Extend U-SQL scripts with Python code in Azure Data Lake Analytics

Article
12/20/2023

Important

Azure Data Lake Analytics retired on 29 February 2024. Learn more with this announcement.

For data analytics, your organization can use Azure Synapse Analytics or Microsoft Fabric.

Prerequisites

Before you begin, ensure the Python extensions are installed in your Azure Data Lake Analytics account.

Navigate to your Data Lake Analytics Account in the Azure portal
In the left menu, under GETTING STARTED select Sample Scripts
Select Install U-SQL Extensions then OK

Overview

Python Extensions for U-SQL enable developers to perform massively parallel execution of Python code. The following example illustrates the basic steps:

Use the REFERENCE ASSEMBLY statement to enable Python extensions for the U-SQL Script
Using the REDUCE operation to partition the input data on a key
The Python extensions for U-SQL include a built-in reducer (Extension.Python.Reducer) that runs Python code on each vertex assigned to the reducer
The U-SQL script contains the embedded Python code that has a function called usqlml_main that accepts a pandas DataFrame as input and returns a pandas DataFrame as output.

REFERENCE ASSEMBLY [ExtPython];
DECLARE @myScript = @"
def get_mentions(tweet):
    return ';'.join( ( w[1:] for w in tweet.split() if w[0]=='@' ) )
def usqlml_main(df):
    del df['time']
    del df['author']
    df['mentions'] = df.tweet.apply(get_mentions)
    del df['tweet']
    return df
";
@t  =
    SELECT * FROM
       (VALUES
           ("D1","T1","A1","@foo Hello World @bar"),
           ("D2","T2","A2","@baz Hello World @beer")
       ) AS date, time, author, tweet );
@m  =
    REDUCE @t ON date
    PRODUCE date string, mentions string
    USING new Extension.Python.Reducer(pyScript:@myScript);
OUTPUT @m
    TO "/tweetmentions.csv"
    USING Outputters.Csv();

How Python Integrates with U-SQL

Datatypes

String and numeric columns from U-SQL are converted as-is between Pandas and U-SQL
U-SQL Nulls are converted to and from Pandas NA values

Schemas

Index vectors in Pandas aren't supported in U-SQL. All input data frames in the Python function always have a 64-bit numerical index from 0 through the number of rows minus 1.
U-SQL datasets can't have duplicate column names
U-SQL datasets column names that aren't strings.

Python Versions

Only Python 3.5.1 (compiled for Windows) is supported.

Standard Python modules

All the standard Python modules are included.

More Python modules

Besides the standard Python libraries, several commonly used Python libraries are included:

pandas
numpy
numexpr

Exception Messages

Currently, an exception in Python code shows up as generic vertex failure. In the future, the U-SQL Job error messages will display the Python exception message.

Input and Output size limitations

Every vertex has a limited amount of memory assigned to it. Currently, that limit is 6 GB for an AU. Because the input and output DataFrames must exist in memory in the Python code, the total size for the input and output can't exceed 6 GB.

Share via