exasol-quickstart 0.3.1

One command to try Exasol with AI add-ons (MCP Server + JSON Tables) — auto-selects the right setup for your OS (Windows / macOS / Linux).

⚡ exasol-quickstart

One command to try Exasol with AI add-ons — it installs the right way for your OS: the database + an MCP server + JSON Tables, ready in minutes.

📦 PyPI: pypi.org/project/exasol-quickstart  ·  💻 GitHub

pipx install exasol-quickstart
exasol-quickstart

That's it. The tool detects your operating system and installs Exasol the right way for it — no flags, no multi-step setup — then prints the endpoints.

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What you get

Component	Endpoint	What it's for
Exasol (database)	127.0.0.1:8563 · user sys / exasol	the Exasol SQL engine
MCP Server	http://127.0.0.1:4896/mcp	point Claude / any MCP client here to talk to the DB
JSON Tables	exasol-quickstart json-tables …	ingest JSON and query it as SQL

Web UI: https://127.0.0.1:8443.

Requirements — only one thing is universal

• Python 3.9+ with pipx — the only thing you always need. No pipx yet?

  python -m pip install --user pipx
  python -m pipx ensurepath        # then reopen the terminal
  

You do not always need Docker. exasol-quickstart picks how Exasol runs based on your OS:

Your OS	How it installs Exasol	Docker?
Windows	Exasol Nano in a container	needs Docker Desktop (no native Windows engine exists)
macOS (Apple Silicon)	Exasol Personal — a native VM	no Docker (experimental today)
Linux	Exasol Nano container (native .run planned)	Docker for now

The container path (Docker) is the most thoroughly tested today; the native macOS path is experimental. Either way, the command and the result are the same — only the under-the-hood install differs.

Commands — tailor it to your needs

exasol-quickstart                      # the full bundle (DB + MCP + JSON Tables)
exasol-quickstart --no-json-tables     # DB + MCP only (faster; skips the Rust build)
exasol-quickstart --dry-run            # show the plan, change nothing
exasol-quickstart --base personal      # force a base: nano-docker | personal | nano-native
exasol-quickstart json-tables --help   # run the JSON Tables CLI
exasol-quickstart --help

Ingest some JSON

docker cp data.json exasol-quickstart-json-tables:/workspace/data.json
exasol-quickstart json-tables ingest-and-wrap --input /workspace/data.json --name demo
# now query it:  SELECT * FROM "EJT_DEMO_VIEW"."demo";

On the container path, the first run pulls exasol/nano + exasol/mcp-server and builds the JSON Tables image once (it compiles a small Rust engine — a few minutes). Later runs are fast. Stop everything with: docker rm -f exasol-quickstart-db exasol-quickstart-mcp exasol-quickstart-json-tables

Why it's built this way

The Exasol engine is Linux-native, so the most portable, tested path runs it in a container; on macOS it can instead use Exasol Personal, which runs the database in a native VM (no Docker). MCP and JSON Tables have conflicting pyexasol pins, so each runs isolated (separate containers, or separate host envs). Full rationale, decision graph, and pros/cons:

📖 The recommended approach

Status

0.3.1 — the bare command auto-selects per OS. The container path (Nano + MCP + JSON Tables) is tested end-to-end, including ingest. The no-Docker native bases (Exasol Personal on macOS, Nano .run on Linux) are wired in and selected automatically when Docker is absent; the macOS path is experimental and not yet validated end-to-end.

Links

• PyPI: https://pypi.org/project/exasol-quickstart/
• GitHub: https://github.com/krishna-exasol/exasol-quickstart
• Docs (design, decision graph, pros/cons): https://krishna-exasol.github.io/bundle-installation-methods/case-studies/recommended-approach/

License

MIT.