Java Example Code

Development Description

Redis supports only enhanced datasource connections.

• Prerequisites

  An enhanced datasource connection has been created on the DLI management console and bound to a queue in packages.

  

  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.

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       import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.sql.*; import org.apache.spark.sql.types.DataTypes; import org.apache.spark.sql.types.StructField; import org.apache.spark.sql.types.StructType; import java.util.*;

  2. Create a session.

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     SparkConf sparkConf = new SparkConf(); sparkConf.setAppName("datasource-redis") .set("spark.redis.host", "192.168.4.199") .set("spark.redis.port", "6379") .set("spark.redis.auth", "******") .set("spark.driver.allowMultipleContexts","true"); JavaSparkContext javaSparkContext = new JavaSparkContext(sparkConf); SQLContext sqlContext = new SQLContext(javaSparkContext);

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

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     JavaRDD<String> javaRDD = javaSparkContext.parallelize(Arrays.asList( "{\"id\":\"1\",\"name\":\"Ann\",\"age\":\"18\"}", "{\"id\":\"2\",\"name\":\"lisi\",\"age\":\"21\"}")); Dataset dataFrame = sqlContext.read().json(javaRDD);

  2. Construct the Redis connection parameters.

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     Map map = new HashMap<String, String>(); map.put("table","person"); map.put("key.column","id");

  3. Save data to Redis.

     1	dataFrame.write().format("redis").options(map).mode(SaveMode.Overwrite).save();

  4. Read data from Redis.

     1	sqlContext.read().format("redis").options(map).load().show();

  5. View the operation result.

     

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

  2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.
     

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

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

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

Complete Example Code

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public class Test_Redis_DaraFrame {
  public static void main(String[] args) {
    //create a SparkSession session  
    SparkConf sparkConf = new SparkConf();  
    sparkConf.setAppName("datasource-redis")
             .set("spark.redis.host", "192.168.4.199")
             .set("spark.redis.port", "6379")
             .set("spark.redis.auth", "******")
             .set("spark.driver.allowMultipleContexts","true");
    JavaSparkContext javaSparkContext = new JavaSparkContext(sparkConf);
    SQLContext sqlContext = new SQLContext(javaSparkContext);

    //Read RDD in JSON format to create DataFrame
    JavaRDD<String> javaRDD = javaSparkContext.parallelize(Arrays.asList(
            "{\"id\":\"1\",\"name\":\"Ann\",\"age\":\"18\"}",
            "{\"id\":\"2\",\"name\":\"lisi\",\"age\":\"21\"}"));
    Dataset dataFrame = sqlContext.read().json(javaRDD);

    Map map = new HashMap<String, String>();
    map.put("table","person");
    map.put("key.column","id");
    dataFrame.write().format("redis").options(map).mode(SaveMode.Overwrite).save();      
    sqlContext.read().format("redis").options(map).load().show();

  }
}