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 $ 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 Personalizing your onboarding with Markdoc — PlanetScale
Introducing the schemadiff command line tool — PlanetScale
Shlomi Noach · 2023-12-18 · via Blog — PlanetScale

Shlomi Noach |

One of the core benefits of PlanetScale database clusters is the workflow enabling zero downtime schema migrations, which are made possible by database branching.

A branch in PlanetScale is technically its own independent MySQL cluster. Deploy requests are used to apply the schema changes from one branch to another. As part of this workflow, we heavily utilize the schemadiff command provided by Vitess to calculate the schema differences between branches, determine the order of changes (including the potential for migration concurrency), and participate in three-way merge logic to apply changes to an upstream database branch.

Today we are releasing the schemadiff command line tool, a thin wrapper around Vitess's schemadiff library.

Getting started with schemadiff

The schemadiff command line tool makes it easy to validate, normalize, and diff schemas, loaded from the standard input, the file system, or from your MySQL database.

To try out schemadiff yourself, check out the schemadiff repository's releases page for the latest binaries available for Linux and Mac. The README also contains additional information on how to use the tool, when you might want to use it, and how to build it from source if you want to target another platform.

Let's take a look at a few use cases.

Describe a database

The load can be used with a source to show what's your existing database:

schemadiff load --source 'myuser:mypass@tcp(127.0.0.1:3306)/test'
-- Output:
CREATE TABLE `t` (
	`id` int,
	PRIMARY KEY (`id`)
);
CREATE TABLE `t2` (
	`id` int,
	`name` varchar(128) NOT NULL DEFAULT '',
	PRIMARY KEY (`id`)
);

It can also accept a string to prettify and normalize it, which can make a schema much easier to read:

echo "create table t (id int(11) unsigned primary key)" | schemadiff load
-- Output:
CREATE TABLE `t` (
	`id` int unsigned,
	PRIMARY KEY (`id`)
);

Attempting to load an incorrectly formatted SQL file will result in an error. This can be extremely useful as part of a CI/CD pipeline to validate change scripts before they are applied to your database:

schemadiff load --source mydb.sql > /dev/null || echo "FAIL"

Showing changes

The diff command can compare two schemas and write the necessary DDL to execute to get them in sync. This is how we generate our change statements when merging branches in PlanetScale.

schemadiff diff --source 'myuser:mypass@tcp(127.0.0.1:3306)/test' --target /path/to/repo/source/code/schema
DROP VIEW `v`;
ALTER TABLE `t` MODIFY COLUMN `id` bigint;
CREATE TABLE `t2` (
	`id` int,
	`name` varchar(128) NOT NULL DEFAULT '',
	PRIMARY KEY (`id`)
);

For more use cases, be sure to review the README for this project. The schemadiff command line tool supports MySQL 8 syntax and is released under Apache 2.0 license.

We hope you find it useful!