31
ClickHouse gets lazier and faster: Introducing lazy materialization
ClickHouse learned to procrastinate strategically. Discover how lazy materialization skips unnecessary column reads to accelerate queries.
32
Download responsibly
This month we’ve ramped up the infrastructure behind the download server, and downloads should now be available earlier and faster. There’s also a small technical change in that requests for a “…latest” file will now be answered with a HTTP redirect to the specific latest version (see previous blog post).
33
Don't bother parsing: Just use images for RAG
If search is the game, looks matter
34
PlanetScale for Postgres is now GA
PlanetScale for Postgres is now generally available.
35
36
TigerBeetle is a most interesting database
This read goes behind the scenes of how TigerBeetle came to be, the incredibly novel software they’ve built, and all of the wacky, wonderful things that make them so special.
37
Exploring PostgreSQL 18's new UUIDv7 support
Exploring what's interesting about UUIDv7 support using a demo crab store.
38
Will Amazon S3 Vectors kill vector databases or save them?
AWS S3 Vectors aims for 90% cost savings for vector storage. But will it kill vectordbs like Milvus? A deep dive into costs, limits, and the future of tiered storage.
39
How we replaced Elasticsearch and MongoDB with Rust and RocksDB
Radar offers geocoding, place search, and fraud detection APIs, powered by HorizonDB, a Rust-based engine delivering 20k QPS and sub-millisecond latency.
40
Are hard drives getting better?
After 13 years, the data is clear—the bathtub curve does not apply to hard drives. And, more importantly, hard drives are failing less and lasting longer.
41
Understanding Smallpond and 3FS
A concise breakdown of what smallpond and 3FS are, how to use them, and if they're suitable for your data processing needs.
42
We cut our Mongo DB costs by 90% by moving to Hetzner
We moved MongoDB Atlas off AWS and onto self-hosted Hetzner boxes — 90% cost cut, no performance loss. Here's the setup, the Ansible, and what broke.
43
44
Polars Cloud: The Distributed Cloud Architecture to Run Polars Anywhere
We introduce Polars Cloud, the distributed cloud architecture to scale and run Polars anywhere.
45
TernFS – An exabyte scale, multi-region distributed filesystem
XTX Markets is a leading algorithmic trading firm which uses state-of-the-art machine learning technology to produce price forecasts for over 50,000 financial instruments across equities, fixed income, currencies, commodities and crypto. It uses those forecasts to trade on exchanges and alternative trading venues, and…
46
Neki – Sharded Postgres by the team behind Vitess
Sharded Postgres by the team behind Vitess
47
Valkey Turns One: Community fork of Redis
Valkey is not only thriving, but now outperforming Redis 8.0 in real world benchmarks.
48
A major Postgres upgrade with zero downtime
Right before Christmas we discovered that our Aurora Postgres instance needed a major version upgrade. This covers how we made the upgrade with zero seconds of downtime.
49
We made Postgres writes faster, but it broke replication
Exploring LSM tree implementation in PostgreSQL for better write performance.
50
Colossus for Rapid Storage
Colossus’s performance comes from its stateful protocol, which plays a pivotal role in the new Rapid Storage offering launched at Google Cloud Next '25.
51
Migrating to Postgres
Since early 2022, Motion was on CockroachDB. Cockroach has many qualities going for it: effortless horizontal scaling, especially when…
52
Jepsen: TigerBeetle 0.16.11
TigerBeetle is an Online Transactional Processing (OLTP) database built for double-entry accounting with a strong emphasis on safety and speed. It builds on the Viewstamped Replication (VR) consensus protocol to offer Strong Serializable consistency. Unlike general-purpose databases, TigerBeetle stores only accounts…
53
A race condition in Aurora RDS
See how we diagnosed and confirmed an AWS Aurora RDS race condition impacting failovers.
54
Use DuckDB-WASM to query TB of data in browser
The Library Innovation Lab is growing knowledge and community by bringing library principles to technological frontiers.
55
PlanetScale Offering $5 Databases
Introducing the $5 PlanetScale plan.
56
Which table format do LLMs understand best?
We benchmarked 11 data formats, including markdown tables, JSON, CSV, YAML, XML, and more, to see which LLMs understand best. We found surprising accuracy and token cost trade-offs.
57
Every System is a Log: Avoiding coordination in distributed applications
Distributed coordination makes application complex and brittle. Because all systems eventually build on logs, we can use a shared-log approach to eliminate most coordination. We discuss this conceptually and show how this is practically implemented in the open source project Restate.
58
Super fast aggregations in PostgreSQL 19
See how PostgreSQL 19 speeds up aggregations by aggregating before joins, with a benchmark showing over 5x faster execution.
59
Optimizing ClickHouse for Intel's ultra-high core count processors
Intel's latest processor generations are pushing the number of cores in a server to unprecedented levels. For analytical databases like ClickHouse, ultra-high core counts represent a huge opportunity and a complex challenge.
60
A sharded DuckDB on 63 nodes runs 1T row aggregation challenge in 5 sec
What happens when you give a distributed SQL engine a trillion-row dataset? GizmoEdge crushed the Coiled challenge with a 1,000-worker cluster on Azure.