Importing files and Python libraries to Amazon Athena for Apache Spark - Amazon Athena
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Importing files and Python libraries to Amazon Athena for Apache Spark

This document provides examples of how to import files and Python libraries to Amazon Athena for Apache Spark.

Considerations and Limitations

  • Python version – Currently, Athena for Spark uses Python version 3.9.16. Note that Python packages are sensitive to minor Python versions.

  • Athena for Spark architecture – Athena for Spark uses Amazon Linux 2 on ARM64 architecture. Note that some Python libraries do not distribute binaries for this architecture.

  • Binary shared objects (SOs) – Because the SparkContext addPyFile method does not detect binary shared objects, it cannot be used in Athena for Spark to add Python packages that depend on shared objects.

  • Resilient Distributed Datasets (RDDs)RDDs are not supported.

  • Dataframe.foreach – The PySpark DataFrame.foreach method is not supported.

Examples

The examples use the following conventions.

  • The placeholder Amazon S3 location s3://DOC-EXAMPLE-BUCKET. Replace this with your own S3 bucket location.

  • All code blocks that execute from a Unix shell are shown as directory_name $. For example, the command ls in the directory /tmp and its output are displayed as follows:

    /tmp $ ls

    Output

    file1 file2

Importing text files for use in calculations

The examples in this section show how to import text files for use in calculations in your notebooks in Athena for Spark.

Adding a file to a notebook after writing it to local temporary directory

The following example shows how to write a file to a local temporary directory, add it to a notebook, and test it.

import os from pyspark import SparkFiles tempdir = '/tmp/' path = os.path.join(tempdir, "test.txt") with open(path, "w") as testFile: _ = testFile.write("5") sc.addFile(path) def func(iterator): with open(SparkFiles.get("test.txt")) as testFile: fileVal = int(testFile.readline()) return [x * fileVal for x in iterator] #Test the file from pyspark.sql.functions import udf from pyspark.sql.functions import col udf_with_import = udf(func) df = spark.createDataFrame([(1, "a"), (2, "b")]) df.withColumn("col", udf_with_import(col('_2'))).show()

Output

Calculation completed. +---+---+-------+ | _1| _2| col| +---+---+-------+ | 1| a|[aaaaa]| | 2| b|[bbbbb]| +---+---+-------+

Importing a file from Amazon S3

The following example shows how to import a file from Amazon S3 into a notebook and test it.

To import a file from Amazon S3 into a notebook
  1. Create a file named test.txt that has a single line that contains the value 5.

  2. Add the file to a bucket in Amazon S3. This example uses the location s3://DOC-EXAMPLE-BUCKET.

  3. Use the following code to import the file to your notebook and test the file.

    from pyspark import SparkFiles sc.addFile('s3://DOC-EXAMPLE-BUCKET/test.txt') def func(iterator): with open(SparkFiles.get("test.txt")) as testFile: fileVal = int(testFile.readline()) return [x * fileVal for x in iterator] #Test the file from pyspark.sql.functions import udf from pyspark.sql.functions import col udf_with_import = udf(func) df = spark.createDataFrame([(1, "a"), (2, "b")]) df.withColumn("col", udf_with_import(col('_2'))).show()

    Output

    Calculation completed. +---+---+-------+ | _1| _2| col| +---+---+-------+ | 1| a|[aaaaa]| | 2| b|[bbbbb]| +---+---+-------+

Adding Python files

The examples in this section show how to add Python files and libraries to your Spark notebooks in Athena.

Adding Python files and registering a UDF

The following example shows how to add Python files from Amazon S3 to your notebook and register a UDF.

