惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

H
Heimdal Security Blog
小众软件
小众软件
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
罗磊的独立博客
Google DeepMind News
Google DeepMind News
大猫的无限游戏
大猫的无限游戏
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Hugging Face - Blog
Hugging Face - Blog
阮一峰的网络日志
阮一峰的网络日志
A
About on SuperTechFans
宝玉的分享
宝玉的分享
博客园 - 聂微东
月光博客
月光博客
Cyberwarzone
Cyberwarzone
Microsoft Security Blog
Microsoft Security Blog
V
Visual Studio Blog
Project Zero
Project Zero
T
Tor Project blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
L
LINUX DO - 最新话题
博客园 - 叶小钗
Recent Commits to openclaw:main
Recent Commits to openclaw:main
Attack and Defense Labs
Attack and Defense Labs
Spread Privacy
Spread Privacy
Forbes - Security
Forbes - Security
Simon Willison's Weblog
Simon Willison's Weblog
N
Netflix TechBlog - Medium
P
Proofpoint News Feed
Engineering at Meta
Engineering at Meta
Hacker News: Ask HN
Hacker News: Ask HN
I
InfoQ
M
MIT News - Artificial intelligence
AI
AI
博客园 - 三生石上(FineUI控件)
W
WeLiveSecurity
C
Check Point Blog
The Hacker News
The Hacker News
C
Cyber Attacks, Cyber Crime and Cyber Security
Application and Cybersecurity Blog
Application and Cybersecurity Blog
T
Tenable Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
The Cloudflare Blog
Blog — PlanetScale
Blog — PlanetScale
美团技术团队
D
Darknet – Hacking Tools, Hacker News & Cyber Security
GbyAI
GbyAI
Hacker News - Newest:
Hacker News - Newest: "LLM"
腾讯CDC
K
Kaspersky official blog

Blog — PlanetScale

Keeping a Postgres queue healthy — PlanetScale Patterns for Postgres Traffic Control — PlanetScale Graceful degradation in Postgres — PlanetScale High memory usage in Postgres is good, actually — PlanetScale Stripe Projects partnership: Provision PlanetScale Postgres and MySQL databases from the Stripe CLI — PlanetScale Enhanced tagging in Postgres Query Insights — PlanetScale Behind the scenes: How Database Traffic Control works — PlanetScale Introducing Database Traffic Control — PlanetScale Scaling Postgres connections with PgBouncer — PlanetScale Drizzle joins PlanetScale — PlanetScale Video Conferencing with Postgres — PlanetScale Faster PlanetScale Postgres connections with Cloudflare Hyperdrive — PlanetScale Introducing the PlanetScale MCP server — PlanetScale Database Transactions — PlanetScale Automating our changelog with Cursor commands — PlanetScale Postgres 18 is now available — PlanetScale Using MotherDuck with PlanetScale — PlanetScale $50 PlanetScale Metal is GA for Postgres — PlanetScale AI-Powered Postgres index suggestions — PlanetScale $5 PlanetScale is live — PlanetScale Announcing Vitess 23 — PlanetScale $50 PlanetScale Metal — PlanetScale Report on our investigation of the 2025-10-20 incident in AWS us-east-1 — PlanetScale $5 PlanetScale — PlanetScale Benchmarking Postgres 17 vs 18 — PlanetScale Larger than RAM Vector Indexes for Relational Databases — PlanetScale Partnering with Cloudflare to bring you the fastest globally distributed applications — PlanetScale Processes and Threads — PlanetScale PlanetScale for Postgres is now GA — PlanetScale Postgres High Availability with CDC — PlanetScale Announcing Neki — PlanetScale Caching — PlanetScale The principles of extreme fault tolerance — PlanetScale Announcing PlanetScale for Postgres — PlanetScale Benchmarking Postgres — PlanetScale Announcing Vitess 22 — PlanetScale The Real Failure Rate of EBS — PlanetScale IO devices and latency — PlanetScale Announcing PlanetScale Metal — PlanetScale PlanetScale Metal: There’s no replacement for displacement — PlanetScale Upgrading Query Insights to Metal — PlanetScale Automating cherry-picks between OSS and private forks — PlanetScale Database Sharding — PlanetScale Anatomy of a Throttler, part 3 — PlanetScale Introducing sharding on PlanetScale with workflows — PlanetScale Announcing Vitess 21 — PlanetScale Announcing the PlanetScale vectors public beta — PlanetScale Anatomy of a Throttler, part 2 — PlanetScale Instant deploy requests — PlanetScale Anatomy of a Throttler, part 1 — PlanetScale Increase IOPS and throughput with sharding — PlanetScale Tracking index usage with Insights — PlanetScale Faster backups with sharding — PlanetScale Building data pipelines with Vitess — PlanetScale The State of Online Schema Migrations in MySQL — PlanetScale Optimizing aggregation in the Vitess query planner — PlanetScale Dealing with large tables — PlanetScale Announcing Vitess 20 — PlanetScale Self-managed Vitess vs Managed Vitess with PlanetScale — PlanetScale Achieving data consistency with the consistent lookup Vindex — PlanetScale The MySQL adaptive hash index — PlanetScale Introducing global replica credentials — PlanetScale Profiling memory usage in MySQL — PlanetScale Summer 2023: Fuzzing Vitess at PlanetScale — PlanetScale How PlanetScale makes schema changes — PlanetScale Identifying and profiling problematic MySQL queries — PlanetScale The Problem with Using a UUID Primary Key in MySQL — PlanetScale Announcing Vitess 19 — PlanetScale PlanetScale forever — PlanetScale Introducing schema recommendations — PlanetScale Amazon Aurora Pricing: The many surprising costs of running an Aurora database — PlanetScale Three common MySQL database design mistakes — PlanetScale OAuth applications are now available to everyone — PlanetScale Deprecating the Scaler plan — PlanetScale PlanetScale branching vs. Amazon Aurora blue/green deployments — PlanetScale Databases at scale — PlanetScale Considerations for building a database disaster recovery plan — PlanetScale Working with Geospatial Features in MySQL — PlanetScale PlanetScale vs Amazon Aurora replication — PlanetScale Introducing the Vantage and PlanetScale integration — PlanetScale MySQL isolation levels and how they work — PlanetScale Introducing the schemadiff command line tool — PlanetScale $ pscale ping — PlanetScale Announcing foreign key constraints support — PlanetScale The challenges of supporting foreign key constraints — PlanetScale What is HTAP? — PlanetScale Introducing Insights Anomalies — PlanetScale Webhook security: a hands-on guide — PlanetScale MySQL replication: Best practices and considerations — PlanetScale A guide to HTML email with Ruby on Rails and Tailwind CSS — PlanetScale Sharding for cost-effective database management — PlanetScale PlanetScale ranks 188th in Deloitte’s top 500 fastest-growing companies — PlanetScale Announcing the Fivetran integration — PlanetScale Introducing webhooks — PlanetScale What is MySQL replication and when should you use it? — PlanetScale Sync user data between Clerk and a PlanetScale MySQL database — PlanetScale Introducing database reports — PlanetScale Distributed caching systems and MySQL — PlanetScale What is MySQL partitioning? — PlanetScale MySQL High Availability: Connection handling and concurrency — PlanetScale
What is Vitess: resiliency, scalability, and performance — PlanetScale
Brian Morrison II · 2022-10-21 · via Blog — PlanetScale

