arrowbricks

Databricks SQL to Arrow, with a Rust core.

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.

• Rust core. Statement submit/poll, bounded-concurrency chunk fetch, the reorder buffer, and Arrow-IPC decode all run in a PyO3/arrow-rs extension bundled in this same package -- 1.6x-2.5x faster than a pure-Python/asyncio client on a multi-chunk result, scaling further with chunk count and concurrency where asyncio+GIL plateaus.
• Compressed cloud-fetch transport by default. Every statement requests LZ4-compressed chunk downloads (same default as the official databricks-sql-connector) and decompresses them in Rust before you ever see the bytes -- less data over the wire, which matters more than local decode speed for a large result. Measured ~2x faster chunk-fetch time against a real 120-column/100k-row table. Disable per-client with compress_results=False (connect()/DatabricksClient(...)) if your link to the warehouse is fast enough that decompression CPU time stops paying for itself.
• Zero required dependencies. pip install arrowbricks and go.
• 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.
• Lazy fetching -- chunks are pulled only 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

Ships as precompiled platform wheels (Linux/macOS/Windows) -- no Rust toolchain needed, and nothing else to install for most of the API. Row-tuple fetches (fetchone/fetchmany/fetchall) need one optional extra: pip install arrowbricks[arro3] -- see Arrow vs. row-tuple fetches below.

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 Arrow table (arro3/pyarrow/DuckDB-compatible)


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

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

async for item in client.stream_query_json("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, or examples/azure_auth.py for a caching token_provider built on Azure AD (DefaultAzureCredential).

FastAPI SSE example

stream_query_json is the full-speed way to serve a query over HTTP: the first row reaches the client after roughly one chunk's fetch/decode time, not the whole query's -- the Rust core is fetching, decoding, and reordering chunks concurrently the entire time, and the HEARTBEATs keep the connection alive through a slow cold warehouse start instead of the client just seeing dead air:

import os
from collections.abc import AsyncIterator

from fastapi import FastAPI
from fastapi.responses import StreamingResponse

from arrowbricks import HEARTBEAT, DatabricksClient

app = FastAPI()
client = DatabricksClient(
    host=os.environ["DATABRICKS_HOST"],
    warehouse_id=os.environ["DATABRICKS_WAREHOUSE_ID"],
    token=os.environ["DATABRICKS_TOKEN"],
)


async def _sse(sql: str) -> AsyncIterator[str]:
    async for item in client.stream_query_json(sql, total_timeout_s=300):
        if item is HEARTBEAT:
            yield ": keep-alive\n\n"  # SSE comment line -- clients ignore it, it just keeps the connection open
        else:
            yield f"data: {item}\n\n"


@app.get("/query")
async def query(sql: str) -> StreamingResponse:
    return StreamingResponse(_sse(sql), media_type="text/event-stream")

uvicorn app:app --reload
curl -N "http://localhost:8000/query?sql=SELECT+*+FROM+range(1000000)"

This example takes sql straight from the request for brevity -- arrowbricks does no SQL validation by design, so a real deployment must validate/allowlist it (or accept fixed query names + params) before exposing a route like this publicly. See examples/fastapi_sse.py for the runnable version, 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/fastapi_sse_validated.py for one way to do that validation, using sqlglot to require a single read-only SELECT against an allowlist of fully-qualified tables.

