Data Engineering using Spark SQL - Getting Started - Understanding Warehouse Directory

In this article, we will dive into the details of the Spark Metastore Warehouse Directory. The Warehouse Directory plays a crucial role in storing databases and tables in Spark SQL.

Key Concepts Explanation

Database Directory

A Database in Spark SQL is essentially a directory in the underlying file system like HDFS. Each database is stored as a separate directory with a .db extension.

// Code Example
val username = System.getProperty("")
val spark = SparkSession.builder
    .config("spark.ui.port", "0")
    .config("spark.sql.warehouse.dir", s"/user/${username}/warehouse")
    .appName(s"${username} | Spark SQL - Getting Started")

Table Directory

A Spark Metastore Table is represented as a directory in the underlying file system just like a database. Each table is stored as a separate directory under the respective database.

Partition Directory

Partitions of Spark Metastore Table are essentially directories in the underlying file system under the table directory. Partitions help in organizing and managing data efficiently.

Warehouse Directory

The Warehouse Directory serves as the base directory where directories related to databases and tables are stored by default. It is controlled by the spark.sql.warehouse.dir property.

// Code Example
SET spark.sql.warehouse.dir;

Do not modify the spark.sql.warehouse.dir property in Spark SQL CLI as it will not have any effect on the Warehouse Directory configuration.

Hands-On Tasks

  1. Set up a new database directory in Spark SQL.
  2. Create a new table directory under the database.


In conclusion, understanding the Warehouse Directory in Spark SQL is essential for organizing and managing databases and tables efficiently. Practice using the concepts discussed in this article and explore further in the Spark SQL community for continuous learning and growth.

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