Hive Warehouse Connector APIs in Azure HDInsight

This article lists all the APIs supported by Hive warehouse connector. All the examples shown below are run using spark-shell and hive warehouse connector session.

How to create Hive warehouse connector session:

import com.hortonworks.hwc.HiveWarehouseSession
val hive = HiveWarehouseSession.session(spark).build()

Prerequisite

Complete the Hive Warehouse Connector setup steps.

Supported APIs

• Set the database:

  hive.setDatabase("<database-name>")
  

• List all databases:

  hive.showDatabases()
  

• List all tables in the current database

  hive.showTables()
  

• Describe a table

  // Describes the table <table-name> in the current database
  hive.describeTable("<table-name>")
  

  // Describes the table <table-name> in <database-name>
  hive.describeTable("<database-name>.<table-name>")
  

• Drop a database

  // ifExists and cascade are boolean variables
  hive.dropDatabase("<database-name>", ifExists, cascade)
  

• Drop a table in the current database

  // ifExists and purge are boolean variables
  hive.dropTable("<table-name>", ifExists, purge)
  

• Create a database

  // ifNotExists is boolean variable
  hive.createDatabase("<database-name>", ifNotExists)
  

• Create a table in current database

  // Returns a builder to create table
  val createTableBuilder = hive.createTable("<table-name>")
  

  Builder for create-table supports only the below operations:

  // Create only if table does not exists already
  createTableBuilder = createTableBuilder.ifNotExists()
  

  // Add columns
  createTableBuilder = createTableBuilder.column("<column-name>", "<datatype>")
  

  // Add partition column
  createTableBuilder = createTableBuilder.partition("<partition-column-name>", "<datatype>")
  

  // Add table properties
  createTableBuilder = createTableBuilder.prop("<key>", "<value>")
  

  // Creates a bucketed table,
  // Parameters are numOfBuckets (integer) followed by column names for bucketing
  createTableBuilder = createTableBuilder.clusterBy(numOfBuckets, "<column1>", .... , "<columnN>")
  

  // Creates the table
  createTableBuilder.create()
  

  Note

  This API creates an ORC formatted table at default location. For other features/options or to create table using hive queries, use executeUpdate API.

• Read a table

  // Returns a Dataset<Row> that contains data of <table-name> in the current database
  hive.table("<table-name>")
  

• Execute DDL commands on HiveServer2

  // Executes the <hive-query> against HiveServer2
  // Returns true or false if the query succeeded or failed respectively
  hive.executeUpdate("<hive-query>")
  

  // Executes the <hive-query> against HiveServer2
  // Throws exception, if propagateException is true and query threw exception in HiveServer2
  // Returns true or false if the query succeeded or failed respectively
  hive.executeUpdate("<hive-query>", propagateException) // propagate exception is boolean value
  

• Execute Hive query and load result in Dataset

  • Executing query via LLAP daemons. [Recommended]

    // <hive-query> should be a hive query 
    hive.executeQuery("<hive-query>")
    

  • Executing query through HiveServer2 via JDBC.

    Set spark.datasource.hive.warehouse.smartExecution to false in spark configs before starting spark session to use this API

    hive.execute("<hive-query>")
    

• Close Hive warehouse connector session

  // Closes all the open connections and
  // release resources/locks from HiveServer2
  hive.close()
  

• Execute Hive Merge query

  This API creates a Hive merge query of below format

  MERGE INTO <current-db>.<target-table> AS <targetAlias> USING <source expression/table> AS <sourceAlias>
  ON <onExpr>
  WHEN MATCHED [AND <updateExpr>] THEN UPDATE SET <nameValuePair1> ... <nameValuePairN>
  WHEN MATCHED [AND <deleteExpr>] THEN DELETE
  WHEN NOT MATCHED [AND <insertExpr>] THEN INSERT VALUES <value1> ... <valueN>
  

  val mergeBuilder = hive.mergeBuilder() // Returns a builder for merge query
  

  Builder supports the following operations:

  mergeBuilder.mergeInto("<target-table>", "<targetAlias>")
  

  mergeBuilder.using("<source-expression/table>", "<sourceAlias>")
  

  mergeBuilder.on("<onExpr>")
  

  mergeBuilder.whenMatchedThenUpdate("<updateExpr>", "<nameValuePair1>", ... , "<nameValuePairN>")
  

  mergeBuilder.whenMatchedThenDelete("<deleteExpr>")
  

  mergeBuilder.whenNotMatchedInsert("<insertExpr>", "<value1>", ... , "<valueN>");
  

  // Executes the merge query
  mergeBuilder.merge()
  

• Write a Dataset to Hive Table in batch

  df.write.format("com.hortonworks.spark.sql.hive.llap.HiveWarehouseConnector")
     .option("table", tableName)
     .mode(SaveMode.Type)
     .save()
  

  • TableName should be of form <db>.<table> or <table>. If no database name is provided, the table will searched/created in the current database

  • SaveMode types are:

    • Append: Appends the dataset to the given table

    • Overwrite: Overwrites the data in the given table with dataset

    • Ignore: Skips write if table already exists, no error thrown

    • ErrorIfExists: Throws error if table already exists

• Write a Dataset to Hive Table using HiveStreaming

  df.write.format("com.hortonworks.spark.sql.hive.llap.HiveStreamingDataSource")
     .option("database", databaseName)
     .option("table", tableName)
     .option("metastoreUri", "<HMS_URI>")
  // .option("metastoreKrbPrincipal", principal), add if executing in ESP cluster
     .save()
  
   // To write to static partition
   df.write.format("com.hortonworks.spark.sql.hive.llap.HiveStreamingDataSource")
     .option("database", databaseName)
     .option("table", tableName)
     .option("partition", partition)
     .option("metastoreUri", "<HMS URI>")
  // .option("metastoreKrbPrincipal", principal), add if executing in ESP cluster
     .save()
  

  Note

  Stream writes always append data.

• Writing a spark stream to a Hive Table

  stream.writeStream
      .format("com.hortonworks.spark.sql.hive.llap.streaming.HiveStreamingDataSource")
      .option("metastoreUri", "<HMS_URI>")
      .option("database", databaseName)
      .option("table", tableName)
    //.option("partition", partition) , add if inserting data in partition
    //.option("metastoreKrbPrincipal", principal), add if executing in ESP cluster
      .start()