Скачать или смотреть Solving the MySQL Metastore Configuration Issue for Spark Users

Discover how to properly set up MySQL as a Hive Metastore for Spark, overcoming common configuration pitfalls.
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This video is based on the question https://stackoverflow.com/q/70482619/ asked by the user 'Max' ( https://stackoverflow.com/u/815638/ ) and on the answer https://stackoverflow.com/a/70489848/ provided by the user 'Max' ( https://stackoverflow.com/u/815638/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Solving the MySQL Metastore Configuration Issue for Spark Users

If you're trying to integrate MySQL as a Hive Metastore for your Spark setup, you may have encountered some frustrating challenges. Many users, like yourself, face issues when trying to move away from the default Derby database, especially when trying to allow multiple connections to Spark. In this guide, we will dive into the common problems you may face during configuration and provide a detailed solution to streamline the process.

The Problem: Connection Setup Issues

When configuring your Hive Metastore with MySQL, you might have noticed some alarming warnings or errors in your Spark logs. Here’s a quick summary of the configurations and the problems encountered:

Connection Parameters: Your initial hive-site.xml configuration included connection properties for MySQL like the following:

[[See Video to Reveal this Text or Code Snippet]]

Error Messages: You might encounter warnings related to deprecated SQL syntax or issues around table creation which can look like:

[[See Video to Reveal this Text or Code Snippet]]

The Solution: Manual Setup of Metastore

While it may be tempting to rely on Spark to configure the metastore automatically, this mechanism often leads to inconsistencies. Here’s how you can manually set up your Hive Metastore to work seamlessly with Spark:

Step 1: Download and Set Up Hadoop and Hive

Make sure you have the following components installed on your local machine:

Apache Hadoop

Apache Hive

You can download the latest versions directly from the respective project websites. Ensure that your installation paths are correctly configured in your environment variables.

Step 2: Use the Schema Tool

Once you have both Hadoop and Hive installed, navigate to the bin directory of Hive and use the schematool command to initialize the metastore database:

[[See Video to Reveal this Text or Code Snippet]]

This command will set up the necessary tables in your MySQL database based on the Hive schema. By doing this, you can avoid many of the warnings and errors that arise from Spark trying to create these tables automatically.

Step 3: Restart Spark

After executing the schema tool, restart your Spark application. Make sure your hive-site.xml configuration is correctly pointing to your MySQL instance. You should not face the same errors as before now that the tables are pre-existing in your database.

Conclusion

By taking the manual route to set up the Hive Metastore, you can effectively sidestep common SQL syntax errors and deprecated warnings that often occur with auto-initialization. Not only does this ensure better compliance with your chosen MySQL version, but it also enables you to use Spark with multiple applications smoothly.

In summary:

Avoid relying on Spark for Metastore initialization.

Use the Hive schematool for creating necessary database setups.

Ensure Hive and Hadoop are correctly set up before proceeding.

This method optimizes your Spark environment and allows for a more robust interaction with MySQL as your metastore. By following these steps, you should experience improved connectivity and fewer headaches in your data processing setup.