2020 has been a difficult year for everyone, but at PingCAP we continued to innovate and inform. This year, we released TiDB 4.0, a real-time Hybrid Transactional/Analytical Processing (HTAP) database, and we published 50+ technical blog posts and 17 case studies. We’d like to thank all our TiDB contributors and users for their trust and support. They not only helped us improve our product, but they also contributed great material for many of our excellent articles.
From pondering the future of databases, to intrepid bug hunters, to TiDB case studies, let’s take a look back at our 10 most popular posts in 2020:
What principles will influence the database of the future? In this post, Ed Huang, PingCAP’s CTO, suggests that the key drivers will be unification, adaptiveness, and intelligence.
Dr. Manuel Rigger and his colleague have found 400+ bugs in popular DBMSs, including 50+ TiDB bugs. Learn their techniques for finding logic bugs in DBMSs.
In this post, PalFish explained why they chose TiDB over MongoDB and MySQL. The key factors were their application requirements and their perspective on NewSQL databases.
As PayPay’s business grew, their payment management database became a bottleneck. Learn why PayPay chose TiDB over Aurora to handle their most crucial data and how they efficiently migrated to TiDB.
VIPKid chose TiDB to manage its high data volume, highly concurrent write application. Learn how TiDB excels in that scenario, along with multidimensional queries, data life cycle management, and real-time analytics.
As its business quickly grew, ZTO Express found Exadata, Kudu, and HBase couldn’t meet their database requirements. To scale out their database and perform multi-dimensional analytics in near real time, they migrated from Oracle Exadata to TiDB.
Key Visualizer is a visual diagnostic tool that makes it easier to troubleshoot distributed SQL databases. Users can observe system health, quickly find hotspots in the cluster, and gain deep insights into applications.
To trace system calls in Linux effectively, you can use perf to analyze system calls that have latency in general scenarios. For containers or Kubernetes that use cgroup v2, traceloop is more convenient.
In part 2 of the Rust Compile Time series, Brian Anderson, one of Rust’s original authors, talked about monomorphization, using the TiKV project as a case study.
Nick Cameron, a long-time Rust programmer, talked about his early impressions of Go.
