What is Schema Evolution in Parquet Format and How Does It Work?

Schema evolution in the context of Parquet format refers to the ability to modify the schema of your data after the original schema has been written. This feature is crucial for data systems that need to evolve over time to accommodate changes in data structures, such as adding new columns, modifying existing ones, or even removing columns.

How Schema Evolution Works

Parquet’s schema evolution capability is primarily due to its ability to store schema information along with the data. Each Parquet file includes metadata that describes the schema of the data within that file. When you read multiple Parquet files, the system can reconcile these different schemas to provide a unified view of the data.

Types of Schema Evolution

• Adding Columns: New columns can be added, and these will appear as nulls for the data written with the older schema.
• Removing Columns: Removing columns is generally straightforward as long as they are not required for the logic processing the data.
• Modifying Columns: Changes like renaming, changing data types, or moving columns require more consideration and may require a migration strategy.

Example in PySpark

Let’s go through an example in PySpark where we have an initial DataFrame with a simple schema, write it to Parquet, add a new column to the DataFrame, and then read both files to see schema evolution in action.

Step 1: Create Initial DataFrame and Write to Parquet

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, IntegerType

# Initialize Spark session
spark = SparkSession.builder.appName("SchemaEvolutionExample").getOrCreate()

# Original schema
schema1 = StructType([
    StructField("name", StringType(), True),
    StructField("age", IntegerType(), True)
])

# Sample data
data1 = [("Alice", 30), ("Bob", 28)]

# Create DataFrame
df1 = spark.createDataFrame(data1, schema1)

# Write DataFrame to Parquet
df1.write.parquet("/tmp/parquet/schema_evolution")

Step 2: Create New DataFrame with Additional Column and Write to Parquet

# New schema with an additional column
schema2 = StructType([
    StructField("name", StringType(), True),
    StructField("age", IntegerType(), True),
    StructField("city", StringType(), True)
])

# Sample data with new column
data2 = [("Alice", 30, "New York"), ("Bob", 28, "Los Angeles")]

# Create new DataFrame
df2 = spark.createDataFrame(data2, schema2)

# Write new DataFrame to Parquet
df2.write.parquet("/tmp/parquet/schema_evolution", mode='append')

Step 3: Read Parquet Files with Schema Evolution

# Read the Parquet files back
df_read = spark.read.parquet("/tmp/parquet/schema_evolution")

# Show the DataFrame with the evolved schema
df_read.show()

+-----+---+-----------+
| name|age|       city|
+-----+---+-----------+
|Alice| 30| New York  |
|  Bob| 28|Los Angeles|
|Alice| 30|       null|
|  Bob| 28|       null|
+-----+---+-----------+

As you can see, the new column “city” is added and appears as null for the older data, demonstrating schema evolution.

Considerations

While schema evolution offers great flexibility, it also comes with a few considerations:

• Ensure compatibility: Not all schema changes are compatible, especially when changing data types.
• Performance: Adding and removing columns frequently might impact read/write performance.
• Migration: More complex schema changes might require careful data migration strategies to ensure consistency.

Understanding these nuances will help you effectively manage schema changes in Parquet format within your Spark applications.