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Building Online HBase Cluster

of 

Zhihu Based on Kubernetes
HBase at Zhihu
Agenda
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Using Kubernetes HBase Online Platform
HBase Online Platform
Using Kubernetes
HBase at Zhihu
• Offline
• Physical machine, hundreds of nodes.
• Work with Spark/Hadoop.
• Online
• Based on Kubernetes, more than 300 containers.
HBase at Zhihu
01
02
Our online storage
01
02
03
MySQL
used in most business
some need scale, some need transform
all SSD,expensive
Redis
cache and partial storage
no shard
expensive
HBase / Cassandra / RocksDB etc. ?
Challenges at the beginning
• All business at one big cluster
• Also runs NodeManager and ImpalaServer
• Basically operation
• Physical node level monitor
What we want
• From Business Sight
• environment isolation
• SLA definition
• business level monition
• From Operation Sight
• balance resource ( CPU, I/O, RAM )
• friendly api
• controllable costs
01
02
Make HBase as a Service.
In short:
HBase Online Platform
Using Kubernetes
HBase at Zhihu
Zhihu’s Unified Cluster Manage Platfom
HBase online cluster
• Platform controls cluster
• Kubernetes schedule resources
• Shared HDFS and ZK
• Expose ZK address or ThriftServer to user
Kubernetes
Cluster resource manager and scheduler
Using container to isolate resource
Application management
Perfect API and active community
01
02
03
04
Component Design
• Pod
• infrastructure component
• one Pod per component
• ReplicationController -> HA
• Define A cluster
• 1 HMaster RC ( replica = 2 )
• 1 RegionServer RC ( replica = n, n >=1 )
• 1 ThriftServer RC ( replica = m, m>=0 )
Failover Design
Data Replication
Component Level
Cluster Level
• HMaster -> use ZooKeeper
• RegionServer -> Stateless designed
• ThriftServer -> use proxy
• HFile -> ???
Component Level
Component Level - HFile
• Shared HDFS Cluster
• Keep the whole cluster stateless
Cluster Level
• What if cluster Pod is down ?
• Kubernetes ReplicationController
• What if Kubernetes is down ?
• Mixed deployment
• Few physical nodes with high CPU && RAM
Data Replication
• Replication in cluster
• HDFS built in ( 3 replicas)
• period hdfs fsck
• Replication between clusters
• snapshot + bulk load
• offline cluster doing MR / Spark
01
02
HBase Online Platform
Using Kubernetes
HBase at Zhihu
Physical Node Resource
CPU: 2 * 12 core
Disk: 4 T
Memory: 128 G
Resource Definition (1)
• Minimize the resource
• Business scaled by number of containers
• Pros
• maximum resource usage on nodes
• simplified debug
• ease scale
• Cons
• minimum resource not easy to define by business
• hardly tune params for RAMs and GC
Resource Definition (2)
• Customize container resource by business
• Business scaled by number of containers
• Pros
• flexible RAM config and tuning
• used in production
Container Configuration
• JAVA_HOME HBASE_HOME
• inject to container via ENV
• hdfs-site.xml core-site.xml
• add xml config to container
• hbase-site.xml hbase-env.sh
• use start-env.sh to init configuration
• Modify params during cluster running is permitted
RegionServer Configuration
• Basie Config
• hbase.hregion.majorcompaction = 0
• hbase.regionserver.handler.count = 50
• hbase.regionserver.codecs = snappy
• hfile.block.cache.size = 0.4
• Using G1GC ( thanks to Xiaomi )
Network
• Dedicated ip per pod
• DNS register/deregister automatically
• Modified /etc/hosts for pod
Client Design
• For Java/Scala
• native HBase client
• only offer ZK address to business
• For Python
• happybase
• client proxy
• service discovery
API Server
• A Bridge between Kubernetes and user
• Encapsulate component of a HBase cluster
• Restful API
• Friendly interface
Painful Points
• Cons:
• fully scan still impact whole cluster
• speed limited coprocessor
• locality && short circuit
• SSD Disk
Monitor Cluster
• Physical nodes Level
• nodes cpu loads && usage ( via IT )
• Cluster Level
• Pods cpu loads ( via cAdvisor)
• read && write rate , P95, cacheHit ( via JMX)
• Table Level
• client write speed && read latency ( via tracing )
• thrift server ( via JMX )
Current Situation
• 10+ online business, 300+ Pods
• P95 average 20-30 ms
• 99.99% SLA in 9 months
Benefits
Easy
Isolate
Flexible
• Almost no code needed
• HBase container publish independently
• Deployment and orchestration straight forward
• Decoupled from physical nodes
Easy
• Resource isolation
• CPU
• memory
• Business isolation
• data
• proxy
• monitor
Isolate
• Multi version
• mostly cdh5.5.0-hbase1.0.0
• one upgrade to 1.2 (HBASE-14283)
• customize version easily
• Configuration motivated by business
• low latency -> read replica
• etc.
Flexible
• Enhance performance
• Netty on ThriftServer
• Python HBase Client
• SSD for Datanode
• Auto scale
• by RegionServer number
• by JVM heap
• etc.
Next
Thanks!

