测试PySpark

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 相关文章：

1. PySpark 概述
2. Spark连接快速入门
3. Spark上使用pandas API快速入门

这篇文章旨在帮你写出健壮的pyspark 代码。

在这里，通过它写pyspark单元测试，看这个代码通过PySpark built，下载该目录代码，查看JIRA 看板票的pyspark测试

创建PySpark应用

这边一个例子是怎么创建pyspark应用，如果你的应用已经测试，你可以跳过这一段，测试你的pyspark程序。

现在，开始测试你的spark session

from pyspark.sql import SparkSession
from pyspark.sql.functions import col
 
# Create a SparkSession
spark = SparkSession.builder.appName("Testing PySpark Example").getOrCreate()

接下来，创建一个DataFrame

sample_data = [{"name": "John    D.", "age": 30},
  {"name": "Alice   G.", "age": 25},
  {"name": "Bob  T.", "age": 35},
  {"name": "Eve   A.", "age": 28}]
 
df = spark.createDataFrame(sample_data)

现在，我们对我们的DataFrame来定义转换算子

from pyspark.sql.functions import col, regexp_replace
 
# Remove additional spaces in name
def remove_extra_spaces(df, column_name):
    # Remove extra spaces from the specified column
    df_transformed = df.withColumn(column_name, regexp_replace(col(column_name), "\\s+", " "))
 
    return df_transformed
 
transformed_df = remove_extra_spaces(df, "name")
 
transformed_df.show()

+---+--------+
|age|    name|
+---+--------+
| 30| John D.|
| 25|Alice G.|
| 35|  Bob T.|
| 28|  Eve A.|
+---+--------+

测试你的pyspark应用

现在来测试你的pyspark转换算子。一个选择简化DataFrame测试结果，可以简化数据或者输入数据。更好的方式写测试例子，这里有一些例子怎么去测试我们的代码，这些代码是基于spark 3.5以下版本。对于这些例子做笔记是非常值得的，可以通过测试框架，不管你是使用unittest or pytest； built-in PySpark 测试是单机的，意味着他兼容测试框架和CI测试

选项1： 仅仅使用PySpark Built-in 测试方法

import pyspark.testing
from pyspark.testing.utils import assertDataFrameEqual
 
# Example 1
df1 = spark.createDataFrame(data=[("1", 1000), ("2", 3000)], schema=["id", "amount"])
df2 = spark.createDataFrame(data=[("1", 1000), ("2", 3000)], schema=["id", "amount"])
assertDataFrameEqual(df1, df2)  # pass, DataFrames are identical

# Example 2
df1 = spark.createDataFrame(data=[("1", 0.1), ("2", 3.23)], schema=["id", "amount"])
df2 = spark.createDataFrame(data=[("1", 0.109), ("2", 3.23)], schema=["id", "amount"])
assertDataFrameEqual(df1, df2, rtol=1e-1)  # pass, DataFrames are approx equal by rtol

 您还可以简单地比较两个 DataFrame 模式：

from pyspark.testing.utils import assertSchemaEqual
from pyspark.sql.types import StructType, StructField, ArrayType, DoubleType
 
s1 = StructType([StructField("names", ArrayType(DoubleType(), True), True)])
s2 = StructType([StructField("names", ArrayType(DoubleType(), True), True)])
 
assertSchemaEqual(s1, s2)  # pass, schemas are identical

选项 2：使用单元测试

对于更复杂的测试场景，您可能需要使用测试框架。

最流行的测试框架选项之一是单元测试。让我们逐步了解如何使用内置 Pythonunittest库来编写 PySpark 测试。有关该unittest库的更多信息，请参阅此处： https: //docs.python.org/3/library/unittest.html。

首先，您需要一个 Spark 会话。您可以使用包@classmethod中的装饰器unittest来负责设置和拆除 Spark 会话。

import unittest
 
class PySparkTestCase(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.spark = SparkSession.builder.appName("Testing PySpark Example").getOrCreate()
 
 
    @classmethod
    def tearDownClass(cls):
        cls.spark.stop()

