Fetching Random Values from PySpark Arrays / Columns

This post shows you how to fetch a random value from a PySpark array or from a set of columns. It’ll also show you how to add a column to a DataFrame with a random value from a Python array and how to fetch n random values from a given column.

Random value from PySpark array

Suppose you have the following DataFrame:

|     letters|
|   [a, b, c]|
|[a, b, c, d]|
|         [x]|
|          []|

You can leverage the array_choice() function defined in quinn to append a random_letter column that fetches a random value from letters.

actual_df = df.withColumn(
|     letters|random_letter|
|   [a, b, c]|            c|
|[a, b, c, d]|            c|
|         [x]|            x|
|          []|         null|

Here’s how the array_choice() function is defined:

import pyspark.sql.functions as F

def array_choice(col):
    index = (F.rand()*F.size(col)).cast("int")
    return col[index]

Random value from columns

You can also use array_choice to fetch a random value from a list of columns. Suppose you have the following DataFrame:

|   1|   2|   3|
|   4|   5|   6|
|   7|   8|   9|
|  10|null|null|

Here’s the code to append a random_number column that selects a random value from num1, num2, or num3.

actual_df = df.withColumn(
    quinn.array_choice(F.array(F.col("num1"), F.col("num2"), F.col("num3")))
|   1|   2|   3|            1|
|   4|   5|   6|            4|
|   7|   8|   9|            8|
|  10|null|null|           10|
|null|null|null|         null|

The array function is used to convert the columns to an array, so the input is suitable for array_choice.

Random value from Python array

Suppose you’d like to add a random_animal column to an existing DataFrame that randomly selects between cat, dog, and mouse.

df = spark.createDataFrame([('jose',), ('maria',), (None,)], ['first_name'])
cols = list(map(lambda col_name: F.lit(col_name), ['cat', 'dog', 'mouse']))
actual_df = df.withColumn(
|      jose|          cat|
|     maria|        mouse|
|      null|          dog|

This tactic is useful when you’re creating fake datasets.

Study this code closely and make sure you’re comfortable with making a list of PySpark column objects (this line of code: cols = list(map(lambda col_name: F.lit(col_name), ['cat', 'dog', 'mouse']))). Manipulating lists of PySpark columns is useful when renaming multiple columns, when removing dots from column names and when changing column types. It’s an important design pattern for PySpark programmers to master.

N random values from a column

Suppose you’d like to get some random values from a PySpark column, as discussed here. Here’s a sample DataFrame:

| id|
| 12|

Here’s how to fetch three random values from the id column:

df.rdd.takeSample(False, 3)

Here’s how you get the result as an array of integers:

list(map(lambda row: row[0], df.rdd.takeSample(False, 3))) # => [123, 12, 245]

This code also works, but requires a full table sort which is expensive:


Examine the physical plan to verify that a full table sort is performed:


Here’s the physical plan that’s outputted:

TakeOrderedAndProject(limit=3, orderBy=[_nondeterministic#38 ASC NULLS FIRST], output=[id#32L])
+- *(1) Project [id#32L, rand(-4436287143488772163) AS _nondeterministic#38]

If your table is huge, then a full table sort will be slow.

Next steps

Feel free to copy / paste array_choice in your notebooks or depend on quinn to access this functionality.

Notebooks that don’t rely on open source / private code abstractions tend to be overly complex. Think about moving generic code like array_choice to codebases outside your notebook. Solving problems with PySpark is hard enough. Don’t make it harder by bogging down your notebooks with additional complexity.

Read the blog posts on creating a PySpark project with Poetry and testing PySpark code to learn more about PySpark best practices.

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