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hbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes

  • 1. Building Online HBase Cluster
 of 
 Zhihu Based on Kubernetes
  • 2. HBase at Zhihu Agenda PPT模板:www.1ppt.com/moban/ PPT素材:www.1ppt.com/sucai/ PPT背景:www.1ppt.com/beijing/ PPT图表:www.1ppt.com/tubiao/ PPT下载:www.1ppt.com/xiazai/ PPT教程: www.1ppt.com/powerpoint/ 资料下载:www.1ppt.com/ziliao/ 范⽂下载:www.1ppt.com/fanwen/ 试卷下载:www.1ppt.com/shiti/ 教案下载:www.1ppt.com/jiaoan/ PPT论坛:www.1ppt.cn PPT课件:www.1ppt.com/kejian/ 语⽂课件:www.1ppt.com/kejian/yuwen/ 数学课件:www.1ppt.com/kejian/shuxue/ 英语课件:www.1ppt.com/kejian/yingyu/ 美术课件:www.1ppt.com/kejian/meishu/ 科学课件:www.1ppt.com/kejian/kexue/ 物理课件:www.1ppt.com/kejian/wuli/ 化学课件:www.1ppt.com/kejian/huaxue/ ⽣物课件:www.1ppt.com/kejian/shengwu/ 地理课件:www.1ppt.com/kejian/dili/ 历史课件:www.1ppt.com/kejian/lishi/ Using Kubernetes HBase Online Platform
  • 3. HBase Online Platform Using Kubernetes HBase at Zhihu
  • 4. • Offline • Physical machine, hundreds of nodes. • Work with Spark/Hadoop. • Online • Based on Kubernetes, more than 300 containers. HBase at Zhihu 01 02
  • 5. Our online storage 01 02 03 MySQL used in most business some need scale, some need transform all SSD,expensive Redis cache and partial storage no shard expensive HBase / Cassandra / RocksDB etc. ?
  • 6. Challenges at the beginning • All business at one big cluster • Also runs NodeManager and ImpalaServer • Basically operation • Physical node level monitor
  • 7. What we want • From Business Sight • environment isolation • SLA definition • business level monition • From Operation Sight • balance resource ( CPU, I/O, RAM ) • friendly api • controllable costs 01 02
  • 8. Make HBase as a Service. In short:
  • 9. HBase Online Platform Using Kubernetes HBase at Zhihu
  • 10. Zhihu’s Unified Cluster Manage Platfom
  • 11. HBase online cluster • Platform controls cluster • Kubernetes schedule resources • Shared HDFS and ZK • Expose ZK address or ThriftServer to user
  • 12. Kubernetes Cluster resource manager and scheduler Using container to isolate resource Application management Perfect API and active community 01 02 03 04
  • 13. Component Design • Pod • infrastructure component • one Pod per component • ReplicationController -> HA • Define A cluster • 1 HMaster RC ( replica = 2 ) • 1 RegionServer RC ( replica = n, n >=1 ) • 1 ThriftServer RC ( replica = m, m>=0 )
  • 15. • HMaster -> use ZooKeeper • RegionServer -> Stateless designed • ThriftServer -> use proxy • HFile -> ??? Component Level
  • 16. Component Level - HFile • Shared HDFS Cluster • Keep the whole cluster stateless
  • 17. Cluster Level • What if cluster Pod is down ? • Kubernetes ReplicationController • What if Kubernetes is down ? • Mixed deployment • Few physical nodes with high CPU && RAM
  • 18. Data Replication • Replication in cluster • HDFS built in ( 3 replicas) • period hdfs fsck • Replication between clusters • snapshot + bulk load • offline cluster doing MR / Spark 01 02
  • 19. HBase Online Platform Using Kubernetes HBase at Zhihu
  • 20. Physical Node Resource CPU: 2 * 12 core Disk: 4 T Memory: 128 G
  • 21. Resource Definition (1) • Minimize the resource • Business scaled by number of containers • Pros • maximum resource usage on nodes • simplified debug • ease scale • Cons • minimum resource not easy to define by business • hardly tune params for RAMs and GC
  • 22. Resource Definition (2) • Customize container resource by business • Business scaled by number of containers • Pros • flexible RAM config and tuning • used in production
  • 23. Container Configuration • JAVA_HOME HBASE_HOME • inject to container via ENV • hdfs-site.xml core-site.xml • add xml config to container • hbase-site.xml hbase-env.sh • use start-env.sh to init configuration • Modify params during cluster running is permitted
  • 24. RegionServer Configuration • Basie Config • hbase.hregion.majorcompaction = 0 • hbase.regionserver.handler.count = 50 • hbase.regionserver.codecs = snappy • hfile.block.cache.size = 0.4 • Using G1GC ( thanks to Xiaomi )
  • 25. Network • Dedicated ip per pod • DNS register/deregister automatically • Modified /etc/hosts for pod
  • 26. Client Design • For Java/Scala • native HBase client • only offer ZK address to business • For Python • happybase • client proxy • service discovery
  • 27. API Server • A Bridge between Kubernetes and user • Encapsulate component of a HBase cluster • Restful API • Friendly interface
  • 28. Painful Points • Cons: • fully scan still impact whole cluster • speed limited coprocessor • locality && short circuit • SSD Disk
  • 29. Monitor Cluster • Physical nodes Level • nodes cpu loads && usage ( via IT ) • Cluster Level • Pods cpu loads ( via cAdvisor) • read && write rate , P95, cacheHit ( via JMX) • Table Level • client write speed && read latency ( via tracing ) • thrift server ( via JMX )
  • 30. Current Situation • 10+ online business, 300+ Pods • P95 average 20-30 ms • 99.99% SLA in 9 months
  • 32. • Almost no code needed • HBase container publish independently • Deployment and orchestration straight forward • Decoupled from physical nodes Easy
  • 33. • Resource isolation • CPU • memory • Business isolation • data • proxy • monitor Isolate
  • 34. • Multi version • mostly cdh5.5.0-hbase1.0.0 • one upgrade to 1.2 (HBASE-14283) • customize version easily • Configuration motivated by business • low latency -> read replica • etc. Flexible
  • 35. • Enhance performance • Netty on ThriftServer • Python HBase Client • SSD for Datanode • Auto scale • by RegionServer number • by JVM heap • etc. Next