61
Just use Postgres
Advice: When you are making a new application that requires persistent storage of data, like is the case for most web applications, your default choice should be Postgres .
62
Are we at peak vector database?
Seriously - why do we need all these vector databases? Do we need dozens of them?
63
Redis is forked
Where do we go from here?
64
Postgres as queue
The team I've been working in for the last year or so have had great success using Postgres-as-queue. We've managed to avoid the following:
65
Reads Causing Writes in Postgres
It is good practice to regularly inspect the statements running in the hot path of your Postgres instance. One way to do this is to examine the pg_stat_statements view , which shows various statistics about the SQL statements executed by the Postgres server.
66
Sensible SQLite Defaults
SQLite is cool now. DHH uses it, Laravel defaults to it. Here is a list of _sensible defaults_ when using sqlite.
67
CRDTs Turned Inside Out
🔰If CRDTs are new to you, here are some introductory links. - An interactive intro to CRDTs [https://jakelazaroff.com/words/an-interactive-intro-to-crdts/] - An introduction to state-based CRDTs [https://www.bartoszsypytkowski.com/the-state-of-a-state-based-crdts/] - CRDTs for non-academics…
68
SQL is syntactic sugar for relational algebra
This idea is particularly sticky because it was more or less true 50 years ago, and it's a passable mental model to use when learning sql. But it's an inadequate mental model for building new sql frontends, designing new query languages, or writing tools likes ORMs that abstract over sql.
69
Optimizing Ruby's JSON, Part 4
In the previous post, we established that as long as ruby/json wasn’t competitive on micro-benchmarks, public perception wouldn’t change. Since what made ruby/json appear so bad on micro-benchmarks was its setup cost, we had to find ways to reduce it further.
70
The CAP theorem of Clustering: Why Every Algorithm Must Sacrifice Something
No clustering algorithm is perfect and you must make a trade-off.
71
Indexing iCloud Photos with AI Using LLaVA and Pgvector
A straightforward idea, gluing stuff together until it works, but it’s a glimpse of what’s possible with help of local AIs in near future
72
The new PostgreSQL 17 make dist
When the PostgreSQL project makes a release, the primary artifact of that is the publication of a source code tarball. That represents the output of all the work that went into the creation of the PostgreSQL software up to the point of the release. The source tarball is then used downstream by packagers to make binary…
73
Optimizing Large-Scale OpenStreetMap Data with SQLite
The personal website of JT Archie. Includes a blog, work ethic, and projects they have worked on.
74
Optimizing Postgres table layout for maximum efficiency
Introduction When modeling a Postgres database, you probably don’t give much thought to the order of columns in your tables. After all, it seems like the kind of thing that wouldn’t affect storage or performance. But what if I told you that simply reordering your columns could reduce the size of your tables and…
75
Building rqlite 9.0: Cutting disk usage by half
rqlite is a lightweight, open-source, distributed relational database written in Go. It is built on the Raft consensus protocol and uses SQLite as its storage engine. Development of 9.0 has begun and aims to reduce disk usage by approximately 50%. This goal will be achieved through a high-level design overhaul…
76
What I talk about when I talk about query optimizer (part 1): IR design
Achieving Data Freedom Through Open Source and Rust
77
Phrase matching in Marginalia Search
Marginalia Search now properly supports phrase matching. This not only permits a more robust implementation of quoted search queries, but also helps promote results where the search terms occur in the document exactly in the same order as they do in the query. This is a write-up about implementing this change. This is…
78
The notifier pattern for applications that use Postgres
Maximizing Postgres connection economy by using a single connection per program to receive and distribute all listen/notify notifications.
79
Data will not tell you what to do
How disappointing it is to learn that the best ideas rarely come from data after 15 years of working in the field.
80
Using PostgreSQL for military geoanalytics tasks
My name is Taras Kloba, Chief Partner Architect at Microsoft. This blog captures my journey through data engineering, certifications, and technology insights.
81
Python datetime pitfalls, and what libraries are (not) doing about it
It’s no secret that the Python datetime library has its quirks. Not only are there probably more than you think; third-party libraries don’t address most of them! I created a new library to explore what a better datetime library could look like.
82
Django and Postgres for the Busy Rails Developer
About 10 years ago I wrote a post PostgreSQL for the Busy MySQL Developer, as part of switching from MySQL to Postgres for my personal and professional projects wherever I could.
83
Exact binary vector search for RAG in 100 lines of Julia
Thanks for HN users mik1998 and borodi who brought up the popcnt instruction and the count_ones function in Julia which carries this out, I've updated the timings and it's even faster now.
84
Build your own SQLite, Part 1: Listing tables
As developers, we use databases all the time. But how do they work? In this series, we'll try to answer that question by building our own SQLite-compatible…
85
Database mocks are not worth it
Testing against a real database uncovers hidden pitfalls that can appear as the application matures
86
Sampling with SQL
Sampling is one of the most powerful tools you can wield to extract meaning from large datasets. It lets you reduce a massive pile of data into a small yet representative dataset that’s fast and easy to use.
87
Systematically Improving Your RAG
Discover systematic strategies to enhance your Retrieval-Augmented Generation (RAG) systems for better performance and user experience.
88
Data Science at the Command Line, 2nd Edition (2021)
This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens…
89
Reactive Relational Algebra
databases plus wibbly wobbly timey wimey stuff
90
The super effectiveness of Pokémon embeddings using only raw JSON and images
Embeddings encourage engineers to go full YOLO because it’s actually rewarding to do so!