This document provides an overview and best practices for operating HBase clusters. It discusses HBase and Hadoop architecture, how to set up an HBase cluster including Zookeeper and region servers, high availability considerations, scaling the cluster, backup and restore processes, and operational best practices around hardware, disks, OS, automation, load balancing, upgrades, monitoring and alerting. It also includes a case study of a 110 node HBase cluster.

1. HBase
Operations
&
Best Practices
Venu Anuganti
July 2013
http://scalein.com/
Blog: http://venublog.com/
Twitter: @vanuganti

2. Who am I
o Data Architect, Technology Advisor
o Founder of ScaleIN, Data Consulting Company, 5+ years
o 100+ companies, 20+ from Fortune 200
o http://scalein.com/
o Architect, Implement & Support SQL, NoSQL and BigData
Solutions
 Industry: Databases, Games, Social, Video, SaaS,
Analytics, Warehouse, Web, Financial, Mobile,
Advertising & SEM Marketing

3. Agenda
 BigData - Hadoop & HBase Overview
 BigData Architecture
 HBase Cluster Setup Walkthrough
 High Availability
 Backup and Restore
 Operational Best Practices

4. BigData Overview

5. BigData Trends
• BigData is the latest industry buzz, many companies
adopting or migrating
o Not a replacement for OLTP or RDBMS systems
• Gartner – 28B in 2012 & 34B in 2013 spend
o 2013 top-10 technology trends – 6th place
• Solves large data problems that existed for years
o Social, User, Mobile growth demanded such a solution
o Google “BigTable” is the key, followed by Amazon “Dynamo”;
new papers like Dremel drives it further
o Hadoop & ecosystem is becoming synonym for BigData
• Combines vast structured/un-structured data
o Overcomes from legacy warehouse model
o Brings data analytics & data science
o Real-time, mining, insights, discovery & complex reporting

6. BigData
• Key factors - Pros
 Can handle any size
 Commodity hardware
 Scalable, Distributed, Highly
Available
 Ecosystem & growing
community
• Key factors – Cons
 Latency
 Hardware evolution, even
though designed for
commodity
 Does not fit for all

7. BigData Architecture

9. Low Level Architecture

10. Why HBase

11. Why HBase
• HBase is proven, widely adopted
 Tightly coupled with hadoop ecosystem
 Almost all major data driven companies using it
• Scales linearly
 Read performance is its core; random, sequential reads
 Can store tera/peta bytes of data
 Large scale scans, millions of records
 Highly distributed
• CAP Theorem – HBase is CP driven
• Competition: Cassandra (AP)

12. Hadoop/HBase
Cluster Setup

13. Cluster Components
3 Major Components
 Master(s)
 HMaster
 Coordination
 Zookeeper
 Slave(s)
 Region server
Name Node
HMaster
Zookeeper
MASTER
Data Node
Region Server
SLAVE 1
Data Node
Region Server
SLAVE 3
Data Node
Region Server
SLAVE 2

14. How It Works
HMASTERDDLCLIENT
HDFS
REGION SERVERS
RS RS RS
ZOOKEEPER CLUSTER
ZK ZK ZK

15. Zookeeper
 Zookeeper
o Coordination for entire cluster
o Master selection
o Root region server lookup
o Node registration
o Client always communicates with Zookeper for lookups
(cached for sub-sequent calls)
hbase(main):001:0> zk "ls /hbase"
[safe-mode, root-region-server, rs, master, shutdown,
replication]

16. Zookeeper Setup
 Zookeeper
• Dedicated nodes in the cluster
• Always in odd number
• Disk, memory, cpu usage is low
• Availability is a key

17. Master Node
 HMaster
o Typically runs with Name Node
o Monitors all region servers, handles RS failover
o Handles all meta data changes
o Assigns regions
o Interface for all meta data changes
o Load balancing on idle times

18. Master Setup
• Dedicated Master Node
o Light on use, but should be on reliable hardware
o Good amount of memory and CPU can help
o Disk space is pretty nominal
• Must Have Redundancy
o Avoid single point of failure (SPOF)
o RAID preferred for redundancy or even JBOD
o DRBD or NFS is also preferred

19. Region Server
 Region Server
o Handles all I/O requests
o Flush MemStore to HDFS
o Splitting
o Compaction
o Basic element of table storage
o Table => Regions => Store per Column Family => CF => MemStore /
CF/Region && StoreFile /Store/Region => Block
o Maintains WAL (Write Ahead Log) for all changes

20. Region Server - Setup
• Should be stand-alone and dedicated
o JBOD disks
o In-expensive
o Data node and region server should be co-located
• Network
o Dual 1G, 10G or InfiniBand, DNS lookup free
• Replication - at least 3, locality
• Region size for splits; too many or too small
regions are not good.

