PySpark 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.

  NOTE:

  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.

• Connecting to data sources through DataFrame APIs
  1. Import dependencies.

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     from __future__ import print_function from pyspark.sql.types import StructType, StructField, IntegerType, StringType from pyspark.sql import SparkSession

  2. Create a session.

     1	sparkSession = SparkSession.builder.appName("datasource-redis").getOrCreate()

  3. Set connection parameters.

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     host = "192.168.4.199" port = "6379" table = "person" auth = "@@@@@@"

  4. Create a DataFrame.
     1. Method 1:

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        dataList = sparkSession.sparkContext.parallelize([(1, "Katie", 19),(2,"Tom",20)]) schema = StructType([StructField("id", IntegerType(), False), StructField("name", StringType(), False), StructField("age", IntegerType(), False)]) dataFrame = sparkSession.createDataFrame(dataList, schema)

     2. Method 2:

        1 2	jdbcDF = sparkSession.createDataFrame([(3,"Jack", 23)]) dataFrame = jdbcDF.withColumnRenamed("_1", "id").withColumnRenamed("_2", "name").withColumnRenamed("_3", "age")

  5. Import data to Redis.

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     dataFrame.write .format("redis")\ .option("host", host)\ .option("port", port)\ .option("table", table)\ .option("password", auth)\ .mode("Overwrite")\ .save()

     NOTE:

     • The options of mode are Overwrite, Append, ErrorIfExis, and Ignore.
     • To specify a key, use .option("key.column", "name"). name indicates the column name.
     • To save nested DataFrames, use .option("model", "binary").
     • If you need to specify the data expiration time, use .option("ttl", 1000). The unit is second.
  6. Read data from Redis.

     1	sparkSession.read.format("redis").option("host", host).option("port", port).option("table", table).option("password", auth).load().show()

  7. View the operation result.

     

• Connecting to data sources through SQL APIs
  1. Create a table to connect to a Redis data source.

     sparkSession.sql(
          "CREATE TEMPORARY VIEW person (name STRING, age INT) USING org.apache.spark.sql.redis OPTIONS (
          'host' = '192.168.4.199',
          'port' = '6379',
          'password' = '######',
          table  'person')".stripMargin)
  2. Insert data.

     1	sparkSession.sql("INSERT INTO TABLE person VALUES ('John', 30),('Peter', 45)".stripMargin)

  3. Query data.

     1	sparkSession.sql("SELECT * FROM person".stripMargin).collect().foreach(println)

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

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

     • 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 a module. Configure Spark parameters (--conf).

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

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

     • 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

• Connecting to data sources through DataFrame APIs

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  # _*_ coding: utf-8 _*_ from __future__ import print_function from pyspark.sql.types import StructType, StructField, IntegerType, StringType from pyspark.sql import SparkSession if __name__ == "__main__": # Create a SparkSession session. sparkSession = SparkSession.builder.appName("datasource-redis").getOrCreate() # Set cross-source connection parameters. host = "192.168.4.199" port = "6379" table = "person" auth = "######" # Create a DataFrame and initialize the DataFrame data. # ******* method noe ********* dataList = sparkSession.sparkContext.parallelize([(1, "Katie", 19),(2,"Tom",20)]) schema = StructType([StructField("id", IntegerType(), False),StructField("name", StringType(), False),StructField("age", IntegerType(), False)]) dataFrame_one = sparkSession.createDataFrame(dataList, schema) # ****** method two ****** # jdbcDF = sparkSession.createDataFrame([(3,"Jack", 23)]) # dataFrame = jdbcDF.withColumnRenamed("_1", "id").withColumnRenamed("_2", "name").withColumnRenamed("_3", "age") # Write data to the redis table dataFrame.write.format("redis").option("host", host).option("port", port).option("table", table).option("password", auth).mode("Overwrite").save() # Read data sparkSession.read.format("redis").option("host", host).option("port", port).option("table", table).option("password", auth).load().show() # close session sparkSession.stop()

• Connecting to data sources through SQL APIs

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  # _*_ coding: utf-8 _*_ from __future__ import print_function from pyspark.sql import SparkSession if __name__ == "__main__": # Create a SparkSession sparkSession = SparkSession.builder.appName("datasource_redis").getOrCreate() sparkSession.sql( "CREATE TEMPORARY VIEW person (name STRING, age INT) USING org.apache.spark.sql.redis OPTIONS (\ 'host' = '192.168.4.199', \ 'port' = '6379',\ 'password' = '######',\ 'table'= 'person')".stripMargin); sparkSession.sql("INSERT INTO TABLE person VALUES ('John', 30),('Peter', 45)".stripMargin) sparkSession.sql("SELECT * FROM person".stripMargin).collect().foreach(println) # close session sparkSession.stop()