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Approximate, streaming SQL engine

Project description

📊 StatQL

StatQL is a desktop‑first SQL engine that streams fast, approximate answers from your data sources—without spinning up a server.

Initial release ships with connectors for PostgreSQL, Redis (α quality) and the local Filesystem. More catalogs—cloud object storage, OLAP warehouses, REST APIs—are on the roadmap.


Quick start

To launch StatQL, install it and start up the local server:

pip install statql
python -m statql            # launches the Streamlit server

Then in your browser, navigate to http://localhost:8501

Integrate your data sources in the Integrations page, then start running your queries!


Usage examples

StatQL emits a fresh result table every 0.5 – 1 s. Each numeric cell is formatted as value ± error, where error is the absolute 95 % confidence‑interval half‑width. As more of the population is sampled, the estimates tighten.

1️⃣ Row count per order status (single table)

SELECT l_linestatus, COUNT() AS rows
FROM pg.local.tpch.public.lineitem
GROUP BY l_linestatus;
l_linestatus rows
F 3 172 481 ± 46 892.7
O 3 181 633 ± 46 744.2

2️⃣ Total PNG footprint in /data/images (filesystem)

SELECT divide(SUM(size), 1_073_741_824) AS gib
FROM fs.media_fs.entries
WHERE get_file_ext(path) = 'png';
gib
118.6 ± 5.3

3️⃣ Row counts across all databases in a cluster (wildcards)

SELECT @db, COUNT() AS rows
FROM pg.us-cluster-4.?.public.lineitem
GROUP BY @db
ORDER BY rows DESC;
@db rows
sales 12 945 002 ± 99 811.4
hr 6 088 551 ± 74 225.9

Video Tutorial

StatQL Tutorial

👉 Full docs → docs/index.md

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