 现在我们来写一个unittest类。

from pyspark.testing.utils import assertDataFrameEqual
 
class TestTranformation(PySparkTestCase):
    def test_single_space(self):
        sample_data = [{"name": "John    D.", "age": 30},
                       {"name": "Alice   G.", "age": 25},
                       {"name": "Bob  T.", "age": 35},
                       {"name": "Eve   A.", "age": 28}]
 
        # Create a Spark DataFrame
        original_df = spark.createDataFrame(sample_data)
 
        # Apply the transformation function from before
        transformed_df = remove_extra_spaces(original_df, "name")
 
        expected_data = [{"name": "John D.", "age": 30},
        {"name": "Alice G.", "age": 25},
        {"name": "Bob T.", "age": 35},
        {"name": "Eve A.", "age": 28}]
 
        expected_df = spark.createDataFrame(expected_data)
 
        assertDataFrameEqual(transformed_df, expected_df)

运行时，unittest将选取名称以“test”开头的所有函数。

import pytest
 
@pytest.fixture
def spark_fixture():
    spark = SparkSession.builder.appName("Testing PySpark Example").getOrCreate()
    yield spark

import pytest
from pyspark.testing.utils import assertDataFrameEqual
 
def test_single_space(spark_fixture):
    sample_data = [{"name": "John    D.", "age": 30},
                   {"name": "Alice   G.", "age": 25},
                   {"name": "Bob  T.", "age": 35},
                   {"name": "Eve   A.", "age": 28}]
 
    # Create a Spark DataFrame
    original_df = spark.createDataFrame(sample_data)
 
    # Apply the transformation function from before
    transformed_df = remove_extra_spaces(original_df, "name")
 
    expected_data = [{"name": "John D.", "age": 30},
    {"name": "Alice G.", "age": 25},
    {"name": "Bob T.", "age": 35},
    {"name": "Eve A.", "age": 28}]
 
    expected_df = spark.createDataFrame(expected_data)
 
    assertDataFrameEqual(transformed_df, expected_df)

# pkg/etl.py
import unittest
 
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from pyspark.sql.functions import regexp_replace
from pyspark.testing.utils import assertDataFrameEqual
 
# Create a SparkSession
spark = SparkSession.builder.appName("Sample PySpark ETL").getOrCreate()
 
sample_data = [{"name": "John    D.", "age": 30},
  {"name": "Alice   G.", "age": 25},
  {"name": "Bob  T.", "age": 35},
  {"name": "Eve   A.", "age": 28}]
 
df = spark.createDataFrame(sample_data)
 
# Define DataFrame transformation function
def remove_extra_spaces(df, column_name):
    # Remove extra spaces from the specified column using regexp_replace
    df_transformed = df.withColumn(column_name, regexp_replace(col(column_name), "\\s+", " "))
 
    return df_transformed

# pkg/test_etl.py
import unittest
 
from pyspark.sql import SparkSession
 
# Define unit test base class
class PySparkTestCase(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.spark = SparkSession.builder.appName("Sample PySpark ETL").getOrCreate()
 
    @classmethod
    def tearDownClass(cls):
        cls.spark.stop()
 
# Define unit test
class TestTranformation(PySparkTestCase):
    def test_single_space(self):
        sample_data = [{"name": "John    D.", "age": 30},
                        {"name": "Alice   G.", "age": 25},
                        {"name": "Bob  T.", "age": 35},
                        {"name": "Eve   A.", "age": 28}]
 
        # Create a Spark DataFrame
        original_df = spark.createDataFrame(sample_data)
 
        # Apply the transformation function from before
        transformed_df = remove_extra_spaces(original_df, "name")
 
        expected_data = [{"name": "John D.", "age": 30},
        {"name": "Alice G.", "age": 25},
        {"name": "Bob T.", "age": 35},
        {"name": "Eve A.", "age": 28}]
 
        expected_df = spark.createDataFrame(expected_data)
 
        assertDataFrameEqual(transformed_df, expected_df)

unittest.main(argv=[''], verbosity=0, exit=False)

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