How to use the 'spark CSV' framework in the Java class library
How to use the 'spark CSV' framework in the Java class library
Introduction:
Spark is a fast and general analysis engine that provides strong data processing capabilities.In the Spark ecosystem, SPARK CSV is a commonly used library that is used to read, write, and operates the data format of the CSV file format.This article will introduce how to use the Spark CSV framework in the Java library and provide specific Java code examples.
step:
1. Introduction to dependencies
First, add Spark CSV to the Java project construction tool (such as Maven or Gradle).For example, add the following dependencies to Maven's pom.xml file:
<dependencies>
<dependency>
<groupId>com.databricks</groupId>
<artifactId>spark-csv_2.11</artifactId>
<version>1.5.0</version>
</dependency>
</dependencies>
Please select the correct dependent version according to the Spark and Scala versions you use.
2. Create SparkSession object
In the Java code, you first need to create a SparkSession object to start the Spark application.You can use the builder mode to create sparkSession, as shown below:
import org.apache.spark.sql.SparkSession;
SparkSession spark = SparkSession
.builder()
.appName("SparkCSVExample")
. Master ("Local [*]") // Set the URL of the SPARK master node
.getOrCreate();
3. Read the CSV file
Next, read the CSV file with the SparkSession object.You can use the method of `Read (). Format (" CSV ") and specify the file path and other options to read CSV data.
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
String filePath = "path/to/csv/file.csv";
Dataset<Row> csvData = spark.read()
.format("csv")
.opting ("header", "true") // Is the first line of the head head
.opting ("Inferschema", "TRUE") // Automatically infer the data type
.load(filePath);
This will return a `DataSet <Row>` object, which contains data with CSV files.
4. Operate CSV data
Once the CSV data is loaded to the DataSet, you can use Spark's DataFrame API or SQL to operate it.Here are some common operation examples:
// Display the structure and content of the data
csvData.show();
// Perform data filtering and filtering
Dataset<Row> filteredData = csvData.filter(csvData.col("age").gt(25));
// Based in a column and calculate the statistical value
Dataset<Row> groupByData = csvData.groupBy("department").agg(functions.avg("salary"));
// Save the result to CSV file
groupByData.write().format("csv").save("path/to/save/file.csv");
5. Close SparkSession
Finally, at the end of the Spark program, remember to close the SparkSession object to release resources.
spark.close();
Complete example code:
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import static org.apache.spark.sql.functions.*;
public class SparkCSVExample {
public static void main(String[] args) {
SparkSession spark = SparkSession
.builder()
.appName("SparkCSVExample")
.master("local[*]")
.getOrCreate();
String filePath = "path/to/csv/file.csv";
Dataset<Row> csvData = spark.read()
.format("csv")
.option("header", "true")
.option("inferSchema", "true")
.load(filePath);
csvData.show();
Dataset<Row> filteredData = csvData.filter(csvData.col("age").gt(25));
Dataset<Row> groupByData = csvData.groupBy("department").agg(avg("salary"));
groupByData.write().format("csv").save("path/to/save/file.csv");
spark.close();
}
}
in conclusion:
By using the Spark CSV framework, you can easily read, write and operate the CSV files in the Java class library.In this article, we introduced how to load CSV data with Java code and show some common data operation examples.I hope this article will help you use the Spark CSV framework in Java.
