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Compare two Parquet files using Spark in Java

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Parquet files are storage file format that is optimized for use with big data processing frameworks. It is particularly well-suited for use with Apache Hadoop, Apache Spark, Apache Hive, and other similar distributed storage and processing systems. Parquet files are often used in data lakes and data warehouses where large volumes of structured data need to be stored and analyzed efficiently.

Here is the program to compare two parquet files using Apache Spark in Java.

Step 1 – Add dependencies

Add below dependencies in your project.

<dependency>
			<groupId>org.apache.spark</groupId>
			<artifactId>spark-core_2.12</artifactId>
			<version>3.2.0</version>
</dependency>

<dependency>
			<groupId>org.apache.spark</groupId>
			<artifactId>spark-sql_2.12</artifactId>
			<version>3.2.0</version>
</dependency>

Step 2 – Create Spark session

Create local spark session. Below code is used for local connection not for remote connection. For remote spark connection you need to set spark.driver.host and spark.driver.port

// Create a Spark session
SparkSession spark = SparkSession.builder()
                .appName("fixmydevParquetComparator")
                .getOrCreate();

Step 3 – Read the Parquet files into DataFrames

Set paths of both parquet files and read it into Dataframes.

// Specify the paths to your Parquet files
String parquetFilePath1 = "path/to/first/parquet/file";
String parquetFilePath2 = "path/to/second/parquet/file";

// Read the Parquet files into DataFrames
Dataset<Row> df1 = spark.read().parquet(parquetFilePath1);
Dataset<Row> df2 = spark.read().parquet(parquetFilePath2);

Step 4 – Compare the DataFrames

Use below code to compare these data frames.

// Compare the DataFrames
        boolean areEqual = df1.except(df2).count() == 0 && df2.except(df1).count() == 0;

        if (areEqual) {
            System.out.println("The Parquet files are equal.");
        } else {
            System.out.println("The Parquet files are not equal.");
        }

Step 5 – Stop the Spark session

Stop spark session to release resources.

// Stop the Spark session
spark.stop();

This program will compare two parquet files that present in the local system. You many encounter out of memory error with this program if you are comparing two large parquet files. You can replace the count() method with isEmpty(). This avoids pulling the entire result set into the driver program, which can lead to out-of-memory errors for large datasets.

Memory efficient Step 4 in the code

// Compare the DataFrames in a distributed manner
        boolean areEqual = df1.except(df2).isEmpty() && df2.except(df1).isEmpty();

        if (areEqual) {
            System.out.println("The Parquet files are equal.");
        } else {
            System.out.println("The Parquet files are not equal.");
        }

Here is the complete code.

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;

public class DistributedParquetComparator {

    public static void main(String[] args) {
        // Create a Spark session
        SparkSession spark = SparkSession.builder()
                .appName("fixmydevParquetComparator")
                .getOrCreate();

        // Specify the paths to your Parquet files
        String parquetFilePath1 = "path/to/first/parquet/file";
        String parquetFilePath2 = "path/to/second/parquet/file";

        // Read the Parquet files into DataFrames
        Dataset<Row> df1 = spark.read().parquet(parquetFilePath1);
        Dataset<Row> df2 = spark.read().parquet(parquetFilePath2);

        // Compare the DataFrames in a distributed manner
        boolean areEqual = df1.except(df2).isEmpty() && df2.except(df1).isEmpty();

        if (areEqual) {
            System.out.println("The Parquet files are equal.");
        } else {
            System.out.println("The Parquet files are not equal.");
        }

        // Stop the Spark session
        spark.stop();
    }
}

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