HBaseCon2017 Apache HBase at Didi
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Didi is a leading mobile transportation platform that aims to redefine mobility by utilizing an HBase architecture for its big data solutions. The Didi HBase platform supports extensive applications, including online reading/writing and batch job storage, while also addressing challenges related to multi-tenancy and resource management. Future goals include further integration with big data frameworks and improving the stability and capabilities of the HBase environment.
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
- 22. Application and Solution(Gis Query) • GPS
- 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
- 28. Application and Solution • Architecture
- 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
- 31. Any Question?