2019-07-01Li ShenMySQL Scalability
TiDB 3.0 is released! This blog post introduces some highlights of TiDB 3.0, including major features focused on stability, significant performance improvements in Sysbench and TPC-C benchmarks, a newly introduced component, and important features and improvements.
The motivation behind building TiSpark was to enable real-time analytics on TiDB without the delay and challenges of ETL. Extract, transform, and load (ETL)--a process to extract data from operational databases, transform that data, then load it into a database designed to supporting analytics--has been one of the most complex, tedious, error-prone, and therefore disliked tasks for many data engineers. However, it was a necessary evil to make data useful, because there hasn't been good solutions on the market to render ETL obsolete--until now.
In this 5-minute tutorial for beginners, we will show you how to spin up a standard TiDB cluster using Docker Compose on your local computer, so you can get a taste of its hybrid power, before using it for work or your own project in production.
2018-04-29Shen LiMySQL Scalability
TiDB 2.0 is released! We absorbed insights and feedbacks from our customers, listened to requests and issues from our community, and reflected internally on our ultimate vision of building a distributed hybrid transactional and analytical processing database that scales itself, heals itself, and lives in the cloud.
As an open source distributed NewSQL Hybrid Transactional/Analytical Processing (HTAP) database, TiDB contains the most important asset of our customers--their data. One of the fundamental and foremost requirements of our system is to be fault-tolerant. But how do you ensure fault tolerance in a distributed database? This article covers the top fault injection tools and techniques in Chaos Engineering, as well as how to execute Chaos practices in TiDB.
Doing performance tuning on distributed systems is no joking matter. It's much more complicated than on a single node server, and bottlenecks can pop up anywhere, from system resources in a single node or subcomponent, to cooperation between nodes, to even network bandwidth. Performance tuning is a practice that aims to find these bottlenecks and address them, in order to reveal more bottlenecks and address them as well, until the system reaches an optimal performance level. In this article, I will share some best practices on how to tune "write" operations in TiDB to achieve maximum performance.