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, upload_volume_file).
• Cursor.execute(sql, parameters=None, *, row_limit=None, offset=None, catalog=None, schema=None, total_timeout_s=None, prefer_inline=False) -> 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. prefer_inline=True tries fetching a small result (well under Databricks' 25 MiB inline cap) in the same round trip as the submission itself, skipping the chunk-fetch entirely -- if the result turns out too big, or has a column type this can't convert (nested ARRAY/MAP/STRUCT, VARIANT), it transparently re-runs the query the normal way, so a caller who sets this without actually expecting a small result pays for the query twice. Leave it off unless you know the result is small.
• 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], and iterating a Cursor directly -- row tuples; needs the arro3 extra.
• Cursor.fetchmany_arrow(size) -> Table, Cursor.fetchall_arrow() -> Table -- an Arrow table (implements __arrow_c_stream__, so arro3/pyarrow/DuckDB can all consume it directly, zero-copy).
• 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.description -- DB-API-style [(name, type_name, None, None, None, None, None), ...] after execute().
• client.stream_query_json(sql, **kwargs) (or the equivalent free function 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). JSON has no literal for NaN/Infinity/-Infinity, so those come back as "col":null by default -- pass non_finite_floats="string" to get "col":"NaN"/"col":"Infinity"/"col":"-Infinity" instead if you need to tell them apart from a real NULL.
• DatabricksClient(host, warehouse_id, *, token=None, token_provider=None, ...) -- the lower-level client Connection wraps. client.upload_volume_file(volume_path, data)/client.delete_volume_file(volume_path) for the Files API.
• write_ipc_stream(table, buf) -- writes any Arrow-C-Data-Interface-compatible object as an uncompressed Arrow-IPC stream (see below).
• ReplayableArrowChunk(data: bytes, chunk_index, declared_row_count=None) -- wraps raw Arrow-IPC stream bytes (e.g. previously downloaded and stored) so they can be read more than once via __arrow_c_stream__ (a schema peek, then the actual scan -- DuckDB's registration path does this), and .to_table() for a one-shot parse. No extra dependency needed.

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

Arrow vs. row-tuple fetches

Everything above works with zero dependencies installed except row-tuple fetches. fetchall_arrow/fetchmany_arrow return an Arrow table straight from the Rust core -- the faster path if your code can consume Arrow directly (DuckDB, pyarrow, polars, a Parquet writer, ...):

import duckdb

table = await cursor.fetchall_arrow()
duckdb.sql("SELECT count(*) FROM table").show()  # DuckDB reads it zero-copy

fetchone/fetchmany/fetchall (and iterating a Cursor directly) materialize actual Python tuples instead -- ("id", "label")-style rows you can index into, print, or pass to code that doesn't know about Arrow at all. That conversion needs arro3-core (pip install arrowbricks[arro3]):

await cursor.execute("SELECT id, label FROM my_catalog.my_schema.my_table")
async for row in cursor:  # or: rows = await cursor.fetchall()
    print(row[0], row[1])

Calling a row-tuple method without arro3-core installed raises a ModuleNotFoundError naming the exact install command, rather than failing silently or with a confusing traceback.

Using with DuckDB

Anything arrowbricks hands back as Arrow (fetchall_arrow/fetchmany_arrow, ReplayableArrowChunk) implements __arrow_c_stream__, so DuckDB can register and query it directly -- zero-copy, no intermediate materialization, and no arro3-core/pyarrow install needed on top:

import duckdb
from arrowbricks import connect

conn = connect(host=..., warehouse_id=..., token=...)
cursor = conn.cursor()
await cursor.execute("SELECT * FROM my_catalog.my_schema.my_table LIMIT 100")
table = await cursor.fetchall_arrow()

con = duckdb.connect()
con.register("my_table", table)
con.sql("SELECT count(*) FROM my_table").show()

ReplayableArrowChunk works the same way for Arrow-IPC bytes you fetched and stored earlier (e.g. a raw chunk's bytes, cached in Redis/a file/wherever) -- DuckDB's registration path calls __arrow_c_stream__ twice (a schema peek, then the actual scan), which is exactly what ReplayableArrowChunk exists to support:

from arrowbricks import ReplayableArrowChunk

chunk = ReplayableArrowChunk(stored_bytes, chunk_index=0)
con.register("my_table", chunk)
con.sql("SELECT * FROM my_table WHERE id = 42").show()

Rust core

rust/arrowbricks_core is the crate implementing the hot path above, built into this same arrowbricks wheel as a compiled submodule -- not a separate PyPI package. See its own README for the crate-level design, plus standalone DuckDB and FastAPI SSE examples against the compiled extension directly.

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, with a Rust core and zero required dependencies of its own. 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.

A note on Arrow IPC compression

write_ipc_stream (and everything in this package that serializes Arrow-IPC bytes) always writes uncompressed bodies. A compressed body (arro3's own default is compression="LZ4") is transparently decompressed by some Arrow readers (e.g. DuckDB's) but not necessarily by every other Arrow IPC reader -- notably, duckdb-wasm's browser-side decoder silently fails to parse LZ4-compressed bodies. Since arrowbricks' bytes might end up read by anything, plain uncompressed is the safe default.

License

MIT