Скачать или смотреть Efficiently Persisting Thousands of Entities with Java and PostgreSQL

Discover the best practices for `batch inserting records` in PostgreSQL using Java with Spring Boot and Hibernate. Learn about custom repository implementation and more!
---
This video is based on the question https://stackoverflow.com/q/68467065/ asked by the user 'Sebastian Lore' ( https://stackoverflow.com/u/10371394/ ) and on the answer https://stackoverflow.com/a/68467407/ provided by the user 'v.ladynev' ( https://stackoverflow.com/u/3405171/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: What is the most efficient way to persist thousands of entities?

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Efficiently Persisting Thousands of Entities with Java and PostgreSQL

When handling large datasets, like CSV files containing millions of records, the challenge often lies in the efficient persistence of these entities into a database. If you're working with PostgreSQL and need to import a CSV file containing over 2 million records, you may wonder about the most effective way to speed up this process. This guide provides a detailed solution to this common problem, focusing on batch insertion techniques using Java with Spring Boot and Hibernate.

Understanding the Problem

In our example, you have a CSV file with 2,070,000 records, and you initially managed to parse and persist the data in approximately eight minutes using a single-threaded approach. The goal is to leverage multithreading to further decrease this time while ensuring efficient data insertion into your PostgreSQL database. The critical question here is: What is the most efficient way to persist such a large list of entities?

The Key Solution: Batch Inserts

Why Batch Inserts?

Batch inserts significantly reduce the number of database round trips needed for multiple records, making data persistence faster and more efficient. Instead of inserting records one by one, you can group multiple inserts into a single database call, drastically improving performance.

Step-by-Step Implementation

Here’s how you can implement batch inserts in your Spring Boot application:

1. Create a Custom Repository Interface

Start by defining a custom repository interface for batch operations. This interface will declare the method that your repository will implement.

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

2. Implement the Custom Repository

Next, create the implementation of the SomeRepositoryCustom interface. In this implementation, you’ll leverage the JdbcTemplate to facilitate batch processing.

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

3. Extend Your JpaRepository

To integrate this custom functionality, extend your JpaRepository with the new SomeRepositoryCustom interface.

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

4. Utilize the Batch Save Method

Finally, you can now call the batch save method to persist your records.

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

Important Considerations

Driver Configuration: Ensure that your database driver is configured properly to handle batch inserts. For MySQL, you may need to add rewriteBatchedStatements=true to your database URL. Additionally, enabling logging for the SQL driver (not Hibernate) can help verify that batch inserts are being executed as expected.

Splitting Records: Depending on your specific setup, you might consider splitting large datasets into smaller packets within your loop to strike a balance between memory use and performance. In many cases, the driver may handle this automatically, but it’s prudent to debug and confirm that this is functioning correctly.

Code Clarity: While using var can simplify your code, consider maintaining explicit types for better readability, particularly in large repositories.

Conclusion

Persisting large amounts of data efficiently is a common requirement in many applications. By implementing batch inserts, as outlined above, you can reduce the time taken to persist thousands of entities into PostgreSQL, making your application more responsive and capable of handling increased workloads. With the steps provided, you can ensure your batch inserts are optimized and ready for production use.

Remember, the method of persisting entities can have a significant impact on your application's performance. Always assess the tools and techniques you use to meet your data