To add Python files to your notebook and register a UDF
  1. Using your own Amazon S3 location, create the file s3://DOC-EXAMPLE-BUCKET/file1.py with the following contents:

    def xyz(input): return 'xyz - udf ' + str(input);
  2. In the same S3 location, create the file s3://DOC-EXAMPLE-BUCKET/file2.py with the following contents:

    from file1 import xyz def uvw(input): return 'uvw -> ' + xyz(input);
  3. In your Athena for Spark notebook, run the following commands.

    sc.addPyFile('s3://DOC-EXAMPLE-BUCKET/file1.py') sc.addPyFile('s3://DOC-EXAMPLE-BUCKET/file2.py') def func(iterator): from file2 import uvw return [uvw(x) for x in iterator] from pyspark.sql.functions import udf from pyspark.sql.functions import col udf_with_import = udf(func) df = spark.createDataFrame([(1, "a"), (2, "b")]) df.withColumn("col", udf_with_import(col('_2'))).show(10)

    Output

    Calculation started (calculation_id=1ec09e01-3dec-a096-00ea-57289cdb8ce7) in (session=c8c09e00-6f20-41e5-98bd-4024913d6cee). Checking calculation status... Calculation completed. +---+---+--------------------+ | _1| _2| col| +---+---+--------------------+ | 1 | a|[uvw -> xyz - ud... | | 2 | b|[uvw -> xyz - ud... | +---+---+--------------------+

Importing a Python .zip file

You can use the Python addPyFile and import methods to import a Python .zip file to your notebook.

Note

The .zip files that you import to Athena Spark may include only Python packages. For example, including packages with C-based files is not supported.

To import a Python .zip file to your notebook
  1. On your local computer, in a desktop directory such as \tmp, create a directory called moduletest.

  2. In the moduletest directory, create a file named hello.py with the following contents:

    def hi(input): return 'hi ' + str(input);
  3. In the same directory, add an empty file with the name __init__.py.

    If you list the directory contents, they should now look like the following.

    /tmp $ ls moduletest __init__.py hello.py
  4. Use the zip command to place the two module files into a file called moduletest.zip.

    moduletest $ zip -r9 ../moduletest.zip *
  5. Upload the .zip file to your bucket in Amazon S3.

  6. Use the following code to import the Python.zip file into your notebook.

    sc.addPyFile('s3://DOC-EXAMPLE-BUCKET/moduletest.zip') from moduletest.hello import hi from pyspark.sql.functions import udf from pyspark.sql.functions import col hi_udf = udf(hi) df = spark.createDataFrame([(1, "a"), (2, "b")]) df.withColumn("col", hi_udf(col('_2'))).show()

    Output

    Calculation started (calculation_id=6ec09e8c-6fe0-4547-5f1b-6b01adb2242c) in (session=dcc09e8c-3f80-9cdc-bfc5-7effa1686b76). Checking calculation status... Calculation completed. +---+---+----+ | _1| _2| col| +---+---+----+ | 1| a|hi a| | 2| b|hi b| +---+---+----+

Importing two versions of a Python library as separate modules

The following code examples show how to add and import two different versions of a Python library from a location in Amazon S3 as two separate modules. The code adds each the library file from S3, imports it, and then prints the library version to verify the import.

sc.addPyFile('s3://DOC-EXAMPLE-BUCKET/python-third-party-libs-test/simplejson_v3_15.zip') sc.addPyFile('s3://DOC-EXAMPLE-BUCKET/python-third-party-libs-test/simplejson_v3_17_6.zip') import simplejson_v3_15 print(simplejson_v3_15.__version__)

Output

3.15.0
import simplejson_v3_17_6 print(simplejson_v3_17_6.__version__)

Output

3.17.6

Importing a Python .zip file from PyPI

This example uses the pip command to download a Python .zip file of the bpabel/piglatin project from the Python Package Index (PyPI).

To import a Python .zip file from PyPI
  1. On your local desktop, use the following commands to create a directory called testpiglatin and create a virtual environment.

    /tmp $ mkdir testpiglatin /tmp $ cd testpiglatin testpiglatin $ virtualenv .

