Understanding Hive Metastore_db creation in different directories

Hive @ Freshers.in

Apache Hive users often encounter a scenario where running a Hive query in different directories leads to the creation of a new metastore_db in each directory. This article aims to explain the reason behind this behavior and offers guidance on how to manage it effectively.

Why Does Hive create metastore_db in each directory?

Default Embedded Derby database

Hive uses Apache Derby, an embedded database, for its metastore in a default setup. The embedded Derby database is intended for lightweight and single-user purposes.

Working Directory Dependent

When you run a Hive query, it looks for the metastore in the current working directory. If it doesn’t find an existing metastore (metastore_db), it creates a new one. This is why executing Hive queries in different directories results in multiple metastore_db instances.

Implications of Multiple metastore_db Instances

  • Inconsistency: Different metastore_db instances in various directories can lead to inconsistency in metadata across these instances.
  • Space Utilization: Each new metastore_db consumes disk space, potentially leading to inefficient space usage.

Managing Hive Metastore for consistency

Configuring a shared Metastore

To avoid the creation of multiple metastore_db directories, configure Hive to use a shared, central metastore. This can be achieved by setting up a standalone metastore service using a more robust database like MySQL or PostgreSQL.

Steps to Configure a shared Metastore

  1. Install a Database Server: Choose a database like MySQL or PostgreSQL and install it on a server.
  2. Configure Hive to Use the Database: Update the Hive configuration (hive-site.xml) to point to the database server for the metastore.
  3. Initialize the Metastore Schema: Use Hive schema tool commands to initialize the database schema for the metastore.

Benefits of a shared Metastore

  • Consistency: Ensures metadata consistency across different Hive sessions and directories.
  • Scalability: More robust for handling larger, multi-user environments.
  • Central Management: Simplifies the management of the metastore.

Hive important pages to refer

  1. Hive
  2. Hive Interview Questions
  3. Hive Official Page
  4. Spark Examples
  5. PySpark Blogs
  6. Bigdata Blogs
  7. Spark Interview Questions
  8. Spark Official Page
Author: user