cartero Wednesday, August 12, 2026 · No. 25943
Embedded Systems

86Box v6.0

Cloudflare

Avian Visitors

Go

I built a toy relational database in Go

Hey everyone, I’ve been building a small relational database in Go called fernDB. It supports a minimal subset of SQL for now, with basic parsing, planning, execution, transactions, indexes, simple statistics, and a cost-based optimizer. Its built on top of frostfire, a transactional key-value engine I built for the storage side of the database. Frostfire handles the lower-level KV storage and transactions, while fernDB is the relational layer on top. The whole thing started because I thou...

PostgreSQL

Christophe Pettus: All Your GUCs in a Row: config_file

PostgreSQL's `config_file` parameter creates a bootstrap paradox: it tells the server where to find its configuration, but lives on the command line only—never…

PostgreSQL

Looking for advice on how to contribute to growing open source database engines

Hi i am a career dev with around 5 years of experience across different transactional and data platform. Looking for advice on how to and where to start contributing on open source growing database engines. I have some understanding of database internals since I had to optimize applications for better perf both oltp and olap. I checked out the famous repos like clickhouse, pinot but there it seems most of the issues are already assigned, pr is ready or very old. submitted by /u/CalmCon...

NewSQL

AstralDB: <3MB general-purpose RDBMS with no external dependencies

I've been working on AstralDB: an RDBMS with broad SQL syntax coverage (DDL/DML, CTEs, OLAP and OLTP, multiple data formats, geospatial, graphs, vectors, etc...), asynchronous I/O, bytecode VM, WAL, encryption, compression, and a MathSci module for signal processing, machine learning, NLP, autograd, and solving differential equations all in a single static <3MB binary. The main purpose of it is to break assumptions about traditional relational database synchronous I/O and query planning that ...

Graphics and Rendering

Voxel Space

MySQL

Migrating Etsy’s database sharding to Vitess

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. W...

Artificial Intelligence

Making Ads Count: Using MMoE and Auxiliary Tasks to Better Connect Buyers & Sellers

When buyers search on Etsy, they need to quickly and easily find the perfect item. At the same time, sellers need to be confident their unique products are being seen by the right customers. Our Ads Search ranking model, which is built on a multitask learning foundation, is the critical link in this connection. Recently, we identified an opportunity to drive more meaningful buyer engagement by enhancing our model’s ability to predict purchase intent. We achieved this via a dual-pronged impr...

RAG

Shaping Product Understanding with Contrastive Reinforcement Learning

Etsy’s marketplace is defined by the creativity and craftsmanship of our sellers and the hundreds of millions of highly diverse products they offer. You can find silversmiths who cold-forge recycled sterling silver, weavers who dye raw fleece with indigo and black walnut, and ceramicists who throw stoneware on a kick wheel. These details define each product and often determine whether it matches a buyer’s taste, style, and interests. Sometimes buyers know exactly what they want, searching...

Time-Series Databases

NanoTDB - Single-binary observability, time-series database, with built-in dashboard.

I have been working on a project called NanoTDB, designed specifically for environments where resources are constrained. I wanted a database solution that is small, simple to deploy, and maintains a minimal memory and CPU footprint, particularly for performance efficiency when running on SD storage. NanoTDB addresses these needs by offering: Key Features: Lightweight Architecture: Optimized for low-power hardware and minimal overhead. Embedded Web Dashboard: Provides real-time visibility int...