Gwenlake Python Library

The Gwenlake Python library provides convenient access to the Gwenlake API from applications written in Python. A single Gwenlake client gives you access to your catalog — projects, datasets, files and SQL.

Installation

pip install -U git+https://github.com/gwenlake/gwenlake-python

Authentication

The client authenticates with a Bearer token, resolved in this order:

an explicit api_key / credentials passed to the client,
a named profile,
the GWENLAKE_API_KEY environment variable,
the default profile in ~/.gwenlake/credentials.

export GWENLAKE_API_KEY='sk-...'

import gwenlake

# uses GWENLAKE_API_KEY, or the default ~/.gwenlake/credentials profile
client = gwenlake.Gwenlake()

# or pass the key explicitly
client = gwenlake.Gwenlake(api_key="sk-...")

# or pick a profile from ~/.gwenlake/credentials
client = gwenlake.Gwenlake(profile="myteam")

The ~/.gwenlake/credentials file is an INI file with one section per profile, holding either a static token (API key) or OAuth2 client_id / client_secret.

Projects

projects = client.projects.list()
for p in projects:
    print(p["alias"], p["id"])

project = client.projects.get("res.project.…")

Datasets

datasets = client.datasets.list()
for d in datasets:
    print(d["alias"], d["id"])

dataset = client.datasets.get("res.dataset.…")

Files

Files live inside a dataset.

dataset_id = "res.dataset.…"

# list files
for f in client.files.list(dataset_id):
    print(f["filename"], f["file_size"])

# upload a local file (optionally into a subdirectory with path=...)
client.files.upload(dataset_id, "report.pdf")
client.files.upload(dataset_id, "report.pdf", path="docs")

# download a file
content = client.files.download(dataset_id, "report.pdf")

# presigned URL / delete
url = client.files.presigned_url(dataset_id, "report.pdf")
client.files.delete(dataset_id, "report.pdf")

SQL

Run SQL against a dataset (DuckDB), referencing it as '<project_alias>.<dataset_alias>'. With format="json" the rows are returned under data:

result = client.statements.create(
    statement="SELECT * FROM 'flights.flight-data' LIMIT 10",
    format="json",
)
for row in result["data"]:
    print(row)

Pass a connection_id to run the statement against a connection's native engine (PostgreSQL, S3, …) instead of a dataset.

Async

Every resource is also available on AsyncGwenlake:

import asyncio
import gwenlake

async def main():
    client = gwenlake.AsyncGwenlake()
    print(await client.projects.list())

asyncio.run(main())

See examples/ for runnable scripts.

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