    Output

    created virtual environment CPython3.9.6.final.0-64 in 410ms creator CPython3Posix(dest=/private/tmp/testpiglatin, clear=False, no_vcs_ignore=False, global=False) seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/Users/user1/Library/Application Support/virtualenv) added seed packages: pip==22.0.4, setuptools==62.1.0, wheel==0.37.1 activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator
  2. Create a subdirectory named unpacked to hold the project.

    testpiglatin $ mkdir unpacked
  3. Use the pip command to install the project into the unpacked directory.

    testpiglatin $ bin/pip install -t $PWD/unpacked piglatin

    Output

    Collecting piglatin Using cached piglatin-1.0.6-py2.py3-none-any.whl (3.1 kB) Installing collected packages: piglatin Successfully installed piglatin-1.0.6
  4. Check the contents of the directory.

    testpiglatin $ ls

    Output

    bin lib pyvenv.cfg unpacked
  5. Change to the unpacked directory and display the contents.

    testpiglatin $ cd unpacked unpacked $ ls

    Output

    piglatin piglatin-1.0.6.dist-info
  6. Use the zip command to place the contents of the piglatin project into a file called library.zip.

    unpacked $ zip -r9 ../library.zip *

    Output

    adding: piglatin/ (stored 0%) adding: piglatin/__init__.py (deflated 56%) adding: piglatin/__pycache__/ (stored 0%) adding: piglatin/__pycache__/__init__.cpython-39.pyc (deflated 31%) adding: piglatin-1.0.6.dist-info/ (stored 0%) adding: piglatin-1.0.6.dist-info/RECORD (deflated 39%) adding: piglatin-1.0.6.dist-info/LICENSE (deflated 41%) adding: piglatin-1.0.6.dist-info/WHEEL (deflated 15%) adding: piglatin-1.0.6.dist-info/REQUESTED (stored 0%) adding: piglatin-1.0.6.dist-info/INSTALLER (stored 0%) adding: piglatin-1.0.6.dist-info/METADATA (deflated 48%)
  7. (Optional) Use the following commands to test the import locally.

    1. Set the Python path to the library.zip file location and start Python.

      /home $ PYTHONPATH=/tmp/testpiglatin/library.zip /home $ python3

      Output

      Python 3.9.6 (default, Jun 29 2021, 06:20:32) [Clang 12.0.0 (clang-1200.0.32.29)] on darwin Type "help", "copyright", "credits" or "license" for more information.
    2. Import the library and run a test command.

      >>> import piglatin >>> piglatin.translate('hello')

      Output

      'ello-hay'
  8. Use commands like the following to add the .zip file from Amazon S3, import it into your notebook in Athena, and test it.

    sc.addPyFile('s3://DOC-EXAMPLE-BUCKET/library.zip') import piglatin piglatin.translate('hello') from pyspark.sql.functions import udf from pyspark.sql.functions import col hi_udf = udf(piglatin.translate) df = spark.createDataFrame([(1, "hello"), (2, "world")]) df.withColumn("col", hi_udf(col('_2'))).show()

    Output

    Calculation started (calculation_id=e2c0a06e-f45d-d96d-9b8c-ff6a58b2a525) in (session=82c0a06d-d60e-8c66-5d12-23bcd55a6457). Checking calculation status... Calculation completed. +---+-----+--------+ | _1| _2| col| +---+-----+--------+ | 1|hello|ello-hay| | 2|world|orld-way| +---+-----+--------+

Importing a Python .zip file from PyPI that has dependencies

This example imports the md2gemini package, which converts text in markdown to Gemini text format, from PyPI. The package has the following dependencies:

cjkwrap mistune wcwidth
To import a Python .zip file that has dependencies
  1. On your local computer, use the following commands to create a directory called testmd2gemini and create a virtual environment.

    /tmp $ mkdir testmd2gemini /tmp $ cd testmd2gemini testmd2gemini$ virtualenv .
  2. Create a subdirectory named unpacked to hold the project.

    testmd2gemini $ mkdir unpacked
  3. Use the pip command to install the project into the unpacked directory.

