Cost‐Based Optimization (CBO) - datacouch-io/spark-java GitHub Wiki

Cost-Based Optimization (CBO) is a powerful optimization technique in Apache Spark SQL that aims to improve query performance by dynamically selecting the most efficient query execution plan based on the estimated costs of different execution strategies. It takes into account various factors, such as statistics about the data and the hardware characteristics of the cluster, to make informed decisions about query execution.

This document provides an overview of Cost-Based Optimization in Apache Spark SQL, its benefits, and how to enable and use CBO. Additionally, we'll provide a Java example that demonstrates the use of CBO for query optimization.

1. Benefits of Cost-Based Optimization
2. Enabling Cost-Based Optimization
3. Using Cost-Based Optimization
4. Example: Cost-Based Optimization in Java

Cost-Based Optimization offers several advantages for query performance optimization:

• Query Efficiency: CBO selects the most efficient query execution plan based on cost estimates, resulting in faster query execution.

• Statistics Usage: It leverages statistics about the data, such as column cardinality and data distribution, to make more accurate cost estimates.

• Hardware Awareness: CBO considers the hardware characteristics of the cluster, such as memory and CPU, when optimizing query plans.

• Adaptability: It adapts to changing data and cluster conditions, ensuring that query plans remain efficient over time.

To enable Cost-Based Optimization in Spark SQL, you need to set the following configuration properties:

• spark.sql.cbo.enabled: Set this property to true to enable CBO. By default, it is set to false.

• spark.sql.cbo.joinReorder.enabled: Set this property to true to enable join reordering, which is a key feature of CBO. By default, it is set to false.

Once CBO is enabled, it automatically kicks in when you execute SQL queries. It considers various factors, including the size of tables, data distribution, and available hardware resources, to optimize query plans.

To benefit from CBO, you do not need to make any changes to your SQL queries. Simply enable CBO as described above, and Spark SQL will take care of the optimization.

Now, let's illustrate how to use Cost-Based Optimization in Java with a simple example. In this example, we will load two DataFrames and perform a join operation, allowing CBO to optimize the join execution plan.

package com.sparkTutorial;  
  
import org.apache.spark.sql.Dataset;  
import org.apache.spark.sql.Row;  
import org.apache.spark.sql.SparkSession;  
import org.apache.spark.sql.functions.*;  
import org.apache.spark.sql.types.DataTypes;  
import org.apache.spark.sql.types.StructType;  
import org.apache.spark.sql.RowFactory;  
  
import java.util.Arrays;  
  
import static org.apache.spark.sql.functions.*;  
  
public class CBOExample {  
    public static void main(String[] args) {  
        // Create a SparkSession  
  SparkSession spark = SparkSession.builder()  
                .appName("CBOExample")  
                .master("local[*]")  
                .getOrCreate();  
  
  // Enable Cost-Based Optimization  
  spark.conf().set("spark.sql.cbo.enabled", "true");  
  spark.conf().set("spark.sql.cbo.joinReorder.enabled", "true");  
  
  // Sample data for orders DataFrame  
  Row[] orderData = new Row[]{  
                RowFactory.create(1, "2023-01-01", "Alice"),  
  RowFactory.create(2, "2023-01-02", "Bob"),  
  RowFactory.create(3, "2023-01-03", "Charlie")  
        };  
  
  // Sample data for orderDetails DataFrame  
  Row[] orderDetailsData = new Row[]{  
                RowFactory.create(1, 100.0),  
  RowFactory.create(2, 150.0),  
  RowFactory.create(1, 75.0),  
  RowFactory.create(3, 200.0),  
  RowFactory.create(2, 50.0)  
        };  
  
  // Define the schema for orders DataFrame  
  StructType orderSchema = new StructType()  
                .add("order_id", DataTypes.IntegerType, false)  
                .add("order_date", DataTypes.StringType, false)  
                .add("customer", DataTypes.StringType, false);  
  
  // Define the schema for orderDetails DataFrame  
  StructType orderDetailsSchema = new StructType()  
                .add("order_id", DataTypes.IntegerType, false)  
                .add("amount", DataTypes.DoubleType, false);  
  
  // Create DataFrames from the sample data  
  Dataset<Row> orders = spark.createDataFrame(Arrays.asList(orderData), orderSchema);  
  