Brian Morrison II |

Overview

In today’s fast-paced development landscape, building software that is fast, scalable, and that resists outages can make or break the success of any application. Containerized systems with orchestration layers like Kubernetes enable this robustness at the software level. Implementing these protections into your database, however, can often require an entire team of developers and database administrators dedicated to managing multiple database replicas with custom sharding logic to protect from outages and enable high availability.

PlanetScale is able to offer these features by using Vitess to power all of the databases on our platform. In this article, I’ll explain what Vitess is, how it works, and why you should care.

What is Vitess?

Vitess is an open-source, database clustering system for MySQL. At its core, it is a collection of systems that work together to enable MySQL to be more resilient, scalable, and performant. It was originally built by the team at YouTube in 2010 to address the increasing database scaling demands required by the platform. Today, it continues to scale massive companies like GitHub and Slack. The project is very actively maintained, with contributions from PlanetScale, Google, GitHub, Slack, Square, Stripe, and several more data-heavy companies.

Resiliency. Scalability. Performance. Read any modern database platform’s whitepapers and you’ll likely notice a lot of the same buzzwords, but let’s break down how Vitess ACTUALLY delivers these benefits.

Tip

Get a crash course in setting up, deploying, and managing Vitess in our Vitess course.

How Vitess delivers resiliency

At the heart of it, MySQL is an application just like any other. It is definitely more specialized than most others, but still has some of the same attributes. One of the best ways to increase the resiliency of any application is to add more instances of it. This way, if one goes down, the others can pick up the slack.

Vitess does this by running multiple instances of MySQL (on one or more servers) and uses a lightweight proxy, known as VTGate, to intelligently route queries to the proper MySQL instance. Vitess can also automatically detect when a MySQL instance goes offline and determine the best candidate to take its place as the primary MySQL process to serve queries for a given table.

Scalability with Vitess

Vitess allows you to scale massive MySQL databases via horizontal sharding with minimal application changes. It can split tables up across multiple MySQL instances to balance the load across multiple servers. When a query is received by the VTGate, the system will automatically determine which MySQL instances a row or set of rows lives in, will adjust the query to simultaneously grab the rows from these instances, and return the data just as if you were querying data from a single database. All of this is completely transparent to the developer — and perhaps more importantly, the user!

Improved performance with Vitess

The points made in the previous two sections alone would massively increase the performance of MySQL simply by balancing the load across multiple servers, but Vitess has a few other enhancements built in to squeeze out as much performance as possible. One of those enhancements is the way that Vitess manages connections between the different subsystems.

The various Vitess components are written with Go and internally communicate with one another over gRPC. With the concurrency features built into the Go language, Vitess is able to easily handle thousands of clients simultaneously. Every client (GUI, application, etc) that connects to a Vitess instance establishes a lightweight connection to the VTGate instead of MySQL directly. VTGate understands the MySQL protocol and performs that intelligent query routing mentioned earlier based on the current Vitess infrastructure. To avoid creating too many connections, each instance of MySQL has an associated process called the VTTablet, to which VTGate sends the query.

Vitess takes the lightweight connections established by each client to VTGate and maps them to a smaller pool of MySQL connections managed by VTTablet. This process in turn helps to avoid overloading the individual MySQL processes, resulting in lower resource utilization since only VTTablet needs to connect to the underlying MySQL process.

Vitess made easy with PlanetScale

PlanetScale prides itself in being the only MySQL-compatible database that both scales and increases developer velocity, and Vitess is at the very center of it. Every single database created through PlanetScale spins up all of this infrastructure, with all the aforementioned benefits, in mere seconds for you to start building on. The end result is that developers who build on our platform get a MySQL database that truly has the capabilities to resist outages and scale to any size, without having to worry about managing the underlying infrastructure.