21. Cluster Setup – 10 Node

22. High Availability

23. High Availability
• HBase Cluster - Failure Candidates
 Data Center
 Cluster
 Rack
 Network Switch
 Power Strip
 Region or Data Node
 Zookeeper Node
 HBase Master
 Name Node

24. HA - Data Center
• Cross data center, geo distributed
• Replication is the only solution
 Up2date data
 Active-active
 Active-passive
 Costly (can be sized)
 Need dedicated network
• On-demand offline cluster
 Only for disaster recovery
 No up2date copy
 Can be sized appropriately
 Need to reprocess for latest data

25. HA – Redundant Cluster
• Redundant cluster within a data center using
replication
• Mainly to have backup cluster for disasters
 Up2date data
 Restore a state back using TTL based
 Restore deleted data by keeping deleted cells
 Run backups
 Read/write distributed with load balancer
 Support development or provide on-demand data
 Support low important activities
• Best practice: Avoid redundant cluster, rather have
one big cluster with high redundancy

26. HA – Rack, Network, Power
• Cluster nodes should be rack and switch aware
• Loosing a rack or a network switch should not bring
cluster down
• Hadoop has built-in rack awareness
 Assign nodes based on rack diagram
 Redundant nodes are within rack, across switch and
rack
 Manual or automatic setup to detect location
• Redundant power and network within each node
(master)

27. HA – Region Servers
• Loosing a region server or data node is very
common, in many cases it could be very frequent
• They are distributed and replicated
• Can be added/removed dynamically, taken out for
regular maintenance
• Replication factor of 3
– Can loose ⅔rd of the cluster nodes
• Replication factor of 4
– Can loose ¾th of the cluster nodes

28. HA – Zookeeper
• Zookeeper nodes are distributed
• Can be added/removed dynamically
• Should be implemented in odd number, due to
quorum (majority voting wins the active state)
• If 4, can loose 1 node (3 major voting)
• If 5, can loose 2 nodes (3 major voting)
• If 6, can loose 2 nodes (4 major voting)
• If 7, can loose 3 nodes (4 major voting)
• Best Practice: 5 or 7 with dedicated hardware.

29. HA – HMaster
• HMaster - single point of failure
• HA - Multiple HMaster nodes within a cluster
 Zookeeper co-ordinates master failure
 Only one active at any given point of time
 Best practice: 2-3 HMasters, 1 per rack

30. Scalability

31. How to scale
• By design, cluster is highly distributed and scalable
• Keep adding more region servers to scale
 Region splits
 Replication factor
 Row key design is a key factor for scaling writes
 No single “hot” region
 Bulk loading, pre-split
 Native java access X other protocols like thrift
 Compaction at regular intervals

32. Performance
 Benchmarking is a key
• Nothing fits for all
• Simulate use cases and run the tests
oBulk loading
oRandom access, read/write
oBulk processing
oScan, filter
• Negative performance
oReplication factor
oZookeeper nodes
oNetwork latency
oSlower disks, CPUs
oHot regions, Bad row key or Bulk loading without pre-splits

33. Tuning
 Tune the cluster to best fit the environment
• Block Size, LRU cache, 64K default, per CF
• JBOD
• Memstore
• Compaction, manual
• WAL flush
• Avoid long GC pauses, JVM
• Region size, small is better, split based on “hot”
• Batch size
• In-memory column families
• Compression, LZO
• Timeouts
• Region handler count, threads/region
• Speculative execution
• Balancer, manual

34. Backup
&
(Point-in-time ) Restore

35. Backup - Built-in
• In general no external backup needed
• HBase is highly distributed and has built-in
versioning, data retention policy
 No need to backup just for redundancy
 Point-in-time restore:
• Use TTL/Table/CF/C and keep the history for X hours/days
 Accidental deletes:
• Use ‘KeepDeletedCells’ to keep all deleted data