  // Convert the date strings to DateType using to_date function with explicit date format  
  orders = orders.withColumn("order_date", to_date(col("order_date"), "yyyy-MM-dd"));  
  
  Dataset<Row> orderDetails = spark.createDataFrame(Arrays.asList(orderDetailsData), orderDetailsSchema);  
  
  // Perform a join operation to calculate total order amount  
  Dataset<Row> joinedDF = orders.join(orderDetails, "order_id")  
                .groupBy("order_id")  
                .agg(sum("amount").alias("total_amount"));  
  
  // Show the result  
  joinedDF.show();  
  
  // Stop the SparkSession  
  spark.stop();  
  }  
}

Explanation

This Java code demonstrates how to use Apache Spark's Structured API (Spark SQL) to perform a simple join operation between two DataFrames, enabling Cost-Based Optimization (CBO). Here's a step-by-step explanation of the code:

1. Import Required Spark Libraries:

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.functions.*;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructType;
import org.apache.spark.sql.RowFactory;

These import statements include necessary classes and functions for working with Spark DataFrames and enabling CBO.

2. Create a SparkSession:

SparkSession spark = SparkSession.builder()
    .appName("CBOExample")
    .master("local[*]")
    .getOrCreate();

This code creates a SparkSession, which is the entry point to working with Spark functionalities. It also specifies the application name ("CBOExample") and runs Spark in local mode using all available CPU cores ("local[]"). In a production environment, you would replace "local[]" with the appropriate master URL for your cluster.

3. Enable Cost-Based Optimization (CBO):

spark.conf().set("spark.sql.cbo.enabled", "true");
spark.conf().set("spark.sql.cbo.joinReorder.enabled", "true");

These lines enable the Cost-Based Optimization (CBO) feature in Spark SQL. CBO is used for optimizing query execution plans.

4. Create Sample Data:

Row[] orderData = new Row[]{
    RowFactory.create(1, "2023-01-01", "Alice"),
    RowFactory.create(2, "2023-01-02", "Bob"),
    RowFactory.create(3, "2023-01-03", "Charlie")
};
Row[] orderDetailsData = new Row[]{
    RowFactory.create(1, 100.0),
    RowFactory.create(2, 150.0),
    RowFactory.create(1, 75.0),
    RowFactory.create(3, 200.0),
    RowFactory.create(2, 50.0)
};

These arrays represent sample data for two DataFrames: orders and orderDetails. Each Row object contains values for the respective columns.

5. Define Data Schema:

StructType orderSchema = new StructType()
    .add("order_id", DataTypes.IntegerType, false)
    .add("order_date", DataTypes.StringType, false)
    .add("customer", DataTypes.StringType, false);

StructType orderDetailsSchema = new StructType()
    .add("order_id", DataTypes.IntegerType, false)
    .add("amount", DataTypes.DoubleType, false);

Here, we define the schema for the orders and orderDetails DataFrames, specifying column names and data types.

6. Create DataFrames:

Dataset<Row> orders = spark.createDataFrame(Arrays.asList(orderData), orderSchema);

The createDataFrame method is used to create DataFrames from the sample data and schemas.

7. Data Transformation:

orders = orders.withColumn("order_date", to_date(col("order_date"), "yyyy-MM-dd"));

In this step, we convert the "order_date" column from a string to a DateType using the to_date function with a specified date format.

8. Join and Aggregate DataFrames:

Dataset<Row> joinedDF = orders.join(orderDetails, "order_id")
    .groupBy("order_id")
    .agg(sum("amount").alias("total_amount"));

We perform a join operation between the orders and orderDetails DataFrames on the "order_id" column. Then, we group the result by "order_id" and calculate the total order amount using the agg function.

9. Display the Result:

joinedDF.show();

We use the show method to display the final result of the join and aggregation.

10. Stop SparkSession:

spark.stop();

Finally, we stop the SparkSession to release resources.

This code provides a basic example of using Spark SQL for data manipulation and demonstrates how to enable Cost-Based Optimization (CBO) for query optimization.

By following this pattern, you can leverage Cost-Based Optimization in Java applications to improve query performance in Apache Spark SQL.