pyspark-val 0.1.4

PySpark validation & testing tooling

pyspark-test

PySpark validation & testing tooling.

Installation

pip install pyspark-val

Usage

assert_pyspark_df_equal(left_df, actual_df)

Additional Arguments

• check_dtype : To compare the data types of spark dataframe. Default true
• check_column_names : To compare column names. Default false. Not required of we are checking data types.
• check_columns_in_order : To check the columns should be in order or not. Default to false
• order_by : Column names with which dataframe must be sorted before comparing. Default None.

Example

import datetime

from pyspark import SparkContext
from pyspark.sql import SparkSession
from pyspark.sql.types import *

from pyspark_test import assert_pyspark_df_equal

sc = SparkContext.getOrCreate(conf=conf)
spark_session = SparkSession(sc)

df_1 = spark_session.createDataFrame(
    data=[
        [datetime.date(2020, 1, 1), 'demo', 1.123, 10],
        [None, None, None, None],
    ],
    schema=StructType(
        [
            StructField('col_a', DateType(), True),
            StructField('col_b', StringType(), True),
            StructField('col_c', DoubleType(), True),
            StructField('col_d', LongType(), True),
        ]
    ),
)

df_2 = spark_session.createDataFrame(
    data=[
        [datetime.date(2020, 1, 1), 'demo', 1.123, 10],
        [None, None, None, None],
    ],
    schema=StructType(
        [
            StructField('col_a', DateType(), True),
            StructField('col_b', StringType(), True),
            StructField('col_c', DoubleType(), True),
            StructField('col_d', LongType(), True),
        ]
    ),
)

assert_pyspark_df_equal(df_1, df_2)