S W A T I A G A R W A L , T H O M A S P A NE B A Y I N C .

HBASE OPERATIONS

OVERVIEW

• Pre-production cluster handling production data sets and work loads• Data storage for listed item drives eBay Search

Indexing• Data storage for ranking data in the future• Leverage map reduce in the same cluster to

build search index

WHY HBASE?

• Column oriented data store on top of HDFS • Availability• Tightly integrated with Hadoop Map/Reduce

framework • No schema: storage can easily evolve and expand• Provides efficient scans and key based lookups• Supports versioning• Data consistency: Atomic row updates• Scalability• Good support from open-source community

HBASE CLUSTER225 Data nodes• Region server• Task Tracker• Data Node

14 Enterprise Nodes• Primary Name Node• Secondary Name

Node• Job Tracker Node• 5 ZooKeeper Nodes• HBase Master• CLI Node• Ganglia Reporting

Nodes• Spare Nodes for

Failover Node Hardware• 12 2TB hard-drives• 72GB RAM• 24 cores under

hyper-threading

CLUSTER LEVEL CONFIGURATION

HADOOP/HBASE CONFIGURATION• Region Server

• HBase Region Server JVM Heap Size: -Xmx15GB• Number of HBase Region Server Handlers: hbase.regionserver.handler.count=50 (Matching

number of active regions)• Region Size: hbase.hregion.max.filesize=53687091200 (50GB to avoid automatic split)• Turn off auto major compaction: hbase.hregion.majorcompaction=0

• Map Reduce• Number of Data Node Threads: dfs.datanode.handler.count=100• Number of Name Node Threads: dfs.namenode.handler.count=1024 (Todd:• Name Node Heap Size: -Xmx30GB• Turn Off Map Speculative Execution: mapred.map.tasks.speculative.execution=false• Turn off Reduce Speculative Execution: mapred.reduce.tasks.speculative.execution=false

• Client settings• HBase RPC Timeout: hbase.rpc.timeout=600000 (10 minutes for client side timeout)• HBase Client Pause: hbase.client.pause=3000

• HDFS • Block Size: dfs.block.size=134217728 (128MB)• Data node xciever count: dfs.datanode.max.xcievers=131072

• Number of mappers per node: mapred.tasktracker.map.tasks.maximum=8• Number of reducers per node: mapred.tasktracker.reduce.tasks.maximum=6• Swap turned off

HBASE TABLES• Multiple tables in a single cluster storing inventory data (item,

seller)• Multiple column families per table: <= 3• Number of columns: < 200.• 1.45 billion rows total• Max row size: ~20KB• Average row size: ~10KB

• 13.01TB data• Bulk load speed: ~500 Million items in 30 minutes• Random write updates: 25K records per minute• Scan speed: 2004 rows per second per region server (average

version 3), 465 rows per second per region server (average version 10)

• Scan speed with filters: 325~353 rows per second per region server

HBASE TABLES (CONT.)

• Pre-split 3600 Regions per table• Table is split into roughly equal sized regions.• Important to pick well distributed keys• Currently using bit reversal

• Region split has been disabled by setting very large region size. • Major compaction on demand• Purge rows periodically• Balance regions among region servers on demand

ROWKEY SCHEME AND SHARDING

• RowKey• 64-bit unsigned integer• Bit reversal of document id• Document ID: 2• RowKey: 0x4000000000000000

• HBase creates regions with even RowKey range• Each map task maps to each region.

MONITORING SYSTEMS

• Ganglia• Nagios Alerts• Table consistency – hbck• Table balancing – in-house tool• Region size• CPU usage• Memory usage• Disk failures• HDFS block count• ……

• In-house Job Monitoring System• Based on OpenTSDB• Job Counters

CHALLENGES/ISSUES• HBase stability • HDFS issues can impact Hbase, such as name node failure• Map/Reduce jobs can impact HBase region servers, such as high memory

usage• Region stuck in migration

• HBase health monitoring• HBase table maintenance• HBase table regions become unbalanced• Major compaction after row purge and updates

• Software Upgrades cause big downtime• Normal hardware failures may cause issues• Stuck regions due to failed hard disk

• Region servers were deadlocked due to jvm• Testing

FUTURE DIRECTION

• High scalability• Scale out a table with more regions• Scale out the whole cluster with more data

• High availability• No downtime for upgrades

• Adopt co-processor• Near-Real-Time Indexing

COMMUNITY ACKNOWLEDGEMENT

• Kannan Muthukkaruppan• Karthik Ranganathan• Lars George• Michael Stack• Ted Yu• Todd Lipcon• Konstantin Shvachko