I built an embedded, pure‑Java database for time‑series and dimensional analytics. It combines a columnar, block‑encoded local store (on top of Berkeley DB Java Edition) with a read API that can also target external SQL systems via JDBC using auto‑generated queries.

While powerful time-series databases like ClickHouse or InfluxDB exist, they are often heavyweight servers. I wanted something more like DuckDB but for the Java ecosystem: a library I could drop into any application to get fast, local analytical queries without managing a separate service.

Dimension-DB is designed for this niche. You can use it in embedded applications, IoT edge devices, or as a high-performance cache within a larger backend.

Key features that make it different:

- Embeddable Core: It's a library, not a server. It uses Berkeley DB JE as its local storage backend, making it trivial to integrate into any Java project with zero operational overhead.

- Adaptive Columnar Storage: It doesn't just store data in columns. It automatically analyzes data blocks and applies the best storage format (RAW, dictionary-encoded ENUM, or run-length-encoded HISTOGRAM) on-the-fly. This massively improves compression and query speed, especially for low-cardinality metrics.

- Hybrid Query Mode: Beyond local storage, it can act as a "read-only" query accelerator for existing databases. You can point it at a PostgreSQL, Oracle, or ClickHouse table, and it will auto-generate efficient SQL for time-series queries, acting as a smart analytical layer.

- Proven Performance: I've benchmarked it against the public NYC Taxi dataset (78 million rows). It achieves a sustained ingest rate of ~55,000 rows/sec and provides significant query acceleration, especially for complex aggregations.

This isn't just a side project; it's the core storage engine that powers Dimension-UI, the desktop monitoring tool I built. I needed a fast, local database to handle real-time visualization and analysis, and existing solutions were either too slow or too complex.

The project is open source and I'm actively looking for feedback. Let me know what you think!

Links:

- Dimension-DB (GitHub & Benchmarks): https://github.com/akardapolov/dimension-db

- Dimension-UI (The desktop app that uses Dimension-DB): https://github.com/akardapolov/dimension-ui

- Dimension-DI (The DI container used in Dimension-UI): https://github.com/akardapolov/dimension-di

Build a ClojureScript native desktop app in 5 minutes [video]

GPS reveals that football practices can be up to 40% more demanding than games

Ready for Post-Quantum Cryptography TLS?

A simple commitment, on how to dogfood your design practice

Cognitive warfare: the new battlefield exploiting our brains

Show HN: I made a better DOM morphing algorithm

Garbage Collection Is a Hack

Show HN: OSS implementation of Test Time Diffusion that runs on a 24gb GPU

External Secrets Operator is now GA with version v1.0.0

Show HN: Dimension-DB – Time‑series/columnar DB for Java (local store and JDBC)

Sharing my Clean Architecture boilerplate I'll be using in 2026

IKEA's Big Smart Home Push Arrives with 21 New Matter Devices

Unexpected drug interaction: GLP-1 medications and sedative duration

Disk Invaders: A Retro Game Inspired by Diskless Kafka

Use the "Bloom Test" Before Making Anki Cards

Canonical's Secure Starlark in Go

When did people favor composition over inheritance?

Show HN: My AI Hub App

Block spent –$68M on an event for employees last quarter

BackMark: Markdown-Native Task Management for AI-Powered Vibe Coding

AI/ML and Robotics with RPI and FPGA

N8N automation helped me make $1000

How Browsers Work

Cheaper: Producing a program with less developer time

Don't blame AI for your job woes

PostgreSQL Is Enough

Texture.io Acquired by IBM

History and culture of substituted amphetamines in Mathematics

Steven Heller's Font of the Month: Archive Matrix

For 30 years, Garfield phones kept washing up on a French beach (2019)

Show HN: I built a platform to solve the cold start problem for content creators