Getting Started

Scenario

This section describes capabilities of Hudi using spark-shell. Using the Spark data source, this section describes how to insert and update a Hudi dataset of the default storage mode Copy-on Write (COW) tables based on code snippets. After each write operation, you will be introduced how to read snapshot and incremental data.

Prerequisites

• You have created a user and added the user to user groups hadoop (primary group) and hive on Manager.

Procedure

1. Download and install the Hudi client. For details, see Installing a Client (Version 3.x or Later).
   

   Currently, Hudi is integrated in Spark2x. You only need to download the Spark2x client on Manager. For example, the client installation directory is /opt/client.

2. Log in to the node where the client is installed as user root and run the following command:

   cd /opt/client

3. Run the following commands to load environment variables:

   source bigdata_env

   source Hudi/component_env

   kinit Created user

   

   • You need to change the password of the created user, and then run the kinit command to log in to the system again.
   • In normal mode (Kerberos authentication disabled), you do not need to run the kinit command.

4. Use spark-shell --master yarn-client to import Hudi packages to generate test data:

   // Import required packages.
   import org.apache.hudi.QuickstartUtils._
   import scala.collection.JavaConversions._
   import org.apache.spark.sql.SaveMode._
   import org.apache.hudi.DataSourceReadOptions._
   import org.apache.hudi.DataSourceWriteOptions._
   import org.apache.hudi.config.HoodieWriteConfig._
   // Define the table name and storage path to generate test data.
   val tableName = "hudi_cow_table"
   val basePath = "hdfs://hacluster/tmp/hudi_cow_table"
   val dataGen = new DataGenerator
   val inserts = convertToStringList(dataGen.generateInserts(10))
   val df = spark.read.json(spark.sparkContext.parallelize(inserts, 2))

5. Write data to the Hudi table in overwrite mode.

   df.write.format("org.apache.hudi").
   options(getQuickstartWriteConfigs).
   option(PRECOMBINE_FIELD_OPT_KEY, "ts").
   option(RECORDKEY_FIELD_OPT_KEY, "uuid").
   option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").
   option(TABLE_NAME, tableName).
   mode(Overwrite).
   save(basePath)

6. Query the Hudi table.

   Register a temporary table and query the table.

   val roViewDF = spark.
   read.
   format("org.apache.hudi").
   load(basePath + "/*/*/*/*")
   roViewDF.createOrReplaceTempView("hudi_ro_table")
   spark.sql("select fare, begin_lon, begin_lat, ts from  hudi_ro_table where fare > 20.0").show()

7. Generate new data and update the Hudi table in append mode.

   val updates = convertToStringList(dataGen.generateUpdates(10))
   val df = spark.read.json(spark.sparkContext.parallelize(updates, 1))
   df.write.format("org.apache.hudi").
   options(getQuickstartWriteConfigs).
   option(PRECOMBINE_FIELD_OPT_KEY, "ts").
   option(RECORDKEY_FIELD_OPT_KEY, "uuid").
   option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").
   option(TABLE_NAME, tableName).
   mode(Append).
   save(basePath)

8. Query incremental data in the Hudi table.
   • Reload data.

     spark.
     read.
     format("org.apache.hudi").
     load(basePath + "/*/*/*/*").
     createOrReplaceTempView("hudi_ro_table")

   • Perform the incremental query.

     val commits = spark.sql("select distinct(_hoodie_commit_time) as commitTime from  hudi_ro_table order by commitTime").map(k => k.getString(0)).take(50)
     val beginTime = commits(commits.length - 2)
     val incViewDF = spark.
     read.
     format("org.apache.hudi").
     option(VIEW_TYPE_OPT_KEY, VIEW_TYPE_INCREMENTAL_OPT_VAL).
     option(BEGIN_INSTANTTIME_OPT_KEY, beginTime).
     load(basePath);
     incViewDF.registerTempTable("hudi_incr_table")
     spark.sql("select `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts from  hudi_incr_table where fare > 20.0").show()

9. Perform the point-in-time query.

   val beginTime = "000"
   val endTime = commits(commits.length - 2)
   val incViewDF = spark.read.format("org.apache.hudi").
   option(VIEW_TYPE_OPT_KEY, VIEW_TYPE_INCREMENTAL_OPT_VAL).
   option(BEGIN_INSTANTTIME_OPT_KEY, beginTime).
   option(END_INSTANTTIME_OPT_KEY, endTime).
   load(basePath);
   incViewDF.registerTempTable("hudi_incr_table")
   spark.sql("select `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts from  hudi_incr_table where fare > 20.0").show()

10. Delete data.
    • Prepare the data to be deleted.

      val df = spark.sql("select uuid, partitionpath from hudi_ro_table limit 2")
      val deletes = dataGen.generateDeletes(df.collectAsList())

    • Execute the deletion.

      val df = spark.read.json(spark.sparkContext.parallelize(deletes, 2));
      df.write.format("org.apache.hudi").
      options(getQuickstartWriteConfigs).
      option(OPERATION_OPT_KEY,"delete").
      option(PRECOMBINE_FIELD_OPT_KEY, "ts").
      option(RECORDKEY_FIELD_OPT_KEY, "uuid").
      option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").
      option(TABLE_NAME, tableName).
      mode(Append).
      save(basePath);

    • Query data again.

      val roViewDFAfterDelete = spark.
      read.
      format("org.apache.hudi").
      load(basePath + "/*/*/*/*")
      roViewDFAfterDelete.createOrReplaceTempView("hudi_ro_table")
      spark.sql("select uuid, partitionPath from hudi_ro_table").show()