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Read and write operations

The HiveWarehouseSession API supports reading and writing hive tables from spark. HWC supports writing to ORC tables only. You can update statements and write DataFrames to partitioned Hive tables, perform batch writes, and use HiveStreaming.

Read operations

Execute a Hive SELECT query and return a DataFrame.

hive.sql("select * from web_sales")

HWC supports push-downs of DataFrame filters and projections applied to .sql().

Alternatively, you can use .execute or .executeQuery as previously described.

Execute a Hive update statement

Execute CREATE, UPDATE, DELETE, INSERT, and MERGE statements in this way:

hive.executeUpdate("ALTER TABLE old_name RENAME TO new_name")

Write a DataFrame to Hive in batch

This operation uses LOAD DATA INTO TABLE.

Java/Scala:

df.write.format(HIVE_WAREHOUSE_CONNECTOR).option("table", <tableName>).save()

Python:

df.write.format(HiveWarehouseSession().HIVE_WAREHOUSE_CONNECTOR).option("table", &tableName>).save()

Write a DataFrame to Hive, specifying partitions

HWC follows Hive semantics for overwriting data with and without partitions and is not affected by the setting of spark.sql.sources.partitionOverwriteMode to static or dynamic. This behavior mimics the latest Spark Community trend reflected in Spark-20236 (link below).

Java/Scala:

df.write.format(HIVE_WAREHOUSE_CONNECTOR).option("table", <tableName>).option("partition", <partition_spec>).save()

Python:

df.write.format(HiveWarehouseSession().HIVE_WAREHOUSE_CONNECTOR).option("table", &tableName>).option("partition", <partition_spec>).save()
Where <partition_spec> is in one of the following forms:
  • option("partition", "c1='val1',c2=val2") // static
  • option("partition", "c1='val1',c2") // static followed by dynamic
  • option("partition", "c1,c2") // dynamic
Depending on the partition spec, HWC generates queries in one of the following forms for writing data to Hive.
  • No partitions specified = LOAD DATA
  • Only static partitions specified = LOAD DATA...PARTITION
  • Some dynamic partition present = CREATE TEMP TABLE + INSERT INTO/OVERWRITE query.

Note: Writing static partitions is faster than writing dynamic partitions.

Write a DataFrame to Hive using HiveStreaming

When using HiveStreaming to write a DataFrame to Hive or a Spark Stream to Hive, you need to escape any commas in the stream, as shown in Use the Hive Warehouse Connector for Streaming (link below).

Java/Scala:

//Using dynamic partitioning
df.write.format(DATAFRAME_TO_STREAM).option("table", <tableName>).save()
          
//Or, writing to a static partition
df.write.format(DATAFRAME_TO_STREAM).option("table", <tableName>).option("partition", <partition>).save()

Python:

//Using dynamic partitioning
df.write.format(HiveWarehouseSession().DATAFRAME_TO_STREAM).option("table", <tableName>).save()
          
//Or, writing to a static partition
df.write.format(HiveWarehouseSession().DATAFRAME_TO_STREAM).option("table", <tableName>).option("partition", <partition>).save()

Write a Spark Stream to Hive using HiveStreaming

Java/Scala:

stream.writeStream.format(STREAM_TO_STREAM).option("table", "web_sales").start()

Python:

stream.writeStream.format(HiveWarehouseSession().STREAM_TO_STREAM).option("table", "web_sales").start()