How Can I Efficiently Retrieve 90 Million Database Records Without Memory Exhaustion?

Using ScrollableResults to Handle Large Data Sets

When working with massive datasets, optimizing data retrieval becomes crucial. In this scenario, using Hibernate's ScrollableResults to read 90 million database records can result in memory exhaustion due to attempted loading of the entire dataset into RAM.

To avoid this, the recommended approach is to utilize the setFirstResult and setMaxResults methods. While iterating through the results, these methods allow specification of specific portions of the dataset to be retrieved at a time, effectively avoiding memory bottlenecks. However, as the offset increases, the performance may degrade.

An alternative solution is to employ a custom SQL query approach. By incrementally retrieving subsets of data based on an increasing offset, you can reduce memory overhead. The following query template demonstrates this strategy:

SELECT * 
FROM person 
WHERE id > <offset> 
AND <other_conditions> 
ORDER BY id asc  
LIMIT <batch_size>

This query retrieves a batch of records with an ID greater than a specified offset, filtering based on any additional conditions. The incremental nature of this approach ensures efficient data retrieval without overwhelming memory resources.

Additionally, optimizing the MySQL query itself can improve performance. Using appropriate indexes and ensuring optimized conditions can significantly reduce processing times, making this method a viable solution for handling large datasets.

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