pyspark-regression 4.1.0

A tool for regression testing Spark Dataframes in Python

pyspark-regression

pyspark-regression is a concise, no-nonsense library for regression testing between PySpark Dataframes.

For install instructions and API documentation, please visit https://forrest-bajbek.github.io/pyspark-regression/

What is a Regression Test?

A Regression Test ensures that changes to code only produce expected outcomes, introducing no new bugs. These tests are particularly challenging when working with database tables, as the result can be too large to visually inspect. When updating a SQL transformation, Data Engineers must ensure that no rows or columns were unintentionally altered, even if the table has hundreds columns and billions of rows.

pyspark-regression reduces the complexity of Regression Testing by implementing a clean Python API for running regression tests between DataFrames in Apache Spark.

Example

Consider the following table:

id	name	price
1	Taco	3.001
2	Burrito	6.50
3	flauta	7.50

Imagine you are a Data Engineer, and you want to change the underlying ETL so that:

1. The price for Tacos is rounded to 2 decimal places.
2. The name for Flautas is capitalized.

You make your changes, and the new table looks like this:

id	name	price
1	Taco	3.00
2	Burrito	6.50
3	Flauta	7.50

Running a regression test will help you confirm that the new ETL changed the data how you expected.

Let's create the old and new tables as dataframes so we can run a Regression Test:

from pyspark.sql import SparkSession
from pyspark.sql.types import *
from pyspark_regression import RegressionTest

spark = SparkSession.builder.getOrCreate()
spark.conf.set("spark.sql.shuffle.partitions", 1)

schema = StructType(
    [
        StructField("id", IntegerType()),
        StructField("name", StringType()),
        StructField("price", DoubleType()),
    ]
)

# The old data
df_old = spark.createDataFrame(
    [
        (1, 'Taco', 3.001),
        (2, 'Burrito', 6.50),
        (3, 'flauta', 7.50),
    ],
    schema=schema
)

# The new data
df_new = spark.createDataFrame(
    [
        (1, 'Taco', 3.00),  # Corrected price
        (2, 'Burrito', 6.50),
        (3, 'Flauta', 7.50),  # Corrected name
    ],
    schema=schema
)

regression_test = RegressionTest(
    df_old=df_old,
    df_new=df_new,
    pk='id',
)

RegressionTest() returns a Python class with properties that let you inspect the differences between dataframes. Most notably, the summary property prints a comprehensive analysis in Markdown.

>>> print(regression_test.summary)

# Regression Test: df
- run_id: de9bd4eb-5313-4057-badc-7322ee23b83b
- run_time: 2022-05-25 08:53:50.581283

## Result: **FAILURE**.
Printing Regression Report...

### Table stats
- Count records in old df: 3
- Count records in new df: 3
- Count pks in old df: 3
- Count pks in new df: 3

### Diffs
- Columns with diffs: {'name', 'price'}
- Number of records with diffs: 2 (%oT: 66.7%)

 Diff Summary:
| column_name   | data_type   | diff_category        |   count_record | count_record_%oT   |
|:--------------|:------------|:---------------------|---------------:|:-------------------|
| name          | string      | capitalization added |              1 | 33.3%              |
| price         | double      | rounding             |              1 | 33.3%              |

 Diff Samples: (5 samples per column_name, per diff_category, per is_duplicate)
| column_name   | data_type   |   pk | old_value   | new_value   | diff_category        |
|:--------------|:------------|-----:|:------------|:------------|:---------------------|
| name          | string      |    3 | 'flauta'    | 'Flauta'    | capitalization added |
| price         | double      |    1 | 3.001       | 3.0         | rounding             |

The RegressionTest class provides low level access to all the methods used to build the summary:

>>> print(regression_test.count_record_old) # count of records in df_old
3

>>> print(regression_test.count_record_new) # count of records in df_new
3

>>> print(regression_test.columns_diff) # Columns with diffs
{'name', 'price'}

>>> regression_test.df_diff.filter("column_name = 'price'").show() # Show all diffs for 'price' column
+-----------+---------+---+---------+---------+-------------+
|column_name|data_type| pk|old_value|new_value|diff_category|
+-----------+---------+---+---------+---------+-------------+
|      price|   double|  1|    3.001|      3.0|     rounding|
+-----------+---------+---+---------+---------+-------------+