Zhiyong Bai As a high performance and scalable key value database, Zhihu use HBase to provide online data store system along with Mysql and Redis. Zhihu’s platform team had accumulated some experience in technology of container, and this time, based on Kubernetes, we build flexible platform of online HBase system, create multiple logic isolated HBase clusters on the shared physical cluster with fast rapid，and provide customized service for different business needs. Combined with Consul and DNS server, we implement high available access of HBase using client mainly written with Python. This presentation is mainly shared the architecture of online HBase platform in Zhihu and some practical experience in production environment. hbaseconasia2017 hbasecon hbase

1. Building Online HBase Cluster
of
Zhihu Based on Kubernetes

2. HBase at Zhihu
Agenda
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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 )

14. Failover Design
Data Replication
Component Level
Cluster Level

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

31. Benefits
Easy
Isolate
Flexible

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

36. Thanks!