Database
We track 42 posts about Database from 26 engineering blogs. Most active: William Kennedy, SeveralNines, Clickhouse. Latest post: Oct 8, 2026.
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Recent posts
How we migrated Clera’s 500 GB production database to ClickHouse Managed Postgres overnight (opens on the source site)
Clera migrated its 500 GB production database and 500+ tables to ClickHouse Managed Postgres overnight, cutting CPU usage from 100% to 10–20%.
Beyond synthetic testing: Capturing and replaying real database workloads at Airbnb (opens on the source site)
Airbnb ·
How we capture real production database traffic at Airbnb and replay it offline to load-test, plan capacity, and de-risk upgrades.By: Zuofei Wang, Erluo LiIntroductionAt Airbnb, MySQL-compatible databases are a critical backbone of our online database infrastructure: a fleet of hundreds of clusters supporting thousands of use cases at millions of queries per second (QPS). Operating databases at scale brings hard problems, including sizing clusters for future growth, keeping behavior consistent across version upgrades and migrations, and reproducing production incidents well enough to debug…
Automated Database Failover: Why Homegrown High Availability Struggles At Scale (opens on the source site)
I’ve encountered DBAs and ops engineers who shared war stories of running production long before automation and modern tooling went mainstream. Experiencing sudden primary faults during sleeping hours, getting paged, logging in to squint over trace logs that show replication lag on all replicas and working out which one is furthest ahead, promoting it, editing […] The post Automated Database Failover: Why Homegrown High Availability Struggles At Scale appeared first on Severalnines.
Amazon Aurora PostgreSQL now supports direct querying of Apache Iceberg and Parquet data in your data lake (opens on the source site)
AWS ·
Amazon Aurora PostgreSQL now lets you directly query Apache Iceberg and Parquet data stored in your data lake alongside live operational data—no ETL pipelines required. Powered by DuckDB embedded within Aurora, this capability enables single queries that join transactional and historical data using familiar PostgreSQL syntax. It supports AWS Glue Data Catalog, S3, and S3 Tables, with optimizations like predicate pushdown and caching for efficient performance.
OpenTelemetry Support from App to Database (opens on the source site)
Enable distributed tracing in the app and native Java backend, propagate request context, and export database and HTTP spans through OpenTelemetry.
Quiz: Enhance Your Flask Web Project With a Database (opens on the source site)
In this quiz, you’ll test your understanding of Enhance Your Flask Web Project With a Database. By working through this quiz, you’ll revisit how to hide secrets in environment variables, define a database schema, connect your Flask app to SQLite, and store the posts that your users submit through a form. SQLite ships with Python, so you can build the whole project without installing a database server. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]
Vercel Marketplace database browser now supports Redis (opens on the source site)
Vercel ·
You can now run Redis commands, browse keys, and inspect values directly in the Vercel dashboard using the Marketplace database browser. Both the Redis and Upstash for Redis integrations in Vercel Marketplace are supported, so you no longer need redis-cli or a separate database UI. From your project, open Infrastructure > Storage and select a Redis database. The database page now includes two new tabs: CLI: Run Redis commands and inspect the results. Run multiple commands by separating them with newlines or semicolons, with support for MULTI/EXEC transactions. Browser: Browse keys in a flat…
Implementing GitOps from Infrastructure to DB Operators to Unify Ops for Kubernetes Databases (opens on the source site)
Most platform teams already use GitOps for their Kubernetes apps. The config lives in Git, Argo CD applies it, and deployments are predictable. But look one layer down and things get messy. Infrastructure setup is often still done by hand, running Terraform locally or through scattered scripts. Database operations, especially for databases running through Kubernetes […] The post Implementing GitOps from Infrastructure to DB Operators to Unify Ops for Kubernetes Databases appeared first on Severalnines.
Eloquent Performance and Database Design: Evidence Before Eager Loading (opens on the source site)
A deep dive into Eloquent performance, from detecting N+1 queries to choosing aggregates, indexes, query plans, pagination, chunking, and transaction boundaries for a growing team dashboard. Read more
v0 adds one-click integrations for email, auth, search, and databases (opens on the source site)
Vercel ·
We're working toward bringing parity across Vercel integration and v0, starting with Resend, Amazon OpenSearch, MongoDB Atlas, Algolia and Clerk. Prompt v0 with what you want to build, and when your prompt requires a provider, v0 renders a connect card in the chat. With Marketplace integrations in v0, you get: Connect as you build: Prompt v0 with what you want to create, and connect the required provider inline. Automatic setup: Once connected, v0 handles the required environment variables and configuration. Provider skills loaded automatically: When you connect a provider that publishes…
Dual-Read Cache Consistency for Live Database Migrations (opens on the source site)
Decoupling databases during monolith-to-microservice refactoring risks dirty reads, stale cache hits, and silent data drift across data stores during live cutovers. Continue reading Dual-Read Cache Consistency for Live Database Migrations on SitePoint.
