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HBaseCon2017 Apache HBase at Didi

HBaseCon
HBaseCon

In DiDi Chuxing Company, which is China’s most popular ride-sharing company. we use HBase to serve when we have a bigdata problem. We run three clusters which serve different business needs. We backported the Region Grouping feature back to our internal HBase version so we could isolate the different use cases. We built the Didi HBase Service platform which is popular amongst engineers at our company. It includes a workflow and project management function as well as a user monitoring view. Internally we recommend users use Phoenix to simplify access.even more,we used row timestamp;multidimensional table schema to slove muti dimension query problems C++, Go, Python, and PHP clients get to HBase via thrift2 proxies and QueryServer. We run many important buisness applications out of our HBase cluster such as ETA/GPS/History Order/API metrics monitoring/ and Traffic in the Cloud. If you are interested in any aspects listed above, please come to our talk. We would like to share our experiences with you.

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Apache HBase at DiDi
Kang Yuan
Agenda
1. About Us
2. DiDi HBase Platform
3. Application and Solution
4. Challenges and future
About Us
• DiDi
• In <Silicon valley>
About Us
• DiDi
• the world’s leading mobile transportation platform
• 20 million rides on a daily basis
Express Mini Bus Bus
Luxury Car Car pool Taxi
Premier Designated
driver
Trial run
Rental Car Taxi For
Aged
ofo sharing
Bike
About Us
• Mission
To Redefine the Future of Mobility
• Vision
To become a global leader in smart transportation and automotive
technology, the world’s largest operator of vehicle networks and a global
leader in smart transportation systems
About Us
• HBase Team:4 Developers
• Kang Yuan
• Yang Li
• Hanzhi Zhang
• Jingyi Yao
• Attached to BigData Architecture Department
• Cooperate with Hadoop/Hive/Spark/Flink/Druid Team closely

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HBaseCon2017 Apache HBase at Didi

  • 1. Apache HBase at DiDi Kang Yuan
  • 2. Agenda 1. About Us 2. DiDi HBase Platform 3. Application and Solution 4. Challenges and future
  • 3. About Us • DiDi • In <Silicon valley>
  • 4. About Us • DiDi • the world’s leading mobile transportation platform • 20 million rides on a daily basis Express Mini Bus Bus Luxury Car Car pool Taxi Premier Designated driver Trial run Rental Car Taxi For Aged ofo sharing Bike
  • 5. About Us • Mission To Redefine the Future of Mobility • Vision To become a global leader in smart transportation and automotive technology, the world’s largest operator of vehicle networks and a global leader in smart transportation systems
  • 6. About Us • HBase Team:4 Developers • Kang Yuan • Yang Li • Hanzhi Zhang • Jingyi Yao • Attached to BigData Architecture Department • Cooperate with Hadoop/Hive/Spark/Flink/Druid Team closely
  • 7. DiDi HBase Platform • Cluster(3) • Storage Cluster – location A • Compute Cluster – location B • Storage Cluster – location B • Location A: the same place with the hadoop Cluster • Location B: for online business or streaming • Application(50+ business and 160+ tables) • Batching Job result storage • Online writing/reading • Persistence for Streaming Jobs • 99.95% available
  • 8. DiDi HBase Platform • HBase Version • Based on 0.98.21 • Region Group patch HBASE-6721 • Thrift2 patch • Multi-tenant Problem • A bad table can put down a cluster • We don’t know who the tables belong to
  • 9. DiDi HBase Platform • Region Group
  • 10. DiDi HBase Platform • Region Group • Isolate important use cases from others • Easy to manage(web ui, user group, operation tools) • Elastic to assign resources • Different Configuration in one Cluster(for different machine types, business, testing etc) • Easy to compute the cost of the business • Easy to upgrade the regionserver in one Group before do it in the whole cluster • Cost(share pool) = TableSize*x x = cost/GB • Cost(specific group ) = Rscount*y y = cost/RS
  • 11. DiDi HBase Platform • Improvement • Web UI to show group • MoveTables bug fix • CreateTableHandler fix
  • 13. DiDi HBase Platform • DiDi HBase Service
  • 14. DiDi HBase Platform Create Project and Tables
  • 16. DiDi HBase Platform Monitor your tables Get your bill, user must care for their cost
  • 17. DiDi HBase Platform • Phoenix • Advantage • Easy to use for RDBMS User(jdbc、sql) • Auto salting table for performance and hot spot avoiding • Like a Big Mysql(One sentence to explain to our users) • Disadvantage • Some bug like ordering vector item • Unstable statistic info caching • No good in Join case • So many other hotter system :Presto/Impala/Spark SQL/Kylin • Successful Use • Row Timestamp • Multidimensional Table Schema
  • 18. • Phoenix(more customers recently) • Row Timestamp for metrics • Monitoring table write/read/storage • Easy to compute avg, max, min for metrics • Quick to query recently data • Multi-dimension Table Schema • MR/Spark Job to compute BI reporting data • Many demission combination result like city, gender, age, business type • Primary Key: JobID, date, dimission1, dimission2, dimission3… • Value: dimNameArray, valueArray • This can fit nearly all the Multi-dimension reporting business DiDi HBase Platform
  • 19. DiDi HBase Platform • Client Access • Multiple Languages Clients • C++, Go, Python, PHP • Thritf2, QueryServer • Security(ACL)
  • 20. Application and Solution(Hadoop Monitor) • Hadoop Monitor • Help hadoop to query their fsimage and jobhistory • BI for Hadoop manager • Store data in phoenix
  • 23. Application and Solution • GPS • Query Model • Rowkey:ID+Timestamp • Rowkey:Reversed GeoHash+timestamp+ID • GeoHash • A index in two dimensions • Fit HBase rowkey prefect • Point 1: same prefix code result in a nearby place • Point 2: query rowkey prefix can location a region whose area decided by prefix length
  • 24. Application and Solution • GPS • GeoHash
  • 25. Application and Solution(Online Machine Learning) • ETA(Estimated Time of Arrival) • Origin data collection • ETL • Feature extraction • Storage • Model Training
  • 26. Application and Solution • ETA(Estimated Time of Arrival) • Training data by spark, every 30 minutes • Pick up data by city from HBase in 5 minutes • Compute ETA in 25 minutes • Rowkey: Salting+CityId+Type0+Type1+Type2+Timestamp • Columns: Order,Feature • Every day HBase data will be dumped into HDFS for offline training
  • 27. Application and Solution(Image) • Traffic in Cloud • High Volume Throughput, Little Read • Read via 8 Thrift nodes • Road traffic info • POI data • Heat-map • Write with Spark Job
  • 29. Challenges and future • More connect with other bigdata framework • Hive Phoenix/HBase Handler • Integration with hive • Use Hive sql to query phoenix • Easy to load data from hadoop cluster • Join with Hive table • Spark Phoenix/HBase Handler
  • 30. Challenges and future • More stable • hbase1.x upgrade • OLAP extends • Kylin • TPC-H Compare • Thrift load balancer • Auto Group balancer • More powerful DHS