pyspark-data-mocker 4.0.0

Mock a datalake easily to be able to test your pyspark data application

pyspark-data-mocker

pyspark-data-mocker is a testing tool that facilitates the burden of setting up a desired datalake, so you can test easily the behavior of your data application. It configures also the spark session to optimize it for testing purpose.

Install

pip install pyspark-data-mocker

Usage

pyspark-data-mocker searches the directory you provide in order to seek and load files that can be interpreted as tables, storing them inside the datalake. That datalake will contain certain databases depending on the folders inside the root directory. For example, let's take a look into the basic_datalake

$ tree tests/data/basic_datalake -n --charset=ascii  # byexample: +rm=~ +skip
tests/data/basic_datalake
|-- grades
|   `-- exams.csv
`-- school
    |-- courses.csv
    `-- students.csv
~
2 directories, 3 files

This file hierarchy will be respected in the further datalake when loaded: each sub-folder will be considered as spark database, and each file will be loaded as table, using the filename to name the table.

How can we load them using pyspark-data-mocker? Really simple!

>>> from pyspark_data_mocker import DataLakeBuilder
>>> builder = DataLakeBuilder().load_from_dir("./tests/data/basic_datalake")  # byexample: +timeout=20 +pass

And that's it! you will now have in that execution context a datalake with the structure defined in the folder basic_datalake. Let's take a closer look by running some queries.

>>> from pyspark.sql import SparkSession
>>> spark = SparkSession.builder.getOrCreate()
>>> spark.sql("SHOW DATABASES").show()
+---------+
|namespace|
+---------+
|  default|
|   grades|
|   school|
+---------+

We have the default database (which came for free when instantiating spark), and the two folders inside tests/data/basic_datalake: school and grades.

>>> spark.sql("SHOW TABLES IN school").show()
+---------+---------+-----------+
|namespace|tableName|isTemporary|
+---------+---------+-----------+
|   school|  courses|      false|
|   school| students|      false|
+---------+---------+-----------+

>>> spark.sql("SELECT * FROM school.courses").show()
+---+------------+
| id| course_name|
+---+------------+
|  1|Algorithms 1|
|  2|Algorithms 2|
|  3|  Calculus 1|
+---+------------+


>>> spark.table("school.students").show()
+---+----------+---------+--------------------+------+----------+
| id|first_name|last_name|               email|gender|birth_date|
+---+----------+---------+--------------------+------+----------+
|  1|  Shirleen|  Dunford|sdunford0@amazona...|Female|1978-08-01|
|  2|      Niko|  Puckrin|npuckrin1@shinyst...|  Male|2000-11-28|
|  3|    Sergei|   Barukh|sbarukh2@bizjourn...|  Male|1992-01-20|
|  4|       Sal|  Maidens|[e-mail]|  Male|2003-12-14|
|  5|    Cooper|MacGuffie| [e-mail]|  Male|2000-03-07|
+---+----------+---------+--------------------+------+----------+

Note how it is already filled with the data each CSV file has! The tool supports all kind of files: csv, parquet, json. The application will infer which format to use by looking the file extension.

>>> spark.sql("SHOW TABLES IN grades").show()
+---------+---------+-----------+
|namespace|tableName|isTemporary|
+---------+---------+-----------+
|   grades|    exams|      false|
+---------+---------+-----------+

>>> spark.table("grades.exams").show()
+---+----------+---------+----------+----+
| id|student_id|course_id|      date|note|
+---+----------+---------+----------+----+
|  1|         1|        1|2022-05-01|   9|
|  2|         2|        1|2022-05-08|   7|
|  3|         3|        1|2022-06-17|   4|
|  4|         1|        3|2023-05-12|   9|
|  5|         2|        3|2023-05-12|  10|
|  6|         3|        3|2022-12-07|   7|
|  7|         4|        3|2022-12-07|   4|
|  8|         5|        3|2022-12-07|   2|
|  9|         1|        2|2023-05-01|   5|
| 10|         2|        2|2023-05-07|   8|
+---+----------+---------+----------+----+

Cleanup

You can easily clean the datalake by using the cleanup function

>>> builder.cleanup()
>>> spark.sql("SHOW DATABASES").show()
+---------+
|namespace|
+---------+
|  default|
+---------+

Documentation

You can check the full documentation to use all features available in pyspark-data-mocker here