Is It Safe to Give an AI Agent Access to Your Production Database? (opens on the source site)
Giving an AI agent unrestricted database access isn't safe. Scoped, read-only, database-enforced access can be. See the framework and guardrails.
Why a 40-Year-Old Database Is Still Winning in the AI Era (opens on the source site)
Why Postgres wins in the AI era: 40 years of reliability, extensibility, and an open ecosystem. Agents need trusted foundations, not fragile architectures.
Data Mesh at Grab (Part III): Operationalizing data reliability with automated DPIs (opens on the source site)
Grab ·
Introduction In the first two parts of this series, we described how Grab approaches data mesh through the Signals Marketplace: a way for teams to publish, discover, and reuse trusted data products across domains. Part II introduced the foundational tools behind certification: Hubble for metadata and ownership, Genchi for data quality observability, and the Data Contract Registry for explicit producer-consumer guarantees. Certification is the starting point for a trusted data marketplace. It gives downstream consumers confidence in an asset’s ownership, documentation, lineage, and quality…
What else runs on your Postgres server, and how do we stop it from taking the database down? (opens on the source site)
ClickHouse Managed Postgres uses runtime budgets, cgroup limits, and disk-full session exemptions to keep supporting services from compromising database availability.
Building safe MCP servers for your PostgreSQL database (opens on the source site)
Model Context Protocol (MCP) is an open protocol that describes how agents can connect to external tools and data sources, and is now widely supported by the most popular coding agents (like GitHub Copilot, Claude Code, and Codex) and agent frameworks (like LangChain and Pydantic AI). If you want to give agents a standard way to access the data in a database, you can build your own MCP server and expose tools for the agent to query or even modify data. But you need to design your MCP server carefully, to ensure that agents can do everything that users want - but nothing that you don't want…
Building safe MCP servers for your PostgreSQL database (opens on the source site)
Model Context Protocol (MCP) is an open protocol that describes how agents can connect to external tools and data sources, and is now widely supported by the most popular coding agents (like GitHub Copilot, Claude Code, and Codex) and agent frameworks (like LangChain and Pydantic AI). If you want to give agents a standard way to access the data in a database, you can build your own MCP server and expose tools for the agent to query or even modify data. But you need to design your MCP server carefully, to ensure that agents can do everything that users want - but nothing that you don't want…
How to Install and Configure phpMyAdmin on Debian 13 (opens on the source site)
MySQL/MariaDB is one of the most popular database servers and is widely used by developers worldwide to build websites, both ... Read More The post How to Install and Configure phpMyAdmin on Debian 13 appeared first on RoseHosting.
Amazon DynamoDB now supports real-time vector search at any scale (opens on the source site)
AWS ·
DynamoDB now supports native vector search with single-digit millisecond latency at 99%+ recall. It is designed for any scale, even trillions of vectors and requires zero infrastructure management.
Scaling Grab's Data Lake: Our journey to Apache Iceberg adoption (opens on the source site)
Grab ·
Introduction: The evolution of Grab’s Data Lake At Grab’s scale, managing petabytes of data across billions of S3 objects demands more than a storage layer. It demands a robust architectural primitive that supports the high-concurrency needs of a modern “Lakehouse.” Our goal is full storage-compute separation, leveraging S3 as an elastic foundation for both near-real-time metrics and large-scale batch transformations. For years, the vast majority of our tables were Hive Parquet, managed through the Hive Metastore with a directory-based layout. This model served us well, but as data volume…
Unpacking a database of unknown type (opens on the source site)
There is a Rutracker (Russian torrent website) non-official dump that came as a file of some unknown database type. (There was also a GUI database viewer for Windows, which I won't run.) Let's see if I can unpack these files by myself. The file extension is meaningless '.RDB'. The file is compressed and/or encrypted: % ent tmp.bin Entropy = 7.990625 bits per byte. The header is: % xxd -g1 tmp.bin | head 00000000: da 36 32 31 33 37 33 34 da 37 7a bc af 27 1c 00 .6213734.7z..'.. 00000010: 03 ff e9 09 be 60 14 00 00 00 00 00 00 3e 00 00 .....`.......>.. 00000020: 00 00 00 00 00 fb 89 7a 12 00 3c…
How to Make Trading Infrastructure Audit-Ready Across SSH, Kubernetes, Databases, and RDP (opens on the source site)
Teleport ·
Learn how SSH, Kubernetes, database, and RDP create audit challenges for high-frequency and quant trading firms and how to unify audit trails across protocols.
