How to build a ‘self-driving’ database

TiDB

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自治和智能化是新一代分布式数据库的发展趋势，在这次 Talk 中将分享对这方面的思考和在 TiDB 中的计划和落地情况。自驱动数据库帮助我们更好的利用已有的硬件资源并且能够同时处理大量工作。

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1 .How to build a Self-Driving database An overview Dongxu

2 .Who am I ! Dongxu Huang ! CTO & Cofounder, PingCAP ! Distributed system engineer / Open source advocator ! h@pingcap.com ! TiDB / TiKV / TiSpark

3 .Self-Driving?

4 .

5 .

6 .Why now? ! Better hardware ! Better tools ! Workload is different

7 .SQL Tuning

8 .

9 .

10 .Imagine we got a logical plan: Its physical plan could be: or:

11 .Cost estimation Network cost Memory cost CPU cost In TiDB, the default memory factor is 5 and CPU factor is 0.8. For example: Operator Sort(r), its cost would be:

12 .Data placement

13 .Problem: Uneven Data Distribution ! Dealing with hotspot ○ Choose a wrong sharding key ! Inefficient usage ○ Some are busy ○ Some are idle ! Caused by the nature of RDBMS

14 .A little bit about how data is organized in TiDB PD PD TSO/Data location Data location PD PD Cluster Metadata Spark TiDB Driver TiKV TiKV DistSQL API DistSQL API Job TiDB Application TiDB Worker SparkSQ KV API TiKV TiKV L Syncer Worker TiDB TiKV TiKV Worker TiDB Spark Cluster TiDB TiKV Cluster Cluster (Storage) TiSpark

15 . Any ORM which ODBC/JDBC MySQL Client supports MySQL MySQL Network Protocol SQL Parser TiDB Cost-based Optimizer Distributed Executor (Coprocessor) TiKV Node1 Node2 Node3 Node4

16 .Why Raft? ! Saft split/merge ! Self-healing Start Follower ! Easy to implement Election Timeout, Start new election. Receive higher Find leader or term msg receive higher term msg Election, re- Candidat campaign Leader e Receive majority vote

17 . Table mapping: Rows => Key-Value pairs (-∞, a) Dataset [a, b) (b, +∞)

18 .Data movement Step 1:

19 .Data movement Step 2:

20 .Placement Driver: The scheduler

21 .

22 .Only by Region count? Size also matters.

23 .Hot Balance

24 .Scheduler - More ! More… ○ Weight Balance - High-weight TiKV will save more data ○ Evict Leader Balance - Some TiKV node can’t have any Raft leader ! OpInfluence - Avoid over frequent balancing

25 .Autonomy

26 .

27 .Our work: Oasis

28 .Isolation Forest Start API ! Anomaly detection New Job ! Easy to implement ! Paper IForest Get Metrics From Prometheus Model Features Train Error Predict Alert Report Finish

29 .Related work - OtterTune ! Database Tuning-as-a-Service ○ Automatically generates DBMS knob configurations ○ Reuse data from previous tuning sessions ! Supported systems ○ PostgreSQL ○ MySQL ○ Greenplum ○ Vectorwise

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