Introducing Log Drains
Log Drains for exporting product logs is now available under Public Alpha
August 15, 2024
pg_graphql 1.5.7: pagination and multi-tenancy support
Latest features of pg_graphql
August 14, 2024
Snaplet is now open source
Snaplet is closing their business and opening their source code
Supabase Auth: Bring-your-own Auth0, Cognito, or Firebase
Use Firebase Auth, Auth0 or AWS Cognito (Amplify) with your Supabase project, secure your users with SMS based MFA, and use send hooks.
Tracking index usage with Insights
Learn about the new PlanetScale Insights index tracking feature.
August 13, 2024
Tinybird vs. ClickHouse®️: What's the difference?
Tinybird is a real-time data platform for user-facing analytics, built using ClickHouse. Here are the differences between Tinybird and other ClickHouse solutions, including self-hosted and managed.
Supabase Realtime: Broadcast and Presence Authorization
Secure Realtime Broadcast and Presence with Authorization
Zero downtime migrations at petabyte scale
Data migrations are a critical part of the database lifecycle, and are sometimes necessary for version upgrades, sharding, or moving to a new platform. In many cases, migrations are painful and error-prone. In this article, we walk through how migrations are performed at PlanetScale, and offer advice on how to improve the migration experience.
Can You Do Both: Fast Scans and Fast Writes in a Single System?
Can You Do Both: Fast Scans and Fast Writes in a Single System?
Have you ever wondered why most existing database systems focus solely on either analytical or transactional performance? The data orientation within the file format and the internals of the storage engine are key reasons for this specialization. Current database systems are unable to balance transactional and analytical processing, and therefore, are forced to optimize for just one of both workload types. Transaction-focused OLTP systems use row-based storage formats for quick updates and lookups, but these formats are inefficient for analytics-focused OLAP tasks. OLAP workloads require scanning many rows while typically accessing only a few columns, and row-based formats are simply not designed to handle this. OLAP systems use compressed columnar formats for fast scans, which make updates complex and slow, often lacking efficient point lookup capabilities. For instance, a simple table scan using a column store can be more than 5x faster than storing data in a row-based format due to the data movement characteristics of the format.
Database Startups
August 12, 2024
Official Supabase extension for VS Code and GitHub Copilot
Today we're launching a new GitHub Copilot extension for VS Code to make your development with Supabase even more delightful.
postgres.new: In-browser Postgres with an AI interface
Introducing postgres.new, the in-browser Postgres sandbox with AI assistance.
July 31, 2024
Data Replication Design Spectrum
Consistent replication algorithms can be placed on a sliding scale based on how they handle replica failures. Across the three common points on this spectrum, the resource efficiency, availability, and latency are compared, providing guidance for how to choose an appropriate replication algorithm for a use case.
July 30, 2024
Faster backups with sharding
Sharding a database comes with many benefits: Scalability, failure isolation, write throughput, and more. However, one of the lesser-known benefits comes from improved backups and restore performance.
Delightful, production-grade replication for Postgres
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July 29, 2024
Building data pipelines with Vitess
Learn the basics of Change Data Capture (CDC) and how to leverage Vitess VStream API to build data pipelines.
Import Postgres tables into Tinybird with the PostgreSQL Table Function
Today we announce the PostgreSQL Table Function, a powerful and flexible way to import and sync data from Postgres to Tinybird with just a few lines of SQL.
A Deep Dive into German Strings
A Deep Dive into German Strings
“Strings are Everywhere”! At least according to a 2018 DBTest Paper from the Hyper team at Tableau. In fact, strings make up nearly half of the data processed at Tableau. This high prevalence undoubtedly applies to many other companies as well, as the paper’s dataset consists of data analyzed by Tableau’s users. The string-heavy nature of the data makes string processing one of the most important tasks of a database system.
Building Data Pipelines With Vitess
Vitess is a popular CNCF project that is used to scale some of the largest MySQL installations in the world — by companies like Slack, Square, Shopify, and GitHub. It provides sharding, connection pooling, and many other features that make it easy to scale MySQL horizontally.
Vitess and MySQL are ideally suited for use as an Online Transaction Processing (OLTP) system — where the end-user interacts directly with the system and fast response times are essential as they get product and service information, generating critical business records such as orders, user profiles, and more.
