Extend U-SQL scripts with Python code in Azure Data Lake Analytics
Before you begin, ensure the Python extensions are installed in your Azure Data Lake Analytics account.
- Navigate to you Data Lake Analytics Account in the Azure portal
- In the left menu, under GETTING STARTED click on Sample Scripts
- Click Install U-SQL Extensions then OK
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 ASSEMBLYstatement to enable Python extensions for the U-SQL Script
- Using the
REDUCEoperation 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_mainthat 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=='@' ) ) 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 D( 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
- 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
- Index vectors in Pandas are not 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 cannot have duplicate column names
- U-SQL datasets column names that are not strings.
Only Python 3.5.1 (compiled for Windows) is supported.
Standard Python modules
All the standard Python modules are included.
Additional Python modules
Besides the standard Python libraries, several commonly used python libraries are included:
pandas numpy numexpr
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 cannot exceed 6 GB.