How to Filter Spark DataFrame Using Another DataFrame with Denylist Criteria?

When working with Apache Spark, you may encounter situations where you need to filter a DataFrame based on criteria defined in another DataFrame. This is often referred to as using a “denylist” or “blacklist” criteria. Let’s dive into how you can achieve this using PySpark.

Steps to Filter a DataFrame Using a Denylist

To demonstrate, we’ll use two DataFrames:

1. A primary DataFrame that contains the data to be filtered.
2. A “denylist” DataFrame containing the criteria for filtering the primary DataFrame.

We’ll proceed with the following steps:

1. Initialize SparkSession.
2. Create sample data for both DataFrames.
3. Perform an anti join operation to filter out the denylisted rows.

Step 1: Initialize SparkSession

First, you need to initialize a SparkSession:

from pyspark.sql import SparkSession

# Initialize SparkSession
spark = SparkSession.builder \
    .appName("Filter DataFrame using Denylist") \
    .getOrCreate()

Step 2: Create Sample DataFrames

Let’s assume the primary DataFrame contains user information, and the denylist DataFrame contains user IDs that should be excluded from the primary DataFrame:

# Sample data for primary DataFrame
primary_data = [
    (1, "John Doe", "john.doe@example.com"),
    (2, "Jane Smith", "jane.smith@example.com"),
    (3, "Alice Johnson", "alice.johnson@example.com"),
    (4, "Bob Brown", "bob.brown@example.com")
]

# Sample data for denylist DataFrame
denylist_data = [
    (2,),  # User ID 2 should be excluded
    (4,)   # User ID 4 should be excluded
]

# Create DataFrames
primary_df = spark.createDataFrame(primary_data, ["user_id", "name", "email"])
denylist_df = spark.createDataFrame(denylist_data, ["user_id"])

# Show Primary DataFrame
primary_df.show()

# Show Denylist DataFrame
denylist_df.show()

Output:

+-------+-------------+----------------------+
|user_id|         name|                 email|
+-------+-------------+----------------------+
|      1|     John Doe|   john.doe@example.com|
|      2|   Jane Smith|   jane.smith@example.com|
|      3| Alice Johnson|alice.johnson@example.com|
|      4|     Bob Brown|    bob.brown@example.com|
+-------+-------------+----------------------+

+-------+
|user_id|
+-------+
|      2|
|      4|
+-------+

Step 3: Filter the Primary DataFrame Using Anti Join

To filter the rows in the primary DataFrame based on the denylist, you can use the `join` operation with `how=’anti’`:

# Filter primary DataFrame using anti join with denylist DataFrame
filtered_df = primary_df.join(denylist_df, on="user_id", how="anti")

# Show the filtered DataFrame
filtered_df.show()

Output:

+-------+--------------+----------------------+
|user_id|          name|                 email|
+-------+--------------+----------------------+
|      1|      John Doe|   john.doe@example.com|
|      3| Alice Johnson|alice.johnson@example.com|
+-------+--------------+----------------------+

Conclusion

Filtering a Spark DataFrame using another DataFrame with a denylist can be efficiently achieved through an anti join. This approach ensures that only the records not present in the denylist DataFrame remain in the primary DataFrame. The example provided uses PySpark, but similar operations can be performed using Spark with other languages like Scala or Java.