datap-rs 0.1.17

Fast multi-backend (DuckDB / DataFusion) dataset HTTP server.

datap-rs

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A fast multi-backend dataset HTTP server, built in Rust and driven from Python.

datap-rs (datapress) exposes one or more Parquet or Delta datasets over a small JSON HTTP API. It ships with two pluggable engines bundled into a single wheel — pick one at runtime:

• DuckDB — battle-tested SQL, lazy parquet reads, low startup.
• DataFusion — pure-Rust, in-memory RecordBatch + equality index for low-latency point lookups.

Identical request/response shapes across both, so you can A/B them under your real workload.

---

Install

pip install datap-rs
# or
uv pip install datap-rs

Wheels are published for macOS (arm64/x86_64), Linux (x86_64/aarch64) and Windows (x86_64) against CPython 3.9+ (abi3).

---

Quick start

For testing, we're using this kaggle US accidents 2016-2023 dataset.

import asyncio
from datap_rs.datapress import DataPress, DataPressConfig, DatasetConfig

async def main() -> None:
    ds = DatasetConfig(
        name="accidents",
        source="data/accidents.parquet",
        format="parquet",          # or "delta"
        mode="auto",               # eq-index policy: "auto" | "none" | "list"
        description="US accidents 2016-2023",
    )
    cfg = DataPressConfig(
        backend="datafusion",      # or "duckdb"
        listen="0.0.0.0",
        port=8000,
        workers=8,
    )
    server = DataPress(cfg, datasets=[ds])
    await server.run()              # blocks until SIGINT

if __name__ == "__main__":
    asyncio.run(main())

Hit it:

curl http://localhost:8000/api/datasets
curl http://localhost:8000/api/datasets/accidents/schema
curl -X POST http://localhost:8000/api/datasets/accidents/query \
  -H 'Content-Type: application/json' \
  -d '{
    "columns": ["ID","Severity","City","State"],
    "predicates": [
      { "col": "State",    "op": "eq",  "val": "TX" },
      { "col": "Severity", "op": "gte", "val": 3   }
    ],
    "page": 1, "page_size": 50
  }'

---

API surface

Four classes, no module-level state:

Class	Purpose
DataPressConfig	Server tuning: backend, listen, port, workers, prefix, compress.
DatasetConfig	One dataset: name, source, format, mode, optional S3 + index.
S3Config	S3 / S3-compatible credentials and endpoint config.
DataPress	Built from a DataPressConfig + list of DatasetConfig. await .run().

Hover any of them in your IDE for full kwarg docs.

S3 / S3-compatible sources

from datap_rs.datapress import DataPress, DataPressConfig, DatasetConfig, S3Config

s3 = S3Config(
    region="us-east-1",
    endpoint="http://localhost:9000",   # MinIO / R2 / Wasabi / Backblaze
    addressing_style="path",            # or "virtual"
    allow_http=True,                    # only for non-https endpoints
)

ds = DatasetConfig(
    name="events",
    source="s3://events/2025/",
    format="parquet",                    # or "delta"
    s3=s3,
)

Credentials fall back to the standard AWS env vars (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_SESSION_TOKEN, AWS_REGION) when not set inline.

Behind a reverse proxy

Set prefix to mount every route under a URL path — handy when nginx / Traefik / Caddy forwards the prefix verbatim:

DataPressConfig(backend="datafusion", port=8000, prefix="/datapress")
# → GET /datapress/api/datasets, GET /datapress/health, ...

prefix must start with / and not end with /. Empty string (default) mounts at the root.

Response compression

Compression is on by default and negotiated per request via the Accept-Encoding header (gzip, brotli, zstd). Clients that want raw JSON send Accept-Encoding: identity or omit the header. Turn it off at the server when sitting behind a proxy that already compresses, or to save CPU on a trusted LAN:

DataPressConfig(backend="datafusion", port=8000, compress=False)

Equality-index policy (DataFusion only)

DatasetConfig(
    name="big",
    source="data/big.parquet",
    mode="list",                                  # "auto" | "none" | "list"
    index_columns=["State", "Severity"],          # required for "list"
    index_max_cardinality=100_000,                # used by "auto"
)

• auto — index every column whose distinct count stays below index_max_cardinality.
• none — skip the index; every query goes through DataFusion SQL.
• list — index only index_columns. Best for very wide datasets.

DuckDB ignores this block.

---

HTTP API

Same five routes for both backends.

Method	Path	Purpose
GET	/health	Liveness probe.
GET	/api/datasets	List configured datasets.
GET	/api/datasets/{name}/schema	Inferred columns + sample row.
POST	/api/datasets/{name}/query	Filter + paginate.
POST	/api/datasets/{name}/count	Total or filtered row count.
POST	/api/datasets/{name}/reload	Atomic dataset reload (requires admin token).

