Performance
These guidelines focus on optimizing the performance of Spring Boot applications using Hibernate, with an emphasis on data economy, large-scale testing, paging, and general SQL database best practices. You can find more best practices in the Database Guidelines section.
1. Data Economy
Database-Level Filtering
Ensure that all filtering is done at the database level rather than in memory. This approach minimizes data transfer to the application and reduces memory usage.
Example:
@Query("""
SELECT e
FROM Exercise e
WHERE e.course.id = :courseId
AND e.releaseDate >= :releaseDate
""")
List<Exercise> findExercisesByCourseAndReleaseDate(@Param("courseId") Long courseId, @Param("releaseDate") ZonedDateTime releaseDate);
Projections and DTOs
When only a subset of fields is needed, use projections or Data Transfer Objects (DTOs) instead of fetching entire entities. This reduces the amount of data loaded and improves query performance.
Example:
@Query("""
SELECT new com.example.dto.ExerciseDTO(e.id, e.title)
FROM Exercise e
WHERE e.course.id = :courseId
AND e.releaseDate >= :releaseDate
""")
List<ExerciseDTO> findExerciseDTOsByCourseAndReleaseDate(@Param("courseId") Long courseId, @Param("releaseDate") ZonedDateTime releaseDate);
2. Large Scale Testing
Test with Realistic Data Loads
Given that courses can have up to 2,000 students, simulate this scale during testing to identify potential performance bottlenecks when handling large amounts of data.
Benchmarking
Perform load testing to ensure that the application can handle the expected volume of data efficiently.
Example:
Use tools like JMeter or Gatling to simulate concurrent users and large datasets.
3. Paging
Implement Paging for Large Results
For queries that return large datasets, implement pagination to avoid loading too much data into memory at once.
Example:
Page<Exercise> findByCourseId(Long courseId, Pageable pageable);
Caution with Collection Fetching and Pagination
Avoid combining LEFT JOIN FETCH with pagination, as this can cause performance issues or even fail due to the Cartesian Product problem.
Example:
Instead of:
@Query("""
SELECT c
FROM Course c
LEFT JOIN FETCH c.exercises
WHERE c.id = :courseId
""")
Page<Course> findCourseWithExercises(@Param("courseId") Long courseId, Pageable pageable);
Do:
@Query("""
SELECT c
FROM Course c
WHERE c.id = :courseId
""")
Course findCourseById(@Param("courseId") Long courseId);
// Fetch exercises in a separate query if needed
@Query("""
SELECT e
FROM Exercise e
WHERE e.course.id = :courseId
""")
List<Exercise> findExercisesByCourseId(@Param("courseId") Long courseId);
You can find out more on https://vladmihalcea.com/hibernate-query-fail-on-pagination-over-collection-fetch
4. Avoiding the N+1 Issue
Eager Fetching and Left Join Fetch
The N+1 query issue occurs when lazy-loaded collections cause multiple queries to be executed — one for the parent entity and additional queries for each related entity. To avoid this issue, consider using eager fetching or JOIN FETCH for collections that are critical to performance.
Example:
@Query("""
SELECT e
FROM Exercise e
JOIN FETCH e.submissions
WHERE e.course.id = :courseId
""")
List<Exercise> findExercisesWithSubmissions(@Param("courseId") Long courseId);
In this example, the query fetches exercises along with their submissions in a single query, avoiding the N+1 problem. Be cautious, however, as fetching too many collections eagerly can lead to performance degradation due to large result sets.
5. Optimal Use of Left Join Fetch
Balance Between Queries
While reducing the number of queries by using LEFT JOIN FETCH is often beneficial, overusing this strategy can lead to performance issues, especially when fetching multiple OneToMany relationships. As a best practice, avoid fetching more than three OneToMany collections in a single query.
Example:
@Query("""
SELECT c
FROM Course c
LEFT JOIN FETCH c.exercises e
LEFT JOIN FETCH e.participations
WHERE c.id = :courseId
""")
Course findCourseWithExercisesAndParticipations(@Param("courseId") Long courseId);
This query efficiently fetches a course with its exercises and their submissions. However, if more collections are added to the fetch, consider splitting the query into multiple parts to prevent large result sets and excessive memory usage.
Selective Fetching
Use lazy loading by default, and override with JOIN FETCH only when necessary for performance-critical queries. This approach minimizes the risk of performance degradation due to large query results.
Example:
@Entity
public class Exercise {
@OneToMany(fetch = FetchType.LAZY, mappedBy = "exercise")
private List<Participation> participations;
// Other fields and methods
}
By default, participations are lazily loaded. When you need to fetch them, use a specific JOIN FETCH query only in performance-sensitive situations. Alternatively, consider using @EntityGraph
to define fetch plans for specific queries.
6. General SQL Database Best Practices
Indexing
Indexes are critical for query performance, especially on columns that are frequently used in WHERE clauses, JOIN conditions, or are sorted. Ensure that all key fields, such as releaseDate and courseId, are properly indexed.
Example:
Create an index on the releaseDate column to speed up queries filtering exercises by date:
CREATE INDEX idx_exercise_release_date ON exercise(release_date);
Normalization vs. Denormalization
While normalization reduces data redundancy, it can lead to complex queries with multiple joins. In scenarios where read performance is critical, consider denormalizing certain tables to reduce the number of joins. However, always balance this against potential issues such as data inconsistency and increased storage requirements.
Use of Foreign Keys
Maintain foreign key constraints to enforce data integrity. However, be aware of the potential performance impact on insert, update, and delete operations in high-load scenarios. Proper indexing can help mitigate these effects.
Example:
ALTER TABLE submission ADD CONSTRAINT fk_exercise FOREIGN KEY (exercise_id) REFERENCES exercise(id);
This foreign key ensures that submissions are always linked to a valid exercise, maintaining data integrity.
Query Optimization
Regularly review and optimize SQL queries to ensure they are performing efficiently. Use tools like EXPLAIN to analyze query execution plans and make adjustments where necessary.
Example:
EXPLAIN SELECT * FROM exercise WHERE course_id = 1 AND release_date > '2024-01-01';
Use the EXPLAIN output to identify slow-running queries and optimize them by adding indexes, rewriting queries, or adjusting table structures.
Avoid Transactions
Transactions are generally very slow and should be avoided when possible.
By following these best practices, you can build Spring Boot applications with Hibernate that are optimized for performance, even under the demands of large-scale data processing.