Java Example Code

Development Description

Mongo can be connected only through enhanced datasource connections.

DDS is compatible with the MongoDB protocol.

• Prerequisites

  An enhanced datasource connection has been created on the DLI management console and bound to a queue in packages. For details, see Enhanced Datasource Connections.

  

  Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

• Code implementation
  1. Import dependencies.
     • Maven dependency involved

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       <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql_2.11</artifactId> <version>2.3.2</version> </dependency>

     • Import dependency packages.

       import org.apache.spark.SparkConf;
       import org.apache.spark.SparkContext;
       import org.apache.spark.api.java.JavaRDD;
       import org.apache.spark.api.java.JavaSparkContext;
       import org.apache.spark.sql.Dataset;
       import org.apache.spark.sql.Row;
       import org.apache.spark.sql.SQLContext;
       import org.apache.spark.sql.SaveMode;

  2. Create a session.

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     SparkContext sparkContext = new SparkContext(new SparkConf().setAppName("datasource-mongo")); JavaSparkContext javaSparkContext = new JavaSparkContext(sparkContext); SQLContext sqlContext = new SQLContext(javaSparkContext);

• Connecting to data sources through DataFrame APIs
  1. Read JSON data as DataFrames.

     JavaRDD<String> javaRDD = javaSparkContext.parallelize(Arrays.asList("{\"id\":\"5\",\"name\":\"Ann\",\"age\":\"23\"}"));
     Dataset<Row> dataFrame = sqlContext.read().json(javaRDD);

  2. Set connection parameters.

     String url = "192.168.4.62:8635,192.168.5.134:8635/test?authSource=admin";
     String uri = "mongodb://username:pwd@host:8635/db";
     String user = "rwuser";
     String database = "test";
     String collection = "test";
     String password = "######";

     

     For details about the parameters, see Table 1.

  3. Import data to Mongo.

     dataFrame.write().format("mongo")   
          .option("url",url)
          .option("uri",uri)     
          .option("database",database)  
          .option("collection",collection)   
          .option("user",user)    
          .option("password",password)  
          .mode(SaveMode.Overwrite)  
          .save();

  4. Read data from Mongo.

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     sqlContext.read().format("mongo") .option("url",url) .option("uri",uri) .option("database",database) .option("collection",collection) .option("user",user) .option("password",password) .load().show();

  5. View the operation result.

     

• Submitting a Spark job
  1. Upload the Java code file to DLI.

     For details about console operations, see Creating a Package. For details about API operations, see Uploading a Package Group.

  2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.
     For details about console operations, see Creating a Spark Job. For details about API operations, see Creating a Batch Processing Job.

     

     • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.mongo when you submit a job.
     • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

       spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/mongo/*

       spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/mongo/*

     • For details about how to submit a job on the console, see the description of the table "Parameters for selecting dependency resources" in Creating a Spark Job.
     • For details about how to submit a job through an API, see the description of the modules parameter in Table 2 "Request parameters" in Creating a Batch Processing Job.

Complete Example Code

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import org.apache.spark.SparkConf;
import org.apache.spark.SparkContext;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SQLContext;
import org.apache.spark.sql.SaveMode;
import java.util.Arrays;

public class TestMongoSparkSql {
  public static void main(String[] args) {
    SparkContext sparkContext = new SparkContext(new SparkConf().setAppName("datasource-mongo"));
    JavaSparkContext javaSparkContext = new JavaSparkContext(sparkContext);
    SQLContext sqlContext = new SQLContext(javaSparkContext);

//    // Read json file as DataFrame, read csv / parquet file, same as json file distribution
//    DataFrame dataFrame = sqlContext.read().format("json").load("filepath");

    // Read RDD in JSON format to create DataFrame
    JavaRDD<String> javaRDD = javaSparkContext.parallelize(Arrays.asList("{\"id\":\"5\",\"name\":\"Ann\",\"age\":\"23\"}"));
    Dataset<Row> dataFrame = sqlContext.read().json(javaRDD);

    String url = "192.168.4.62:8635,192.168.5.134:8635/test?authSource=admin";
    String uri = "mongodb://username:pwd@host:8635/db";
    String user = "rwuser";
    String database = "test";
    String collection = "test";
    String password = "######";

    dataFrame.write().format("mongo")
            .option("url",url)
            .option("uri",uri)
            .option("database",database) 
            .option("collection",collection)
            .option("user",user)
            .option("password",password)
            .mode(SaveMode.Overwrite) 
            .save();

    sqlContext.read().format("mongo")
            .option("url",url)
            .option("uri",uri)
            .option("database",database)
            .option("collection",collection)
            .option("user",user)
            .option("password",password)
            .load().show();
    sparkContext.stop();
    javaSparkContext.close();
  }
}