datap-rs 0.2.16

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

datap-rs

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Documentation · Source · PyPI

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 Linux (x86_64/aarch64), macOS (arm64), 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/v1/datasets
curl http://localhost:8000/api/v1/datasets/accidents/schema
curl -X POST http://localhost:8000/api/v1/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

Six public classes, no module-level state:

Class	Purpose
DataPressConfig	Server tuning: backend, listen, port, workers, prefix, compress, max_body_bytes, max_page_size, request_timeout_ms, shutdown_timeout_secs, metrics_enabled, metrics_path.
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 + optional AuthConfig. await .run().
AuthConfig	OIDC / OAuth2 bearer enforcement (requires the auth feature in the wheel).
DataPressClient	Sync HTTP client for talking to a running server (stdlib + lazy pyarrow).

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/v1/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)

Request limits & timeouts

Two server-side guardrails are on by default:

DataPressConfig(
    backend="datafusion",
    port=8000,
    max_body_bytes=1_048_576,    # 413 above this; default 1 MiB
    max_page_size=100_000,       # clamp query page_size above this
    request_timeout_ms=30_000,   # 504 above this; 0 disables; default 30s
    shutdown_timeout_secs=30,    # SIGTERM/SIGINT grace period, in seconds
)

Bodies larger than max_body_bytes are rejected with 413 Payload Too Large. Query page_size values larger than max_page_size are clamped before the backend runs. Handlers that take longer than request_timeout_ms are cancelled and the client sees 504 Gateway Timeout. Set the timeout to 0 to disable it entirely (useful behind a proxy that already enforces one).

Graceful shutdown

On SIGTERM or SIGINT (Ctrl+C) the server stops accepting new connections, then waits up to shutdown_timeout_secs seconds for in-flight requests to finish before stopping workers. Set it lower for faster restarts, higher for long-running query handlers.

Client

A small sync client is bundled for talking to a running server:

from datap_rs import DataPressClient

c = DataPressClient("http://127.0.0.1:8000")
c.healthz()                                  # -> {"status": "ok"}
c.readyz()                                   # -> {"status": "ready", "datasets": N}
c.datasets()                                 # -> ["accidents", ...]
c.schema("accidents")                        # -> dict
c.count("accidents")                         # -> int
table = c.query("accidents", {               # -> pyarrow.Table
    "columns":   ["State", "Severity"],
    "page_size": 10_000,
})

query() requests Arrow IPC and returns a pyarrow.Table (pyarrow is imported lazily). For the JSON envelope verbatim, use query_json(). On non-2xx responses a DataPressHTTPError is raised with .status, .body and .payload.

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/v1/datasets	List configured datasets.
GET	/api/v1/datasets/{name}/schema	Inferred columns + sample row.
POST	/api/v1/datasets/{name}/query	Filter + paginate.
POST	/api/v1/datasets/{name}/count	Total or filtered row count.
POST	/api/v1/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	1000	Clamped to DataPressConfig.max_page_size (100_000 by default).

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/v1/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/v1/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/v1/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"]

To read the complete result set into Polars, walk pages until the server returns fewer rows than requested:

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

ARROW = "application/vnd.apache.arrow.stream"


def query_all_polars(
    base_url: str,
    dataset: str,
    body: dict,
    page_size: int = 100_000,
) -> pl.DataFrame:
    tables: list[pa.Table] = []
    page = 1

    with requests.Session() as session:
        while True:
            response = session.post(
                f"{base_url.rstrip('/')}/api/v1/datasets/{dataset}/query",
                json={**body, "page": page, "page_size": page_size},
                headers={"Accept": ARROW},
            )
            response.raise_for_status()

            table = ipc.open_stream(response.content).read_all()
            tables.append(table)

            if table.num_rows < page_size:
                break
            page += 1

    table = tables[0] if len(tables) == 1 else pa.concat_tables(tables)
    return pl.from_arrow(table)

Use a deterministic order_by for full exports from datasets that may be reloaded while you page through results. Arrow IPC is supported by both backends.

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/v1/datasets/accidents/count \
  -H 'Content-Type: application/json' -d '{}'
# → { "count": 7728394 }

Admin reload

POST /api/v1/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/v1/datasets/accidents/reload
# → { "dataset": "accidents", "rows": 7728394, "elapsed_ms": 1842 }

Reload publication is backend-specific. DataFusion uses a service-level double buffer: it builds a new DatasetState off to the side, then publishes it with an ArcSwap snapshot update. In-flight queries keep using the old Arrow buffers; later queries see the new state. Peak RSS can approach roughly twice the materialised dataset size during reload.

DuckDB delegates the heavy publication step to the engine with CREATE OR REPLACE TABLE ... AS SELECT .... DuckDB handles that as an ACID transaction over the table/catalog replacement: failures leave the existing table live, and successful reloads become visible atomically to later queries while in-flight queries continue against their starting snapshot. DataPress then refreshes its small cached schema and row-count metadata. Per-dataset reloads are serialised by an async mutex; reloads of different datasets run in parallel.

---

Authentication (OIDC / OAuth2)

Optional bearer-token enforcement against any OpenID Connect issuer (Keycloak, Auth0, Entra ID, Okta, Zitadel, …). Requires a wheel built with the auth Cargo feature:

maturin build --release --features auth

Pre-built PyPI wheels include it by default.

from datap_rs.datapress import (
    DataPress, DataPressConfig, DatasetConfig, AuthConfig,
)

auth = AuthConfig(
    enabled=True,
  issuer="https://issuer.example.com",
    audience="datapress-api",
    read_scopes=["datasets:read"],
    reload_scopes=["datasets:reload"],
    # anonymous_read=False,
    # algorithms=["RS256"],
    # leeway_secs=60,
    # jwks_refresh_secs=3600,
    # tenant_claim="/tenant_id",
    # allowed_tenants=["acme"],
    # admin_token_fallback=True,    # honour legacy X-Admin-Token
    # start_degraded=True,          # boot even if JWKS fetch fails
)

server = DataPress(cfg, datasets=[ds], auth=auth)
await server.run()

When enabled=False (default) all other fields are ignored and the server behaves exactly as before. Validation errors (missing issuer, malformed tenant_claim, …) raise ValueError at construction time.

Call any endpoint with Authorization: Bearer <jwt>. Reload endpoints require reload_scopes; read endpoints require read_scopes unless anonymous_read=True.

Use your provider's issuer URL exactly as it appears in the discovery document or JWT iss claim. /realms/<realm> is Keycloak-specific; many providers use URLs such as https://tenant.us.auth0.com/, https://login.microsoftonline.com/<tenant-id>/v2.0, or an Okta authorization-server URL.

AuthConfig applies to one server instance. For strict per-dataset scope boundaries from Python, run one DataPress instance per dataset or access domain and use scopes such as datasets:accidents:read / datasets:accidents:reload on that instance.

Try it locally

The repo ships a one-command Keycloak stack at examples/keycloak/ with a pre-provisioned realm, service-account client, scopes and a test user. docker compose up -d and point issuer at http://localhost:8080/realms/datapress.

---

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