User-Defined Functions (UDF)#

A key piece of functionality in DaFt is the ability to flexibly define custom functions that can run on any data in your dataframe. This guide walks you through the different types of UDFs that DaFt allows you to run.

Let’s first create a dataframe that will be used as a running example throughout this tutorial!

import daft
import numpy as np

df = daft.from_pydict({
    # the `image` column contains images represented as 2D numpy arrays
    "image": [np.ones((128, 128)) for i in range(16)],
    # the `crop` column contains a box to crop from our image, represented as a list of integers: [x1, x2, y1, y2]
    "crop": [[0, 1, 0, 1] for i in range(16)],
})
df

+----------+---------------+
| image    | crop          |
| Python   | List[Int64]   |
+==========+===============+
+----------+---------------+
(No data to display: Dataframe not materialized)

Per-column per-row functions using `.apply`#

You can use .apply to run a Python function on every row in a column.

For example, the following example creates a new "flattened_image" column by calling .flatten() on every object in the "image" column.

df.with_column(
    "flattened_image",
    df["image"].apply(lambda img: img.flatten(), return_dtype=daft.DataType.python())
).show(2)

+----------------------+---------------+---------------------+
| image                | crop          | flattened_image     |
| Python               | List[Int64]   | Python              |
+======================+===============+=====================+
| [[1. 1. 1. ... 1. 1. | [0, 1, 0, 1]  | [1. 1. 1. ... 1. 1. |
| 1.]  [1. 1. 1. ...   |               | 1.]                 |
| 1. 1. 1.]  [1. 1.... |               |                     |
+----------------------+---------------+---------------------+
| [[1. 1. 1. ... 1. 1. | [0, 1, 0, 1]  | [1. 1. 1. ... 1. 1. |
| 1.]  [1. 1. 1. ...   |               | 1.]                 |
| 1. 1. 1.]  [1. 1.... |               |                     |
+----------------------+---------------+---------------------+
(Showing first 2 rows)

Note here that we use the return_dtype keyword argument to specify that our returned column type is a Python column!

Multi-column per-partition functions using `@udf`#

.apply is great for convenience, but has two main limitations:

It can only run on single columns
It can only run on single items at a time

Daft provides the @udf decorator for defining your own UDFs that process multiple columns or multiple rows at a time.

For example, let’s try writing a function that will crop all our images in the "image" column by its corresponding value in the "crop" column:

@daft.udf(return_dtype=daft.DataType.python())
def crop_images(images, crops, padding=0):
    cropped = []
    for img, crop in zip(images.to_pylist(), crops.to_pylist()):
        x1, x2, y1, y2 = crop
        cropped_img = img[x1:x2 + padding, y1:y2 + padding]
        cropped.append(cropped_img)
    return cropped

df = df.with_column(
    "cropped",
    crop_images(df["image"], df["crop"], padding=1),
)
df.show(2)

+----------------------+---------------+--------------------+
| image                | crop          | cropped            |
| Python               | List[Int64]   | Python             |
+======================+===============+====================+
| [[1. 1. 1. ... 1. 1. | [0, 1, 0, 1]  | [[1. 1.]  [1. 1.]] |
| 1.]  [1. 1. 1. ...   |               |                    |
| 1. 1. 1.]  [1. 1.... |               |                    |
+----------------------+---------------+--------------------+
| [[1. 1. 1. ... 1. 1. | [0, 1, 0, 1]  | [[1. 1.]  [1. 1.]] |
| 1.]  [1. 1. 1. ...   |               |                    |
| 1. 1. 1.]  [1. 1.... |               |                    |
+----------------------+---------------+--------------------+
(Showing first 2 rows)

There’s a few things happening here, let’s break it down:

crop_images is a normal Python function. It takes as input:
1. A list of images: images
2. A list of cropping boxes: crops
3. An integer indicating how much padding to apply to the right and bottom of the cropping: padding
To allow Daft to pass column data into the images and crops arguments, we decorate the function with @udf
1. return_dtype defines the returned data type. In this case, we return a column containing Python objects of numpy arrays
2. At runtime, because we call the UDF on the "image" and "crop" columns, the UDF will receive a daft.series.Series object for each argument.
We can create a new column in our DataFrame by applying our UDF on the "image" and "crop" columns inside of a df.with_column() call.

UDF Inputs#

When you specify an Expression as an input to a UDF, Daft will calculate the result of that Expression and pass it into your function as a daft.Series object.

The Daft Series is just an abstraction on a “column” of data! You can obtain several different data representations from a Series:

PyArrow Arrays (pa.Array): s.to_arrow()
Python lists (list): s.to_pylist()

Depending on your application, you may choose a different data representation that is more performant or more convenient!

Note

Certain array formats have some restrictions around the type of data that they can handle:

1. Null Handling: In Pandas and Numpy, nulls are represented as NaNs for numeric types, and Nones for non-numeric types. Additionally, the existence of nulls will trigger a type casting from integer to float arrays. If null handling is important to your use-case, we recommend using one of the other available options.

Python Objects: PyArrow array formats cannot support Python columns.

We recommend using Python lists if performance is not a major consideration, and using the arrow-native formats such as PyArrow arrays and numpy arrays if performance is important.

Return Types#

The return_dtype argument specifies what type of column your UDF will return. Types can be specified using the daft.DataType class.

Your UDF function itself needs to return a batch of columnar data, and can do so as any one of the following array types:

Numpy Arrays (np.ndarray)
PyArrow Arrays (pa.Array)
Python lists (list)

Note that if the data you have returned is not castable to the return_dtype that you specify (e.g. if you return a list of floats when you’ve specified a return_dtype=DataType.bool()), Daft will throw a runtime error!

Stateful UDFs#

UDFs can also be created on Classes, which allow for initialization on some expensive state that can be shared between invocations of the class, for example downloading data or creating a model.

@daft.udf(return_dtype=daft.DataType.int64())
class RunModel:

    def __init__(self):
        # Perform expensive initializations
        self._model = create_model()

    def __call__(self, features_col):
        return self._model(features_col)

Running Stateful UDFs are exactly the same as running their Stateless cousins.

df = df.with_column("image_classifications", RunModel(df["images"]))

Resource Requests#

Sometimes, you may want to request for specific resources for your UDF. For example, some UDFs need one GPU to run as they will load a model onto the GPU.

Custom resources can be requested when you call df.with_column():

from daft import ResourceRequest

# Runs the UDF `func` with the specified resource requests
df = df.with_column(
    "image_classifications",
    RunModel(df["images"]),
    resource_request=ResourceRequest(num_gpus=1, num_cpus=8),
)

In the above example, if ran Daft on a Ray cluster consisting of 8 GPUs and 64 CPUs, Daft would be able to run 8 replicas of your UDF in parallel, thus massively increasing the throughput of your UDF!

User-Defined Functions (UDF)

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