Shen PingCAP 2017
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1 Shen PingCAP
2 About me Shen Li ( 申砾 ) Tech Lead of TiDB, VP of Engineering Netease / 360 / PingCAP Infrastructure software engineer
3 WHY DO WE NEED A NEW DATABASE?
4 Brief History Standalone RDBMS NoSQL Middleware & Proxy NewSQL RDBMS NoSQL NewSQL 1970s Present MySQL PostgreSQL Oracle DB2... Redis HBase Cassandra MongoDB... Google Spanner Google F1 TiDB
5 NewSQL database Horizontal Scalability ACID Transaction High Availability Auto-Failover SQL
6 OLTP & OLAP
7 Why use two separate systems Huge data size Complex query logic Latency VS Throughput Point query VS Full range scan Transaction & Isolation level
8 HOW DO WE BUILD A NEWSQL DATABASE?
9 What is TiDB Scalability as the first class feature SQL is necessary Compatible with MySQL, in most cases OLTP + OLAP = HTAP (Hybrid Transactional/Analytical Processing) 24/7 availability, even in case of datacenter outages Open source, of course
10 Architecture Stateless SQL Layer grpc TiDB... TiDB... TiDB TiKV... TiKV... TiKV... TiKV grpc Metadata / Timestamp request grpc Placeme nt Driver (PD) Control flow: Balance / Failover Raft Raft Raft Distributed Storage Layer
11 Data distribution Hash Based Partition o Redis o Scale well o Bad for scan Range Based Partition o HBase o Good for SQL workload o Range size should be small enough and large enough
12 Storage stack 1/2 TiKV is the underlying storage layer Physically, data is stored in RocksDB We build a Raft layer on top of RocksDB o What is Raft? Written in Rust! TiKV API (grpc) Transaction MVCC Raft (grpc) RocksDB Transactional KV API ( /pingcap/tidb/blob/ master/cmd/bench Raw KV API kv/main.go) ( m/pingcap/tidb/bl ob/master/cmd/b enchraw/main.go)
13 Storage stack 2/2 Data is organized by s : a set of continuous keyvalue pairs RPC (grpc) Transactio n MVCC Raft RocksDB Raft group 1:[a-e] 3:[k-o] 4:[p-t] 5:[u-z] RocksDB Instance 1:[a-e] 2:[fj] 3:[k-o] 4:[p-t] RocksDB Instance 2:[fj] 3:[k-o] 5:[u-z] RocksDB Instance 1:[a-e] 2:[fj] 4:[p-t] 5:[u-z] RocksDB Instance
14 Dynamic Multi-Raft What s Dynamic Multi-Raft? o Dynamic split / merge Safe split / merge split 1.1:[ac] 1:[a-e] split 1.2:[de]
15 Safe Split: 1/4 Raft group 1:[a-e] TiKV1 raft 1:[a-e] TiKV2 raft 1:[a-e] TiKV3 Leader Follower Follower
16 Safe Split: 2/4 1.1:[a-c] 1.2:[d-e] TiKV1 raft 1:[a-e] TiKV2 raft 1:[a-e] TiKV3 Follower Follower Leader
17 Safe Split: 3/4 Split log (replicated by Raft) 1.1:[a-c] 1.2:[d-e] TiKV1 Split log 1:[a-e] TiKV2 1:[a-e] TiKV3 Leader Follower Follower
18 Safe Split: 4/4 1.1:[a-c] raft 1.1:[a-c] raft 1.1:[a-c] 1.2:[d-e] TiKV1 raft 1.2:[d-e] TiKV2 raft 1.2:[d-e] TiKV3 Leader Follower Follower
19 Scale-out (initial state) Node A is running out of space Node B Node A 1* Node C 1 3 Node D
20 Scale-out (add new node) Add a new node E Node B 1^ 2 1* Node D Node A 3 3 Node C Node E 1) Transfer leadership of region 1 from Node A to Node B
21 Scale-out (balance) Node B 1* Node D 3 3 Node A E Node 1 Node C 2) Add Replica on Node E
22 Scale-out (balance) Node B 1* Node D 3 3 Node A E Node 1 Node C 3) Remove Replica from Node A
23 ACID Transaction Based on Google Percolator Almost decentralized 2-phase commit o Timestamp Allocator Optimistic transaction model Default isolation level: Repeatable Read External consistency: Snapshot Isolation + Lock o SELECT FOR UPDATE
24 Distributed SQL Full-featured SQL layer Predicate pushdown Cost-based optimizer Parallel Operators Multiple Join Operators Hash/Streaming Operators
25 TiDB SQL Layer overview
26 What happens behind a query CREATE TABLE t (c1 INT, c2 TEXT, KEY idx_c1(c1)); SELECT COUNT(c1) FROM t WHERE c1 > 10 AND c2 = golang ;
27 Query Plan Physical Plan on TiDB Final Aggregate SUM(COUNT(c1)) DistSQL Scan COUNT(c1) COUNT(c1) COUNT(c1) COUNT(c1) TiKV TiKV TiKV Physical Plan on TiKV (index scan) Partial Aggregate COUNT(c1) Row Filter c2 = golang Read Row Data by RowID Read Index idx1: (10, + ) Row RowID
28 Distributed Hash Join
29 Job Scheduling Priority Job Size Scheduler Workers Job Job Queue
30 SQL IS NOT ENOUGH
31 TiSpark 1/3 TiDB + SparkSQL = TiSpark PD Spark Master TiDB TiDB TiDB Spark Exec Spark Exec Spark Exec TiKV Connector TiKV Connector TiKV Connector Data Storage & Coprocessor TiKV TiKV TiKV TiKV TiKV
32 TiSpark 2/3 TiKV Connector is better than JDBC connector Index support Complex Calculation Pushdown CBO Pick up right Access Path Join Reorder Priority & Isolation Level
33 TiSpark 3/3 Analytical / Transactional support all on one platform No need for ETL Real-time query with Spark Possibility for get rid of Hadoop Embrace Spark echo-system Support of complex transformation and analytics with Scala / Python and R Machine Learning Libraries Spark Streaming
34 Hybrid Transactional/Analytical Processing
35
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