <[email protected]>
RegionServer
DataNode
RegionServer
DataNode
RegionServer
DataNode
RegionServer
DataNode
...
63HBase in distributed mode
store/access data on HDFS. The master process does the distribution of regions amongRegionServers, and each RegionServer typically hosts multiple regions.
Given that the underlying data is stored in HDFS, which is available to all clients asa single namespace, all RegionServers have access to the same persisted files in the filesystem and can therefore host any region (figure 3.8). By physically collocating Data-Nodes and RegionServers, you can use the data locality property; that is, RegionServ-ers can theoretically read and write to the local DataNode as the primary DataNode.
You may wonder where the TaskTrackers are in this scheme of things. In someHBase deployments, the MapReduce framework isn’t deployed at all if the workload isprimarily random reads and writes. In other deployments, where the MapReduce pro-cessing is also a part of the workloads, TaskTrackers, DataNodes, and HBase Region-Servers can run together.
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-998800005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-443200009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-9988
00005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-4432
00009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
Full table T1
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-9988
00005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-4432
00009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
Table T1 split into 3 regions - R1, R2, and R3
T1R1
T1R2
T1R3
Figure 3.6 A table consists of multiple smaller chunks called regions.
DataNode RegionServer DataNode RegionServer DataNode RegionServer
Figure 3.7 HBase RegionServer and HDFS DataNode processes are typically collocated on the same host.
Licensed to Nick Dimiduk <[email protected]>
Master
ZooKeeper
63HBase in distributed mode
store/access data on HDFS. The master process does the distribution of regions amongRegionServers, and each RegionServer typically hosts multiple regions.
Given that the underlying data is stored in HDFS, which is available to all clients asa single namespace, all RegionServers have access to the same persisted files in the filesystem and can therefore host any region (figure 3.8). By physically collocating Data-Nodes and RegionServers, you can use the data locality property; that is, RegionServ-ers can theoretically read and write to the local DataNode as the primary DataNode.
You may wonder where the TaskTrackers are in this scheme of things. In someHBase deployments, the MapReduce framework isn’t deployed at all if the workload isprimarily random reads and writes. In other deployments, where the MapReduce pro-cessing is also a part of the workloads, TaskTrackers, DataNodes, and HBase Region-Servers can run together.
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-998800005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-443200009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-9988
00005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-4432
00009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
Full table T1
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-9988
00005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-4432
00009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
Table T1 split into 3 regions - R1, R2, and R3
T1R1
T1R2
T1R3
Figure 3.6 A table consists of multiple smaller chunks called regions.
DataNode RegionServer DataNode RegionServer DataNode RegionServer
Figure 3.7 HBase RegionServer and HDFS DataNode processes are typically collocated on the same host.
Licensed to Nick Dimiduk <[email protected]>
NameNode
63HBase in distributed mode
store/access data on HDFS. The master process does the distribution of regions amongRegionServers, and each RegionServer typically hosts multiple regions.
Given that the underlying data is stored in HDFS, which is available to all clients asa single namespace, all RegionServers have access to the same persisted files in the filesystem and can therefore host any region (figure 3.8). By physically collocating Data-Nodes and RegionServers, you can use the data locality property; that is, RegionServ-ers can theoretically read and write to the local DataNode as the primary DataNode.
You may wonder where the TaskTrackers are in this scheme of things. In someHBase deployments, the MapReduce framework isn’t deployed at all if the workload isprimarily random reads and writes. In other deployments, where the MapReduce pro-cessing is also a part of the workloads, TaskTrackers, DataNodes, and HBase Region-Servers can run together.
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-998800005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-443200009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-9988
00005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-4432
00009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
Full table T1
00001 John 415-111-123400002 Paul 408-432-992200003 Ron 415-993-212400004 Bob 818-243-9988
00005 Carly 206-221-912300006 Scott 818-231-256600007 Simon 425-112-987700008 Lucas 415-992-4432
00009 Steve 530-288-983200010 Kelly 916-992-123400011 Betty 650-241-119200012 Anne 206-294-1298
Table T1 split into 3 regions - R1, R2, and R3
T1R1
T1R2
T1R3
Figure 3.6 A table consists of multiple smaller chunks called regions.
DataNode RegionServer DataNode RegionServer DataNode RegionServer
Figure 3.7 HBase RegionServer and HDFS DataNode processes are typically collocated on the same host.