Query body

{
  "columns":   ["ID","City","State","Severity"],
  "predicates": [
    { "col": "State",    "op": "eq",  "val": "TX" },
    { "col": "Severity", "op": "gte", "val": 3   }
  ],
  "order_by": [ { "col": "Severity", "dir": "desc" } ],
  "limit":     1000,
  "page":      1,
  "page_size": 50
}

Field	Type	Default	Notes
columns	string[]	[]	Empty = all columns.
predicates	Predicate[]	[]	ANDed together.
order_by	OrderBy[]	[]	{ col, dir? }; dir is asc (default) or desc.
group_by	string[]	[]	Group-by columns; when set, columns is ignored.
aggregations	Aggregation[]	[]	{ col?, op, alias? }; ops: count|sum|avg|min|max. Requires group_by.
distinct	bool	false	Dedup the projected columns. Mutually exclusive with group_by / aggregations.
limit	int or null	null	Hard cap on total rows across pages.
page	int >= 1	1	1-based.
page_size	int 1..=1_000_000	1000	Clamped.

Predicate operators

op	val	Meaning
eq	scalar	col = val
neq	scalar	col <> val
gt / gte	number / string	col > val / col >= val
lt / lte	number / string	col < val / col <= val
like	string with %/_	SQL LIKE
ilike	string with %/_	Case-insensitive LIKE
in	non-empty array	col IN (v1, v2, …)
is_null	omit	col IS NULL
is_not_null	omit	col IS NOT NULL

Grouping / aggregation

curl -X POST http://localhost:8000/api/datasets/accidents/query \
  -H 'Content-Type: application/json' \
  -d '{
    "group_by": ["State"],
    "aggregations": [
      { "op":  "count" },
      { "col": "Severity", "op": "avg", "alias": "avg_sev" }
    ],
    "order_by": [{ "col": "count", "dir": "desc" }],
    "page_size": 10
  }'

When group_by is non-empty the SELECT list is derived from the group columns plus each aggregation's alias; the top-level columns field is ignored. aggregations without group_by returns 400. order_by keys must be a group column or aggregation alias.

Distinct

curl -X POST http://localhost:8000/api/datasets/accidents/query \
  -H 'Content-Type: application/json' \
  -d '{ "columns": ["State"], "distinct": true, "order_by": [{"col":"State"}] }'

Mutually exclusive with group_by / aggregations.

Arrow IPC responses

Opt in per-request with the Accept header (or ?format=arrow) to skip the JSON envelope and receive an Arrow IPC stream instead:

import requests, pyarrow.ipc as ipc, polars as pl

r = requests.post(
    "http://localhost:8000/api/datasets/accidents/query",
    json={"columns": ["ID","State"], "page_size": 1000},
    headers={"Accept": "application/vnd.apache.arrow.stream"},
)
table = ipc.open_stream(r.content).read_all()   # pyarrow.Table
df    = pl.from_arrow(table)                    # zero-copy → Polars
page, page_size = r.headers["X-Page"], r.headers["X-Page-Size"]

DataFusion backend only. DuckDB returns 400; fall back to JSON.

Count body

Same predicate shape, no projection or pagination:

{ "predicates": [ { "col": "State", "op": "eq", "val": "TX" } ] }

Response: { "count": <int> }. Empty body ({}) counts every row. On materialised DataFusion datasets, the no-predicate case is O(1) and indexed eq / in predicates short-circuit through the equality index.

curl -X POST http://localhost:8000/api/datasets/accidents/count \
  -H 'Content-Type: application/json' -d '{}'
# → { "count": 7728394 }

Admin reload

POST /api/datasets/{name}/reload rebuilds a dataset from its source and atomically swaps it in. Requires the X-Admin-Token header to match the ADMIN_TOKEN env var. Endpoint is disabled when ADMIN_TOKEN is unset (secure default).

import os
os.environ["ADMIN_TOKEN"] = "supersecret"     # before constructing DataPress

curl -X POST -H "X-Admin-Token: supersecret" \
  http://localhost:8000/api/datasets/accidents/reload
# → { "dataset": "accidents", "rows": 7728394, "elapsed_ms": 1842 }

Double-buffered, zero-downtime swap. Reload builds the new dataset off to the side (parquet decode + equality-index build happen on a worker thread against the old snapshot still being served), then a single ArcSwap::store flips the pointer in the shared map. In-flight queries finish against the old Arc; the next request sees the new data. The old buffers are dropped lazily once the last reader releases its reference — no locks, no GC pause, no "loading…" window. If the rebuild fails the swap simply doesn't happen and the old snapshot stays live. Per-dataset reloads are serialised by an async mutex; reloads of different datasets run in parallel. Peak RSS roughly doubles for the dataset being reloaded while both buffers are resident.

---

Choosing a backend

• DuckDB — the safe default. Handles arbitrary SQL well, manages its own buffer pool, starts up in milliseconds because it lazily reads parquet pages on demand.
• DataFusion — pick when the data fits in RAM and you repeatedly query the same columns with equality / IN predicates; the eq-index turns those into O(1) lookups. Also produces a leaner static binary (no vendored C++).

Both engines are compiled into the same wheel — switching is one keyword argument away.

---

Logging

datapress initialises env_logger on import. Control verbosity with the standard RUST_LOG variable:

RUST_LOG=info  python example.py
RUST_LOG=debug python example.py

---

License

MIT. See LICENSE in the source repo.

Source, issue tracker and Rust crates: https://github.com/jeroenflvr/fast-api