arrowbricks

Runs SQL against a Databricks SQL warehouse via the Statement Execution API and hands you the result as Arrow -- a Cursor shaped like databricks-sql-python's (execute, fetchone/fetchmany/fetchall, fetchall_arrow/fetchmany_arrow), or stream_query_json for streaming NDJSON. One Arrow engine (arro3), no DuckDB, no pandas/pyarrow.

• Single responsibility: Databricks to Arrow via arro3. No embedded query engine -- that's duckbricks, built on top of this.
• Bring-your-own-auth -- a static token or your own token-refresh callable. No cloud-SDK dependency baked in.
• Result-order preserved even though chunks can complete out of order over the network.
• Chunks are fetched lazily as fetchone/fetchmany/fetchall actually need them, not all upfront.
• Heartbeats between slow chunks (execute_streamed/stream_query_json), so a caller streaming this over e.g. SSE never goes silent during a cold warehouse start.

Install

pip install arrowbricks

Dependencies: httpx + arro3-core + arro3-io. That's the whole tree.

Quickstart

import asyncio
from arrowbricks import connect


async def main():
    conn = connect(
        host="adb-1234567890.1.azuredatabricks.net",
        warehouse_id="abcd1234efgh5678",
        token="dapi...",  # or token_provider=... -- see Auth below
    )
    cursor = conn.cursor()

    await cursor.execute("SELECT * FROM my_catalog.my_schema.my_table LIMIT 100")
    async for row in cursor:
        print(row)

    await cursor.execute("SELECT * FROM my_catalog.my_schema.my_table LIMIT 100")
    table = await cursor.fetchall_arrow()  # an arro3 Table


asyncio.run(main())

For streaming NDJSON (e.g. a FastAPI SSE endpoint, first row out as soon as its chunk arrives):

from arrowbricks import HEARTBEAT, DatabricksClient, stream_query_json

client = DatabricksClient(host=..., warehouse_id=..., token=...)

async for item in stream_query_json(client, "SELECT * FROM my_catalog.my_schema.big_table"):
    if item is HEARTBEAT:
        continue  # forward as an SSE keep-alive comment, e.g.
    print(item)  # one ready-to-send JSON string per row

See examples/basic.py for a runnable version, examples/cursor_paging.py for paging a large result with fetchmany/fetchmany_arrow without buffering it all upfront, examples/fastapi_sse.py for streaming a query to a client as Server-Sent Events, examples/fastapi_sse_pivot.py for the same over a buffered Cursor.fetchall_streamed result with one combined heartbeat/timeout budget across both the wait and the download, or examples/azure_auth.py for a caching token_provider built on Azure AD (DefaultAzureCredential).

Why not databricks-sql-connector?

The official driver is the right choice if you need full DB-API 2.0 compatibility over Databricks' Thrift/ODBC-style protocol. If you just want a query result as Arrow/JSON in your own async app, it drags in a lot for that: pandas, thrift, openpyxl, pybreaker, pyjwt, oauthlib, lz4, requests, urllib3 as hard dependencies. arrowbricks talks to the plain REST Statement Execution API instead, and its whole dependency tree is httpx + arro3-core + arro3-io. The Cursor API is deliberately shaped like the official driver's so switching between them is mostly a constructor change, but arrowbricks is async throughout (execute, fetchone, etc. are all coroutines) -- there's no sync escape hatch.

Why not duckbricks?

duckbricks does the same Databricks-to-Arrow work, then goes further: it uses a real embedded DuckDB engine to materialize results into your own DuckDB connection/table (feed_select_to_duckdb_table), or push a DuckDB query's result up to Databricks (feed_duckdb_table_to_databricks). If you need that -- a real local SQL engine sitting on top, not just "run this query, get Arrow/JSON back" -- use duckbricks; it depends on arrowbricks for the Databricks/Arrow half. If you don't need DuckDB at all, arrowbricks alone is the smaller, single-responsibility half.

Auth

connect/DatabricksClient take either:

• token: str -- a static personal access token or pre-issued OAuth token, or
• token_provider -- a callable (sync or async) returning a token string, called on every request.

arrowbricks has no opinion on how you get a token and no cloud-SDK dependency of its own. If your provider is expensive to call, cache/refresh inside it -- arrowbricks does no caching on your behalf.

conn = connect(host=..., warehouse_id=..., token_provider=my_token_provider)

API

• connect(host, warehouse_id, *, token=None, token_provider=None, ...) -> Connection
• Connection.cursor() -> Cursor
• Connection.client -> DatabricksClient -- the same client cursor() uses, for lower-level access (e.g. stream_query_json, execute_json_statement, upload_volume_file).
• Cursor.execute(sql, parameters=None, *, row_limit=None, offset=None, catalog=None, schema=None, total_timeout_s=None) -> Cursor -- submits and waits for the statement, like a real DB-API cursor. parameters, if given, is Databricks' own named-parameter format -- [{"name": ..., "value": ..., "type": ...}] bound against :name markers in sql.
• Cursor.execute_streamed(...) -- same args, but an async generator yielding HEARTBEAT while waiting on a slow cold start, then the ready Cursor -- for bridging e.g. an SSE connection. Its timeout/heartbeats stop the moment the statement is ready, before any chunk has been downloaded -- see fetchall_streamed below for the download phase itself.
• Cursor.fetchone() -> tuple | None, Cursor.fetchmany(size) -> list[tuple], Cursor.fetchall() -> list[tuple]
• Cursor.fetchmany_arrow(size) -> arro3.core.Table, Cursor.fetchall_arrow() -> arro3.core.Table
• Cursor.fetchall_streamed(*, total_timeout_s=None) / Cursor.fetchall_arrow_streamed(*, total_timeout_s=None) -- like fetchall()/fetchall_arrow(), but yield HEARTBEAT while pulling chunks instead of blocking silently, then the final rows/Table -- for a caller downloading a large result over SSE who needs heartbeats (and a timeout) through the download, not just the initial wait. Compose with execute_streamed and a shared deadline if you want one combined budget across both phases (see examples/fastapi_sse_pivot.py).
• Cursor is an async iterator, yielding one row (tuple) at a time.
• Cursor.description -- DB-API-style [(name, type_name, None, None, None, None, None), ...] after execute().
• stream_query_json(client, sql, **kwargs) -- yields HEARTBEAT, then each row as a JSON string, as soon as its chunk arrives. Timestamps come out as full ISO-8601, every column key is always present ("col":null for a null value, never an omitted key).
• DatabricksClient(host, warehouse_id, *, token=None, token_provider=None, ...) -- the lower-level client Connection wraps. client.execute_json_statement(sql, ...) for plain JSON rows with no Arrow parse at all; client.upload_volume_file(volume_path, data)/client.delete_volume_file(volume_path) for the Files API.
• write_ipc_stream(table_or_chunk, buf) -- thin wrapper around arro3.io.write_ipc_stream that always writes uncompressed bodies (see below).

Cursor.execute/execute_streamed/stream_query_json all accept catalog, schema, row_limit, offset, and total_timeout_s.

A note on Arrow IPC compression

write_ipc_stream (and everything in this package that serializes Arrow-IPC bytes) always writes uncompressed bodies. arro3's own default (compression="LZ4") is transparently decompressed by DuckDB's Arrow reader, but not necessarily by every other Arrow IPC reader -- notably, duckdb-wasm's browser-side decoder silently fails to parse LZ4-compressed bodies. If you're producing bytes that might be consumed by something other than a Python DuckDB connection, this default matters.

License

MIT