36. Backup - Tools
• Use Export/Import tool
 Based on timestamp; and use it for point-in-time
backup/restore
• Use region snapshots
 Take HFile snapshots and copy them over to new
storage location
 Copy Hlog files for point-in-time roll-forward from
snapshot time (replay using WALPlayer post import).
 Table snapshots (0.94.6+)

37. Backup - Replication
• Use replicated cluster as one of the backup /
disaster recovery
• Statement based, write ahead log (WAL, HLog)
from each region server
 Asynchronous
 Active Active using 1-1 replication
 Active Passive using 1-N replication
 Can be of same or different node size
 0.92 onwards Active Active possible

38. Operational
Best Practices

39. Hardware
• Commodity Hardware
• 1U or 2U preferred, avoid 4U or NAS or expensive
systems
• JBOD on slaves, RAID 1+0 on masters
• No SSDs, No virtualized storage
• Good number of cores (4-16), HT enabled
• Good amount of RAM (24-72G)
• Dual 1G network, 10G or InfiniBand

40. Disks
• SATA, 7/10/15K, cheaper the better
• Use RAID firmware drives, faster error detection &
enable disks to fail on h/w errors
• Limit to 6/8 drives on 8 core, allow 1 drive/core
= 100 IOPS/Drive
= 4 * 1T = 4T, 400 IOPS, 400MB
= 8 * 500G = 4T, 800 IOPS
= not beyond 800/900MB/sec due to n/w saturation
• Ext3/ext4/XFS
• Mount => noatime, nodiratime

41. OS, Kernel
• RHEL or CentOS or Ubuntu
• Swappiness=0, and no swap files
• File limits to hadoop user
(/etc/security/limits.conf) => 64/128K
• JVM GC, HBase heap
• NTP
• Block size

42. Automation
• Automation is a key in distributed cluster setup
 To easily launch a new node
 To restore to base state
 Keep same packages, configurations across the cluster
• Use puppet/Chef/Existing process
 Keep as much as possible puppetized
 No accidental upgrades as it can restart the service
• Cloudera Manager (CM) for any node
management tasks
 You can also puppetize & automate the process
 CM will install all necessary packages

43. Load Balancer
• Internal
 Periodically run balancer to ensure data distribution
among region servers
• hadoop-daemon.sh start balancer -threshold 10
• External
 Has built-in load balancing capability
 If using thrift bindings; then thrift servers needs to be
load balanced
 Future versions will address thrift balancing as well

44. Upgrades
• In general upgrades should be well planned
• To update changes to cluster nodes (OS, configs,
hardware, etc.); you can also do rolling restart
without taking cluster down
• Hadoop/HBase supports simple upgrade paths
with rollback strategy to go back to old version
• Make sure HBase/Hadoop versions are compatible
• Use rolling restart for minor version upgrades

45. Monitoring
• Quick Checks
 Use built-in web tools
 Cloudera manager
 Command line tools or wrapper scripts
• RRD, Monitoring
 Cloudera manager
 Ganglia, Cacti, Nagios, NewRelic
 OpenTSDB
 Need proper alerting system for all events
 Threshold monitoring for any surprises

46. Alerting System
 Need proper alerting system
 JMX exposes all metrics
 Ops Dashboard (Ganglia, Cacti, OpenTSDB, NewRelic)
 Small dashboard for critical events
 Define proper levels for escalation
 Critical
 Loosing a Master or ZooKeeper Node
 +/- 10% drop in performance or latency
 Key thresholds (load, swap, IO)
 Loosing 2 or more slave nodes
 Disk failures
 Loosing a single slave node (critical in prime time)
 Un-balanced nodes
 FATAL errors in logs

47. Case Study

48. Case Study - 1
• 110 node cluster
 Dual Quad Core, Intel Xeon, 2.2GHz
 48G, no swap
 6 2T SATA, 7K
 Ubuntu 11.04
 Puppet
 Fabric for running commands on all nodes
 /home/hadoop is everything, symlinks
 Nagios
 OpenTSDB for Trending points, dashboard
 M/R limited to 50% of available RAM

49. Questions ?
• http://scalein.com/
• http://venublog.com/
• venu@venublog.com
• Twitter: @vanuganti