Opteryx Core

Opteryx Core is the SQL execution engine behind opteryx.app. It is a fork of Opteryx with a smaller, more opinionated API and configuration surface, shaped around the workloads used by the hosted service.

This library is designed for fast, read-heavy analytical queries over Parquet-backed data. It handles SQL parsing, planning, predicate pushdown, projection pruning, and execution so you can query datasets from Python without standing up a separate warehouse.

This project is opinionated toward the needs of opteryx.app. It is still useful as a standalone library if you want to query local Parquet, NDJSON, and CSV datasets, embed SQL into a Python service or notebook, or experiment with engine internals directly.

Requirements

  • Python 3.11 or later
  • A C/C++ toolchain for local source builds
  • Rust/Cargo for the Rust extension in src/

Install

pip install opteryx-core

Import it as:

import opteryx

Quick Start: Query Local Files

If your current working directory contains local Parquet data, the simplest way to use Opteryx Core is to register a local workspace and query it with dot-separated names.

import opteryx
from opteryx.connectors import DiskConnector

opteryx.register_workspace("data", DiskConnector)

session = opteryx.session()
result = session.execute_to_arrow(
    "SELECT id, name FROM data.planets WHERE id < 5"
)

print(result)

In this model, dataset names are resolved relative to the current working directory. For example, data.planets resolves to ./data/planets, and Opteryx Core reads the Parquet files it finds there.

What It Is For

  • Powering the execution layer used by opteryx.app
  • Running analytical SQL against local Parquet-backed datasets
  • Embedding a query engine inside Python applications, scripts, notebooks, and services
  • Working on engine internals such as planning, execution, and Parquet performance

Local Development

The supported local build path is the repository Makefile:

make dev-install
make compile
make q

Useful targets:

Target Purpose
make compile Clean in-place build of Cython, C++, and Rust extensions
make c Incremental extension build
make q Fast SQL shape smoke test
make test Full pytest suite after compiling
make dt Draken native unit tests
make check Ruff and import-order checks without modifying files

Do not use pip install . as the primary development build path; make compile matches the layout expected by this repository.

Repository Layout

Path Purpose
opteryx/ Python package, planner, operators, connectors, expression evaluation, and Cython modules
draken/ Native columnar vector substrate used by the execution engine
rugo/ Internal Parquet and JSONL reader used by scans and metadata paths
src/ Rust extension code, currently including the SQL dialect integration
tests/ Unit, integration, fuzzing, sqllogictest, and benchmark harnesses
testdata/ Local datasets and benchmark fixtures
dev/ Development, release, vendoring, and analysis scripts
scratch/ Experimental prototypes and one-off investigations
third_party/ Vendored native dependencies

Best With Opteryx Catalog

Opteryx Core works best when paired with the opteryx_catalog library. That is the intended model for named datasets, catalog-backed tables, and the general experience used in opteryx.app.

Typical setup:

import os

import opteryx

from opteryx import set_default_connector
from opteryx.connectors import OpteryxConnector
from opteryx_catalog import OpteryxCatalog

set_default_connector(
    OpteryxConnector,
    catalog=OpteryxCatalog,
    firestore_project=os.environ["GCP_PROJECT_ID"],
    firestore_database=os.environ["FIRESTORE_DATABASE"],
    gcs_bucket=os.environ["GCS_BUCKET"],
)

Once configured, you can query catalog-backed datasets using dot-separated names such as public.space.planets or opteryx.ops.billing.

For local data, Opteryx Core is typically used through registered workspaces such as testdata, scratch, or data. Queries refer to datasets by dot-separated names relative to the workspace root, for example testdata.planets, testdata.satellites, or scratch.signals.

Where It Fits

Opteryx Core is best thought of as an embedded analytical engine rather than a full end-user platform. If you want a hosted experience, multi-tenant service features, and the broader product workflow, use opteryx.app. If you want the core engine in your own environment, this package gives you that engine directly. If you want the intended table-resolution model, pair it with opteryx_catalog.

Contributing

If you use Opteryx-Core yourself, we want to hear from you.

  • Use it on your own datasets
  • Raise bugs when queries, schemas, or performance do not behave as expected
  • Open pull requests for fixes, tests, docs, or performance improvements
  • Share repro cases, failing queries, and edge-case Parquet files

This project is being actively built, and outside usage helps make it better.

