PingCAP-Infra-Meetup-105-Happy+Hacking+TiDB

本次杜川老师的分享主要分成三个部分: 1.首先通过对现有 Streaming 系统和 Batch 系统的分析,讨论了在数据处理领域 Streaming 和 Batch 的异同,明确了 Streaming 的核心本质,探讨了 Streaming 和 Batch 融合处理的可能性和必要性,并对现有类似系统进行了简单的分析。 2.简单回顾了 RDMS 中经典的 Volcano 模型的执行流程,探讨了在 RDMS 上支持 Streaming 处理的难点以及 Streaming SQL 设计的关键要素。 3.介绍了 TBSSQL 的设计思路,架构设计和若干关键技术点的方案选择,展示了 TBSSQL 的运行 Demo。并以 TBSSQL 为例,简单介绍了在 TiDB 上增加一个 Feature 的大致思路和入手点。
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1. Streaming, SQL and TiDB Batch Streaming SQL Presented By Du Chuan

2.About me • Infrastructure engineer - working on cloud database • TiDB committer - mainly in the TiDB SQL layer • Technophile - database & distribute systems

3.Agenda • Streaming Overview • SQL and Streaming SQL • Introduction to TBSSQL

4.Part I – Streaming Overview

5.“Before it’s news, it’s data. After it‘s news, it’s data.”

6.Batch systems VS Streaming systems

7.Batch VS Streaming • Processing on data • Get data via processing • Finite data set • Infinite data streaming

8.But wait… What’s streaming, indeed?

9.Essentials of Streaming • Bind with time • Strictly ordered by time • Infinite

10.Streaming is the way the world organizing data

11. Batch …… …… Streaming

12.Batch is a snapshot of some Streaming At some point of time In some scope

13.So... One system to rule them both ?

14.Batch && Streaming systems • Using a batch runtime to simulate • Using a streaming runtime to simulate streaming batch

15.What about the RDMS?

16. An example – Ads click analysis Click- User ID Click User 1 ADS Firehose 1 DML Click- User ID Click User 2 ADS 2 Click- User ID Click User 3 ADS 3 Click- User ID Click User 1 ADS 1 Click- User ID Click User 3 ADS 3 Click- User ID User Table Ads Table Click User 1 ADS 1

17.Part II – SQL and Streaming SQL

18.The volcano model… Again

19.SQL consists various kinds of operators

20. The SQL operators SELECT t1.a, count(*) FROM t1 JOIN t2 ON t1.b = t2.b WHERE t1.c > 10 GROUP BY t1.a; Projection Join Selection Aggregation

21.The SQL operator interface • Open: Init operator SQL Operator • Next: Consume input and generate output • Close: Do cleanup job

22.The volcano model input = child.Next() return proj(input, t.a + t.b) SELECT t.a + t.b (". $ + ". &) FROM t input = child.Next() WHERE t.c > 10; return filter(input, t.c > 10) (t.c > 10) t return scan(t)

23.The SQL operator reordering >1 ( ) ) ) > 2 < 2 > ( ) ) ) 2 < >1 2

24. Aggregation operator • SELECT a, COUNT(c) AS cnt FROM t GROUP BY a HAVING cnt > 1; 1. Grouping 2. Apply aggregators 3. Filter using“Having”clause

25. Join operator • SELECT * FROM t1 (LEFT/RIGHT/FULL OUTER) JOIN)t2 ON t1.a = t2.a; 1. Get Cartesian product 2. Filter using “On” . clause

26.When SQL operator meets Streaming…

27.SELECT a, COUNT(c) FROM t GROUP BY a; 1. Grouping(Never End) 2. Apply aggregators Infinite Data Stream

28.Solution: limit the scope of streaming using “window”

29. SELECT a, COUNT(c) FROM t GROUP BY a; 1. Grouping 2. Apply Aggregators Window1 Infinite Data Stream Window2

TiDB 是一款定位于在线事务处理/在线分析处理( HTAP: Hybrid Transactional/Analytical Processing)的融合型数据库产品,实现了一键水平伸缩,强一致性的多副本数据安全,分布式事务,实时 OLAP 等重要特性。