Licensed to Nick Dimiduk <[email protected]>
HBaseClient
HDFS
HBase
2014-‐11-‐18 11
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What’s all the excitement about? "Major Changes for 1.0
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Stability: Co-Locate Meta with Master
• Simplify, Improve region assignment reliability – Fewer components involved in updating “truth”
• Master embeds a RegionServer – Will host only system tables – Baby step towards combining RS/Master into a single hbase
daemon • Backup masters unchanged
– Can be configured to host user tables while in standby • Plumbing is all there, OFF by default
http://issues.apache.org/jira/browse/HBASE-10569
2014-‐11-‐18 13
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Availability: Region Replicas
• Multiple RegionServers host a Region – One is “primary”, others are “replicas” – Only primary accepts writes
• Client reads against primary only or any – Results marked as appropriate
• Baby step toward quorum reads, writes • Plumbing is all there, OFF by default
http://issues.apache.org/jira/browse/HBASE-10070 http://www.slideshare.net/HBaseCon/features-session-1
2014-‐11-‐18 14
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Usability: Client API Cleanup
• Improved self-consistency • Simpler semantics • Easier to maintain • Obvious @InterfaceAudience annotations
http://issues.apache.org/jira/browse/HBASE-10602 http://s.apache.org/hbase-1.0-api
https://github.com/ndimiduk/hbase-1.0-api-examples
2014-‐11-‐18 15
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New and Noteworthy
• Greatly expanded hbase.apache.org/book.html • Truncate table shell command • Automatic tuning of global MemStore and
BlockCache sizes • BucketCache easier to configure • Compressed BlockCache • Pluggable replication endpoint • A Dockerfile to easily build and run HBase from
source
2014-‐11-‐18 16
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Under the Covers
• ZooKeeper abstractions • Meta table used for assignment • Cell-based read/write path • Combining mvcc/seqid • Sundry security, tags, labels improvements
2014-‐11-‐18 17
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Groundwork for 2.0
• More, Smaller Regions – Millions, 1G or less – Less write amplification – Splitting hbase:meta
• Performance – More off-heap – Less resource contention – Faster region failover/recovery – Multiple WALs – QoS/Quotas/Multi-tenancy
• Rigging – Faster, more intelligent
assignment – Procedure bus – Resumable, query-able
operations • Other possibilities
– Quorum/consensus reads, writes?
– Hydrabase, multi-DC consensus?
– Streaming RPCs? – High level coprocessor API
2014-‐11-‐18 18
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Semantic Versioning
• Major/Minor/Patch version numbers – Only major/minor pre-1.0
• Dimensions – Client/Server wire compatibility – Server/Server wire and feature compatibility – API compatibility – ABI compatibility – Proposal up for a vote
http://s.apache.org/hbase-semver
2014-‐11-‐18 19
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Tell it to me straight, how bad is it?"Upgrade Path
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Online/Wire Compatibility
• Direct migration from 0.94 supported – Looks a lot like upgrade from 0.94 to 0.96: requires
downtime – Not tested yet, will be before release
• RPC is backward-compatible to 0.96 – Enabled mixing clients and servers across versions – So long as no new features are enabled
• Rolling upgrade "out of the box" from 0.98 • Rolling upgrade "with some massaging" from 0.96
– IE, 0.96 cannot read HFileV3, the new default – not tested yet, will be before release
2014-‐11-‐18 21
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Client Application Compatibility
• API is backward compatible to 0.96 – No code change required – You’ll start getting new deprecation warnings – We recommend you start using new APIs
• ABI is NOT backward compatible – Cannot drop current application jars onto new
runtime – Recompile your application vs. 1.0 jars – Just like 0.96 to 0.98 upgrade
2014-‐11-‐18 22
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Hadoop Versions
• Hadoop 1.x is NOT supported – Bite the bullet; you’ll enjoy the performance benefits
• Hadoop 2.x only – Most thoroughly tested on 2.4.x, 2.5.x – Probably works on 2.2.x, 2.3.x, but less thoroughly
tested
https://hbase.apache.org/book/configuration.html#hadoop
2014-‐11-‐18 23
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Java Versions
• JDK 6 is NOT supported! • JDK 7 is the target runtime • JDK 8 support is experimental
https://hbase.apache.org/book/configuration.html#hadoop
2014-‐11-‐18 24
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Developer Preview 0.99.x
• Pre-release “beta” builds for testing • Not for production
DEVELOPER PREVIEWS NOT FOR PRODUCTION
• Try out the new features • Help us test your upgrade path • Be a part of history in the making! • 0.99.1 available now
http://search-hadoop.com/m/DHED4186dj1
2014-‐11-‐18 25
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Thanks!
M A N N I N G
Nick Dimiduk Amandeep Khurana
FOREWORD BY Michael Stack
hbaseinaction.com
Nick Dimiduk github.com/ndimiduk
@xefyr
n10k.com
http://s.apache.org/hbase-1.0
2014-‐11-‐18 26