chDB

[chDB @chdb_io]

https://t.co/HvzSzJ2L97

[chDB @chdb_io]

@skiabox Yeah — Memory64 is what lets the wasm heap go past 4GB, so it's a hard prereq. Chrome 133 is out of beta now, and if you can't upgrade the browser, the Node/Python packages run the exact same engine without the Memory64 gate.

[chDB @chdb_io]

Introducing chDB-Wasm 🎉
A complete ClickHouse OLAP engine, compiled to WebAssembly, running fully client-side. No server. No install. Just open a tab and run SQL.
Try it now: https://t.co/bkC8T7nPk1

[chDB @chdb_io]

New in chdb-wasm on main: the browser SQL shell now matches clickhouse-client.

PrettyCompact rendering, ClickHouse SQL syntax highlighting, a live progress bar, and Ctrl+C to cancel a running query.

https://t.co/WZIkhji0Iw

PrettyCompact rendering, ClickHouse SQL syntax highlighting, a live progress bar, and Ctrl+C to cancel a running query.

[chDB @chdb_io]

@ppkatariya SQL surface is 1:1 with server — same ClickHouse dialect, MergeTree family, all analytical functions. What drops out is anything that needs the host: distributed cluster / Keeper / replication, plus remote reads are CORS-scoped since the browser does the fetch.

[chDB @chdb_io]

@d0r1an_polygala chDB is a launch partner for AWS Lambda microVMs.

[chDB @chdb_io]

Wiring an MCP server for "query any data anywhere"? chDB reads 70+ formats from one Python import: Parquet on S3, Iceberg, MySQL, Postgres, Arrow, JSON, CSV, ORC. No sidecar. No ETL. The agent picks a URI and runs SQL.

[chDB @chdb_io]

New in chdb-node on main: a hypequery adapter.

Run hypequery's type-safe TypeScript query builder against in-process chDB, no ClickHouse server needed. Wire it up with one adapter call.

https://t.co/OHL3rC4bnn

[chDB @chdb_io]

New in chdb-node on main: Vercel AI SDK and Mastra tool adapters.

Agents can list tables, describe s3/file/postgres sources, and run read-only SQL on embedded chDB. Ships with a Mastra HNSW vector store too.

https://t.co/w5YZGsHKXn

[auxten @auxten]

Everyone said this was impossible.
ClickHouse is a massive multi-threaded engine. Porting it to WebAssembly — a single-threaded runtime — was supposed to be a non-starter. Every AI we asked told us the same thing: can't be done.
But years of working on this engine gave me a different intuition. From first principles, there was no fundamental barrier. Just a hard problem that everyone had assumed away.
So we pointed AI at it — refactor, regress, repeat — instead of taking "impossible" for an answer.

The result: https://t.co/OgmJ09qJpC
A full ClickHouse OLAP engine, running entirely in your browser tab. No server. Query local files and S3 Parquet in-process.
Huge thanks to @wudidapaopao for the relentless work that made it real.

[chDB @chdb_io]

Under the hood:
• Full ClickHouse SQL
• Reads Parquet, CSV, JSON directly
• Query remote sources (S3, URLs) from the browser
• Runs entirely in WebAssembly, no backend

[chDB @chdb_io]

New in chdb-wasm on main: the browser SQL shell now matches clickhouse-client.

PrettyCompact rendering, ClickHouse SQL syntax highlighting, a live progress bar, and Ctrl+C to cancel a running query.

https://t.co/WZIkhji0Iw

PrettyCompact rendering, ClickHouse SQL syntax highlighting, a live progress bar, and Ctrl+C to cancel a running query.

[ClickHouse @ClickHouseDB]

Part three of our LogHouse series: 19 PiB -> 431 PiB across 1.59 quadrillion rows.

https://t.co/opJQcGl441

Two years ago, LogHouse stored 19 PiB.

Today it stores 431 PiB across 1.59 quadrillion rows, ingesting peaks of 80 GiB/s and 190 million rows/sec.

[chDB @chdb_io]

chDB as agent memory: 500k rows/s INSERT on a laptop, sparse-index per-user time slices via ORDER BY (user_id, ts), and ~10x compression on chat text with LowCardinality(role) + ZSTD. Memory layer that scales with the engine, not a separate service.

- 500k rows/s sustained INSERT on a laptop
- ORDER BY (user_id, ts): per-user time slice skips most parts
- LowCardinality(role) + ZSTD: ~10x compression on chat text

- 500k rows/s sustained INSERT on a laptop
- ORDER BY (user_id, ts): per-user time slice skips most parts
- LowCardinality(role) + ZSTD: ~10x compression on chat text

ine, not a separate service.Agent memory in chDB, single process:

- 500k rows/s sustained INSERT on a laptop
me slice skips most parts

[chDB @chdb_io]

New in chdb-node on main: the fluent query builder gets a stream() terminal that yields big results one row at a time, with values bound server-side.

A forgotten LIMIT on an OLAP scan no longer buffers the whole result and risks OOM.

https://t.co/ELepMTNNGv

[chDB @chdb_io]

Embedded analytics on a DataFrame, in-process:

import chdb, pandas as pd
df = https://t.co/fgkFZl4n7O_parquet("traces.parquet")
chdb.query("SELECT tool, avg(latency_ms) FROM Python(df) GROUP BY 1 ORDER BY 2 DESC", "DataFrame")

Full ClickHouse SQL. No server, no copy.

[chDB @chdb_io]

New in chdb-core on main: chDB now builds to WebAssembly (wasm64). Run the full ClickHouse engine in the browser or Node via a new chdb-wasm npm package. Query local files, S3, and Parquet from a Web Worker. https://t.co/9qY43QZSXa

[chDB @chdb_io]

Embedded OLAP for an agent:

- sqlite: row store, fine for state, small logs
- duckdb: columnar, polished pandas + Parquet path
- chDB: columnar, full ClickHouse SQL, native vectors and time-series

All three zero-ops. Match the engine to the workload.

[chDB @chdb_io]

Agent memory works in pgvector. Works in Pinecone. Works in your local vector DB.

What changes with chDB embedded is that 'find similar past chats for user X in the last 30 days' is one SQL, in-process, no service, no creds.

Sometimes the win is just the missing sidecar.

What changes with chDB embedded is that 'find similar past chats for user X in the last 30 days' is one SQL, in-process, no service, no creds.

What changes with chDB embedded is that 'find similar past chats for user X in the last 30 days' is one SQL, in-process, no service, no creds.

[ClickHouse @ClickHouseDB]

Back in March we took stock of geospatial in ClickHouse.

https://t.co/HuL4Wo9AQZ

An H3-indexed radius query over 10M NYC taxi rows ran in 0.006s, about 12x faster than a bounding box.

There are six geometry types and WKT/WKB import from PostGIS and GeoParquet too.