Working with Hive Tables in PySpark

To work with Hive tables in PySpark, you first need to configure the Spark session to use Hive by enabling Hive support and adding the Hive dependencies.

Here's an example of how you can create a SparkSession that enables Hive support and adds the Hive dependencies:

Enable Hive Support

from pyspark.sql import SparkSession 
spark = SparkSession.builder.appName("HiveTableExample") 
    \ .config("spark.jars.packages", "org.apache.hive:hive-jdbc:2.3.4") 
    \ .enableHiveSupport() \ 
    .getOrCreate()

To Create a DataFrame from a Hive table in Spark, you can use the spark.sql function to execute a query and return the result as a DataFrame. Here is an example:

Create a DataFrame from a Hive table in Spark

from pyspark.sql import SparkSession 
# Create a SparkSession spark = SparkSession.builder.appName("HiveTableToDataFrame").enableHiveSupport().getOrCreate() # Execute a Hive query and return the result as a DataFrame df = spark.sql("SELECT * FROM my_hive_table") # Show the DataFrame df.show()

Here, we first create a SparkSession using the SparkSession.builder class and enable Hive support by calling enableHiveSupport(). Next, we use the sql function of the SparkSession to execute a Hive query, in this case "SELECT * FROM my_hive_table", and return the result as a DataFrame. The DataFrame can then be displayed using the show() method.

You can also Read the Hive Table by passing the table name to the read.table() method:

Read Hive Table

df = spark.read.table("my_hive_table") df.show()

Make sure that you have the configurations set and the Hive dependencies added to the Spark session. You can check the configurations by doing:

Check Hive Configuration

spark.sql("SET -v").show(1000,False)

You can set the configurations by doing:

Setting Hive Configuration

spark.conf.set("hive.metastore.uris","thrift://host:port")

In addition to the above methods, you can also create a DataFrame from a Hive table using the spark.table method.

Usage of spark.table method

df = spark.table("my_hive_table") df.show()

It is also possible to specify the database name along with the table name when creating a DataFrame from a Hive table. For example, if the table is located in a database named "my_database", you can use the following:

Database with Table Name

df = spark.table("my_database.my_hive_table") df.show()

When you read a Hive table, Spark will use the Hive Metastore to determine the schema of the table. If the table is partitioned, Spark will read only the partitions that match the specified predicate. You can also read the whole table by not specifying a predicate.

If you would like to change the schema of the DataFrame, you can use the withColumnRenamed method, drop method and select method to rename, remove or select specific columns respectively, as shown below:

Operations on Hive Tables

# Rename a column df = df.withColumnRenamed("old_name", "new_name") # Remove a column df = df.drop("column_name") # Select specific columns df = df.select("column1", "column2", "column3")

Note that, if the Hive table you are reading is very large, you may want to consider using the spark.sql method to read the table in chunks or to filter the data before reading it into a DataFrame.

Another important thing is to make sure that the correct Hive version is being used. In case you are using a different version of Hive than the one that the Spark cluster is configured to use, you can specify the Hive version using the spark.sql.hive.metastore.version configuration property.

Hive Metastore version

spark.conf.set("spark.sql.hive.metastore.version", "2.3.4")

You also need to make sure that the Hive dependencies are added to the Spark session, otherwise you will get error messages.

Add Hive Dependencies

from pyspark.sql import SparkSession spark = SparkSession.builder.appName("HiveTableToDataFrame").config("spark.jars.packages", "org.apache.hive:hive-jdbc:2.3.4").enableHiveSupport().getOrCreate()

You can also use Hive external tables, which allow you to access data stored outside of the Hive warehouse directory. External tables are read-only, you can't perform any write operations on them.

Use Hive External Table

df = spark.sql("SELECT * FROM external_table")

In summary, to create a DataFrame from a Hive table in Spark, you can use the sql method, the table method or the read.table method, make sure you have the correct configurations set and the Hive dependencies added to the Spark session, and you can also use Hive external tables.