Docs: https://docs.opteryx.app/ Source: https://github.com/mabel-dev/opteryx-core License: Apache-2.0

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.9.87)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for joocer from gravatar.com joocer

Credits

Author/Maintainer: Justin Joyce

Requires

Python >=3.11

Provides Extra

testing performance embeddings

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

opteryx_core-0.9.53.tar.gz (12.2 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

opteryx_core-0.9.53-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (44.6 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.17+ x86-64

opteryx_core-0.9.53-cp314-cp314-macosx_11_0_arm64.whl (24.9 MB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

opteryx_core-0.9.53-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (44.4 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64

opteryx_core-0.9.53-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (44.5 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

opteryx_core-0.9.53-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (44.7 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

File details

Details for the file opteryx_core-0.9.53.tar.gz.

File metadata

  • Download URL: opteryx_core-0.9.53.tar.gz
  • Upload date:
  • Size: 12.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for opteryx_core-0.9.53.tar.gz
Algorithm Hash digest
SHA256 2cb84901264074285d9590b5480e9de03832269e2dded5a1debbed09c4a86f8f
MD5 9efe83d6c208bf366779590732d1be56
BLAKE2b-256 8d7001fc233e1dab70ba1e0610456b131be45d9f0678f4f0e60293649619343b

See more details on using hashes here.

File details

Details for the file opteryx_core-0.9.53-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for opteryx_core-0.9.53-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 b1d5f466416f09e8ce7942cea3fdab1680bc2a63f162551e061a3bc606204a46
MD5 7ec779c6fd2c41280c127a6816acf486
BLAKE2b-256 5795c0a5b625d72dd17a015a7fa6c3fa60390a115832fedfc439e5738b675690

See more details on using hashes here.

File details

Details for the file opteryx_core-0.9.53-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for opteryx_core-0.9.53-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 275848d5fd9b84aa38e18af12f08835a4a046053bfce2a9707560adba9e59d5e
MD5 c4aba07e423e29417a1b3d42c7380293
BLAKE2b-256 eb7556057fe49d30e8dcb6d176c0c8b2c2b2e15115f8964756182d91f9b5a1db

See more details on using hashes here.

File details

Details for the file opteryx_core-0.9.53-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for opteryx_core-0.9.53-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 23e20bb5ec84f8916bf3c11686a839c34403a269f74b61f702030ee440af0763
MD5 f08d945581fde003c5668ea35636439b
BLAKE2b-256 d8032f1e464b76f79815ed0e23ac3636492f7ce1fec507739cd126894e26d294

See more details on using hashes here.

File details

Details for the file opteryx_core-0.9.53-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for opteryx_core-0.9.53-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 a7bf9ca23759545626a2e6b2a5b44aef38e554742863afea27bf31174dc01657
MD5 2e2551973b2ca669b6fd67c0a04d6e59
BLAKE2b-256 2312a70e8581647d47a3837aae26a7cb6f60221e381d296d3264e982f66be84f

See more details on using hashes here.

File details

Details for the file opteryx_core-0.9.53-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for opteryx_core-0.9.53-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 e797c18d9b504f6ffa9d48f182d5a7f78f84e4518d3a45b3a9f0671adc9936ee
MD5 37e38761f828dd561ca58d709de76b33
BLAKE2b-256 04e06eb631de45ab8523980170d0f1c4b09b1414ba56990aeb5d61573d7526c3

See more details on using hashes here.

Release history Release notifications | RSS feed

0.9.87

7 files

0.9.86

7 files

0.9.85

7 files

0.9.84

7 files

0.9.83

7 files

0.9.82

7 files

0.9.81

7 files

0.9.80

7 files

0.9.79

7 files

0.9.78

7 files

0.9.77

7 files

0.9.76

7 files

0.9.75

7 files

0.9.74

7 files

0.9.73

7 files

0.9.71

10 files

0.9.70

10 files

0.9.69

10 files

0.9.68

6 files

0.9.67

6 files

0.9.66

6 files

0.9.65

6 files

0.9.64

6 files

0.9.63

6 files

0.9.62

6 files

0.9.60

6 files

0.9.58

6 files

0.9.56

6 files

0.9.55

6 files

0.9.54

6 files

This release

0.9.53 This release

6 files

0.9.52

6 files

0.9.50

5 files

0.9.49

5 files

0.9.48

5 files

0.9.47

5 files

0.9.46

5 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page