    /testmd2gemini $ bin/pip install -t $PWD/unpacked md2gemini

    Output

    Collecting md2gemini Downloading md2gemini-1.9.0-py3-none-any.whl (31 kB) Collecting wcwidth Downloading wcwidth-0.2.5-py2.py3-none-any.whl (30 kB) Collecting mistune<3,>=2.0.0 Downloading mistune-2.0.2-py2.py3-none-any.whl (24 kB) Collecting cjkwrap Downloading CJKwrap-2.2-py2.py3-none-any.whl (4.3 kB) Installing collected packages: wcwidth, mistune, cjkwrap, md2gemini Successfully installed cjkwrap-2.2 md2gemini-1.9.0 mistune-2.0.2 wcwidth-0.2.5 ...
  4. Change to the unpacked directory and check the contents.

    testmd2gemini $ cd unpacked unpacked $ ls -lah

    Output

    total 16 drwxr-xr-x 13 user1 wheel 416B Jun 7 18:43 . drwxr-xr-x 8 user1 wheel 256B Jun 7 18:44 .. drwxr-xr-x 9 user1 staff 288B Jun 7 18:43 CJKwrap-2.2.dist-info drwxr-xr-x 3 user1 staff 96B Jun 7 18:43 __pycache__ drwxr-xr-x 3 user1 staff 96B Jun 7 18:43 bin -rw-r--r-- 1 user1 staff 5.0K Jun 7 18:43 cjkwrap.py drwxr-xr-x 7 user1 staff 224B Jun 7 18:43 md2gemini drwxr-xr-x 10 user1 staff 320B Jun 7 18:43 md2gemini-1.9.0.dist-info drwxr-xr-x 12 user1 staff 384B Jun 7 18:43 mistune drwxr-xr-x 8 user1 staff 256B Jun 7 18:43 mistune-2.0.2.dist-info drwxr-xr-x 16 user1 staff 512B Jun 7 18:43 tests drwxr-xr-x 10 user1 staff 320B Jun 7 18:43 wcwidth drwxr-xr-x 9 user1 staff 288B Jun 7 18:43 wcwidth-0.2.5.dist-info
  5. Use the zip command to place the contents of the md2gemini project into a file called md2gemini.zip.

    unpacked $ zip -r9 ../md2gemini *

    Output

    adding: CJKwrap-2.2.dist-info/ (stored 0%) adding: CJKwrap-2.2.dist-info/RECORD (deflated 37%) .... adding: wcwidth-0.2.5.dist-info/INSTALLER (stored 0%) adding: wcwidth-0.2.5.dist-info/METADATA (deflated 62%)
  6. (Optional) Use the following commands to test that the library works on your local computer.

    1. Set the Python path to the md2gemini.zip file location and start Python.

      /home $ PYTHONPATH=/tmp/testmd2gemini/md2gemini.zip /home python3
    2. Import the library and run a test.

      >>> from md2gemini import md2gemini >>> print(md2gemini('[abc](https://abc.def)'))

      Output

      https://abc.def abc
  7. Use the following commands to add the .zip file from Amazon S3, import it into your notebook in Athena, and perform a non UDF test.

    # (non udf test) sc.addPyFile('s3://DOC-EXAMPLE-BUCKET/md2gemini.zip') from md2gemini import md2gemini print(md2gemini('[abc](https://abc.def)'))

    Output

    Calculation started (calculation_id=0ac0a082-6c3f-5a8f-eb6e-f8e9a5f9bc44) in (session=36c0a082-5338-3755-9f41-0cc954c55b35). Checking calculation status... Calculation completed. => https://abc.def (https://abc.def/) abc
  8. Use the following commands to perform a UDF test.

    # (udf test) from pyspark.sql.functions import udf from pyspark.sql.functions import col from md2gemini import md2gemini hi_udf = udf(md2gemini) df = spark.createDataFrame([(1, "[first website](https://abc.def)"), (2, "[second website](https://aws.com)")]) df.withColumn("col", hi_udf(col('_2'))).show()

    Output

    Calculation started (calculation_id=60c0a082-f04d-41c1-a10d-d5d365ef5157) in (session=36c0a082-5338-3755-9f41-0cc954c55b35). Checking calculation status... Calculation completed. +---+--------------------+--------------------+ | _1| _2| col| +---+--------------------+--------------------+ | 1|[first website](h...|=> https://abc.de...| | 2|[second website](...|=> https://aws.co...| +---+--------------------+--------------------+