Skip to main content

Kaskada query builder and local execution engine.

Project description

Kaskada Timestreams

Kaskada's timestreams library makes it easy to work with structured event-based data. Define temporal queries on event-based data loaded from Python, using Pandas or PyArrow and push new data in as it occurs. Or, execute the queries directly on events in your data lake and/or as they arrive on a stream.

With Kaskada you can unleash the value of real-time, temporal queries without the complexity of "big" infrastructure components like a distributed stream or stream processing system.

Under the hood, timestreams is an efficient temporal query engine built in Rust. It is built on Apache Arrow, using the same columnar execution strategy that makes ...

Install Python

Use pyenv and install at least 3.8 (most development occurs under 3.11). If multiple versions are installed, nox will test against each of them.

Building and Testing

To build this package, first install maturin:

poetry shell
poetry install --no-root
maturin develop
pytest

Alternatively, install nox and run the tests inside an isolated environment:

nox

Previewing Docs

  • Install quarto-cli on your machine. Also consider installing an IDE extension.

    See: https://quarto.org/docs/get-started/

  • Generate reference docs

    nox -s docs-gen
    

    You should re-run this after making any updates to the pysrc docstrings. If Preview Docs is running in another shell, the system should auto-refresh with your changes.

  • Preview docs (with auto-refresh on edit)

    nox -s docs
    
  • Cleanup generated and cached docs

    nox -s docs-clean
    

    Try this if you see something unexpected (especially after deleting or renaming).

  • Builds docs to docs/_site

    nox -s docs-build
    

    This is primarily used in CI.

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distribution

kaskada-0.6.1.tar.gz (797.6 kB view details)

Uploaded Source

Built Distributions

kaskada-0.6.1-cp38-abi3-win_amd64.whl (14.3 MB view details)

Uploaded CPython 3.8+ Windows x86-64

kaskada-0.6.1-cp38-abi3-manylinux_2_28_x86_64.whl (17.6 MB view details)

Uploaded CPython 3.8+ manylinux: glibc 2.28+ x86-64

kaskada-0.6.1-cp38-abi3-macosx_11_0_x86_64.whl (15.5 MB view details)

Uploaded CPython 3.8+ macOS 11.0+ x86-64

kaskada-0.6.1-cp38-abi3-macosx_11_0_x86_64.macosx_11_0_arm64.macosx_11_0_universal2.whl (30.3 MB view details)

Uploaded CPython 3.8+ macOS 11.0+ ARM64 macOS 11.0+ universal2 (ARM64, x86-64) macOS 11.0+ x86-64

File details

Details for the file kaskada-0.6.1.tar.gz.

File metadata

  • Download URL: kaskada-0.6.1.tar.gz
  • Upload date:
  • Size: 797.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/4.0.2 CPython/3.11.6

File hashes

Hashes for kaskada-0.6.1.tar.gz
Algorithm Hash digest
SHA256 431fbaa05d87afd09f6d681888694d3ccb1ee6f816aa9344e934a02d84187e77
MD5 094802f22dbf8274c677a1716710f5b4
BLAKE2b-256 44d1dc10b99e8dbf655f24c19e81396e589fa7ed2e72872a00664ea505d766bd

See more details on using hashes here.

File details

Details for the file kaskada-0.6.1-cp38-abi3-win_amd64.whl.

File metadata

  • Download URL: kaskada-0.6.1-cp38-abi3-win_amd64.whl
  • Upload date:
  • Size: 14.3 MB
  • Tags: CPython 3.8+, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/4.0.2 CPython/3.11.6

File hashes

Hashes for kaskada-0.6.1-cp38-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 1727c329ffe65228609c4cfb93cf8a6ac99df28391621dc7499d3e3353fd1789
MD5 3391ba997c7c5bf2aae5be104968cdff
BLAKE2b-256 e5e8022b46a253bde2947fcc08baad109bb5c504af66926f430e83614d8f3880

See more details on using hashes here.

File details

Details for the file kaskada-0.6.1-cp38-abi3-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for kaskada-0.6.1-cp38-abi3-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 7cc643f0eae978bac7387f1d70cfaef7950dc08805ff6b572e24564f01958e6d
MD5 cc6aff4877a685cc143e5a42b1f62e5a
BLAKE2b-256 f803abca2eec655b9f68977fcd31a8c14659396848c43710cc67bcc5cd196c43

See more details on using hashes here.

File details

Details for the file kaskada-0.6.1-cp38-abi3-macosx_11_0_x86_64.whl.

File metadata

File hashes

Hashes for kaskada-0.6.1-cp38-abi3-macosx_11_0_x86_64.whl
Algorithm Hash digest
SHA256 0ca5ede175608d2ec9e7bcfc2cda9b2227f22c643bc5b59bbf6b892b6769d019
MD5 52051a8696962d83bc456435c0914696
BLAKE2b-256 300369e0bf8e0f5ba982853e96dfa267087383e30c7331cdce08c343bf442d76

See more details on using hashes here.

File details

Details for the file kaskada-0.6.1-cp38-abi3-macosx_11_0_x86_64.macosx_11_0_arm64.macosx_11_0_universal2.whl.

File metadata

File hashes

Hashes for kaskada-0.6.1-cp38-abi3-macosx_11_0_x86_64.macosx_11_0_arm64.macosx_11_0_universal2.whl
Algorithm Hash digest
SHA256 cd8350e7cad75986a6a4d61d43b94699b5d10097851959e7c67e11c01a267e40
MD5 c339d195f468117673c5a89be41b1e9d
BLAKE2b-256 d790ec21af75f3d409d2d1d189015753f7ac5e81f940694771b896a32417347d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page