Daft

Latest version: v0.4.9

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0.1.3

The Daft 0.1.3 release features fixes for a few performance regressions.

Enhancements

1. Very basic s3 parquet microbenchmark [954](https://github.com/Eventual-Inc/Daft/pull/954)

Bug Fixes

1. [I/O] Change back to random access read for Parquet. [953](https://github.com/Eventual-Inc/Daft/pull/953)
2. [CI] Fix flaky Ray Datasets integration test. [952](https://github.com/Eventual-Inc/Daft/pull/952)
3. [Ray Runner] Unfixing batch size for task awaiting [951](https://github.com/Eventual-Inc/Daft/pull/951)
4. Testing object related performance fixes [949](https://github.com/Eventual-Inc/Daft/pull/949)

Build Changes

1. [ci] [daft publish] pin urllib to < 2 for conda [950](https://github.com/Eventual-Inc/Daft/pull/950)

0.1.2

The Daft 0.1.2 release features performance improvements, bugfixes and some of our first Daft logical types!

New Features

Extension Types for Ray Runner and Embedding Logical Type

Adds our first “Logical Type”: Embeddings!

An Embedding is a “Logical Type” that encompasses a Fixed Size List. It is common in applications for Machine Learning and AI.

See: [929](https://github.com/Eventual-Inc/Daft/pull/929)

Enhancements

1. Use PyArrow filesystem for tabular file reads [939](https://github.com/Eventual-Inc/Daft/pull/939)
2. [I/O] Port to pyarrow filesystems by default. [942](https://github.com/Eventual-Inc/Daft/pull/942)
3. Memoize ray.get for batch metadata lookup [937](https://github.com/Eventual-Inc/Daft/pull/937)
4. [I/O] Expose user-provided fsspec filesystem arg in read APIs. [931](https://github.com/Eventual-Inc/Daft/pull/931)
5. Introduce Logical Arrays and SeriesLike Trait [920](https://github.com/Eventual-Inc/Daft/pull/920)
6. [Extension Types] Add support for cross-lang extension types. [899](https://github.com/Eventual-Inc/Daft/pull/899)

Bug Fixes

1. fix concats for extension array for old versions of pyarrow [944](https://github.com/Eventual-Inc/Daft/pull/944)

Build Changes

1. [ci] enable pyrunner for 310 [946](https://github.com/Eventual-Inc/Daft/pull/946)
2. Add Pyarrow 6.0 in matrix for CI testing [945](https://github.com/Eventual-Inc/Daft/pull/945)
3. Update requirement of tabulate to >=0.9.0 [940](https://github.com/Eventual-Inc/Daft/pull/940)
4. unpin numpy for 3.7 to get dependabot to stop complaining [938](https://github.com/Eventual-Inc/Daft/pull/938)
5. Bump slackapi/slack-github-action from 1.23.0 to 1.24.0 [936](https://github.com/Eventual-Inc/Daft/pull/936)
6. Bump hypothesis from 6.75.2 to 6.75.3 [928](https://github.com/Eventual-Inc/Daft/pull/928)
7. Bump dask from 2023.4.1 to 2023.5.0 [927](https://github.com/Eventual-Inc/Daft/pull/927)
8. Bump serde from 1.0.162 to 1.0.163 [921](https://github.com/Eventual-Inc/Daft/pull/921)

Documentation

1. Add comment to explain __future__ annotations isort rule in dataframe.py [947](https://github.com/Eventual-Inc/Daft/pull/947)
2. [Embedding tutorial] Suggest running on GPU cluster [932](https://github.com/Eventual-Inc/Daft/pull/932)
3. Embeddings tutorial [930](https://github.com/Eventual-Inc/Daft/pull/930)

0.1.1

The Daft 0.1.1 release provides bugfixes and stabilization fixes.

Enhancements

1. Enable and test writing temporal types [897](https://github.com/Eventual-Inc/Daft/pull/897)
2. Fix universal expressions on temporals [895](https://github.com/Eventual-Inc/Daft/pull/895)
3. [Arrow Types] Add automatic Python object fallback for unsupported Arrow types. [886](https://github.com/Eventual-Inc/Daft/pull/886)

Bug Fixes

1. Fix fsspec multithreading clobbering issue [898](https://github.com/Eventual-Inc/Daft/pull/898)
2. Fix temporal unit tests for older versions of pyarrow [919](https://github.com/Eventual-Inc/Daft/pull/919)
3. Fix colon URL downloads and default to strict mode for .url.download() [896](https://github.com/Eventual-Inc/Daft/pull/896)
4. [CI] Fix flaky Datasets integration test. [917](https://github.com/Eventual-Inc/Daft/pull/917)
5. Import daft in local benchmarking unit tests [887](https://github.com/Eventual-Inc/Daft/pull/887)
6. Fix imports in microbenchmarks [885](https://github.com/Eventual-Inc/Daft/pull/885)

Build Changes

1. enable python 3.10 unit tests [915](https://github.com/Eventual-Inc/Daft/pull/915)
2. Update pyo3 to 0.18.3 [914](https://github.com/Eventual-Inc/Daft/pull/914)
3. Bump serde from 1.0.160 to 1.0.162 [912](https://github.com/Eventual-Inc/Daft/pull/912)
4. Bump arrow2 from 0.17.0 to 0.17.1 [910](https://github.com/Eventual-Inc/Daft/pull/910)
5. Bump actions/upload-artifact from 2 to 3 [909](https://github.com/Eventual-Inc/Daft/pull/909)
6. Bump actions/download-artifact from 2 to 3 [907](https://github.com/Eventual-Inc/Daft/pull/907)
7. Bump actions/setup-python from 3 to 4 [906](https://github.com/Eventual-Inc/Daft/pull/906)
8. Enable dependabot for pip, cargo and github-actions [904](https://github.com/Eventual-Inc/Daft/pull/904)
9. pinned deps for requirements-dev.txt [903](https://github.com/Eventual-Inc/Daft/pull/903)

Documentation

1. Fix README.rst quickstart [925](https://github.com/Eventual-Inc/Daft/pull/925)
2. Fix typo: CSV -> Parquet [893](https://github.com/Eventual-Inc/Daft/pull/893)
3. Add initial technical architecture docs [890](https://github.com/Eventual-Inc/Daft/pull/890)
4. Fix 10-min tutorial link to colab [884](https://github.com/Eventual-Inc/Daft/pull/884)

0.1.0

UDFs now no longer require up-front declaration of which arguments have to be Expressions, and what input types they are passed in as (list, numpy, arrow etc). Instead:

1. Inputs are always passed in as daft.series.Series objects and users can now easily convert this to the format they care about using Series.to_pylist(), Series.to_numpy() etc.
2. Which inputs are going to be daft.series.Series vs Python objects is inferred at runtime by checking which arguments a user passes in are Expressions.

For more information, consult: [UDF User Guide](https://www.getdaft.io/projects/docs/en/latest/learn/user_guides/udf.html)

Typing

Our old typing APIs have changed - the definitive typing API is now found at daft.DataType.

If you are declaring types (for instance as return types for UDFs), you should now use the DataType.* constructor methods!

Input/Output APIs

Creation of DataFrames has been promoted to module-level functions!

Before:

python
from daft import DataFrame

df = DataFrame.read_csv(...)


After:

python
import daft

df = daft.read_csv(...)


This is a big improvement in useability (moving forward, Daft will try to make it as easy as possible to use us by just importing the top-level daft module).

For more information, please see: [API Documentation for Input/Output](https://www.getdaft.io/projects/docs/en/latest/api_docs/input_output.html).

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