AI-Assisted Production Database Ops with ClusterControl MCP and CCX MCP (opens on the source site)
In December, we introduced how Model Context Protocol could make ClusterControl easier to work with from AI assistants. Since then, Severalnines has expanded that MCP direction across its database operations platforms with ClusterControl MCP and CCX MCP. The latest ClusterControl MCP is the major update, providing a more robust implementation with 69 tools and 20 […] The post AI-Assisted Production Database Ops with ClusterControl MCP and CCX MCP appeared first on Severalnines.
Migrating Etsy’s database sharding to Vitess (opens on the source site)
Etsy ·
Etsy has maintained a sharded MySQL architecture since around 2010. This database cluster contains most of Etsy’s online data and is made up of ~1,000 tables distributed across ~1,000 shards. Over the last 16 years, it has grown significantly: combined, these tables have over 425 TB of data and receive roughly 1.7 million requests per second. Etsy engineers access our MySQL data through a proprietary object-relational mapping (ORM). The ORM has a corresponding model for each MySQL table. When a table is sharded, its rows are partitioned among different databases known as shards. Each shard…
Migrating Etsy’s database sharding to Vitess (opens on the source site)
Etsy ·
Etsy has maintained a sharded MySQL architecture since around 2010. This database cluster contains most of Etsy’s online data and is made up of ~1,000 tables distributed across ~1,000 shards. Over the last 16 years, it has grown significantly: combined, these tables have over 425 TB of data and receive roughly 1.7 million requests per second. Etsy engineers access our MySQL data through a proprietary object-relational mapping (ORM). The ORM has a corresponding model for each MySQL table. When a table is sharded, its rows are partitioned among different databases known as shards. Each shard…
Query Database Using Plain English (opens on the source site)
Introduction In this post you’ll see how you can create a system that allows users to query a relational database using plain English. This allows users not familiar with SQL or business intelligence systems to get insights from data. Setting Up If you want to follow along, you’ll need to clone the code from the GitHub repo. This will download the code, and the database file containing the data (bikes.ddb) Note: The data is from the Austin Bike Share dataset.
Engineering stories behind the Medium Daily Digest Algorithm: Part 2 (opens on the source site)
Medium ·
How we made our filtering 10x cheaper by removing our Bloom FiltersBloom Filters are great tools to make fast and cheap filtering. They also come with plenty of problems and can easily get expensive and cumbersome. We switched to user-based direct database queries, which made our filtering cheaper and easy to maintain. Here’s the full breakdown of that migration.Intro: This is a 4-part series breaking down improvements to the algorithm behind the Medium’s Daily Digest over the past year. When we started this work, the Digest was suboptimal — and since it’s a huge distribution surface,…
Engineering stories behind the Medium Daily Digest Algorithm: Part 4 (opens on the source site)
Medium ·
Cross-Digest diversificationIn this part 4, we’ll see how we went from investigating a few complaints from digest power users to improving our digest recommendations across the board.Intro: This is a 4-part series breaking down improvements to the algorithm behind the Medium’s Daily Digest over the past year. When we started this work, the Digest was suboptimal — and since it’s a huge distribution surface, reaching millions of readers every day, we started working on incremental improvements.By the end of these projects, the digest was 10% more likely to convert users to paying members, less…
How We Migrated Millions of UGC Records to Aurora MySQL (opens on the source site)
Discover how Bazaarvoice migrated millions of UGC records from RDS MySQL to AWS Aurora – at scale and with minimal user impact. Learn about the technical challenges, strategies, and outcomes that enabled this ambitious transformation in reliability, performance, and cost efficiency Bazaarvoice ingests and serves millions of user-generated content (UGC) items—reviews, ratings, questions, answers, and […]
Optimistic Locking Overview (opens on the source site)
Optimistic locking is a concurrency control mechanism where we assume that multiple transactions can safely access data without conflict, allowing them to proceed without locking the data upfront. Unlike pessimistic locking, where resources are locked to avoid conflicts, optimistic locking allows transactions to proceed without locks and checks for conflicts only when updating the data. If a conflict is detected (e.g., another transaction has already modified the data), the operation fails, and you can retry it.When Should You Use Optimistic Locking?Optimistic locking is ideal for use in…
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