Lazy Loading & the N+1 Query Problem — An In-depth Guide (JPA / Hibernate)
1. What is lazy loading?
Lazy loading defers loading of an entity’s associations until the code accesses them. In JPA/Hibernate, collections like @OneToMany and @ManyToMany are lazy by default. That means fetching the parent entity (User) does not automatically hit the DB for its child collection (companies) until you call user.getCompanies().
Example entity
@Entity
class User {
@Id private Long id;
private String name;
@OneToMany(mappedBy = "owner") // LAZY by default
private List<Company> companies;
}
Calling userRepository.findById(1L) will load the User only. Accessing user.getCompanies() triggers a separate SQL query at that time.
2. Two common problems lazy loading causes
LazyInitializationException
Occurs when you try to access a lazily loaded association after the persistence session (EntityManager / Hibernate Session) is closed. Common in layered apps where the service returns entities and the controller or view accesses associations.
N+1 Query Problem
When you load a collection of parents, then access each parent's lazy association in a loop, you end up with 1 query to fetch parents + N queries to fetch children — the classic N+1. This causes excessive DB load and latency.
3. Concrete examples (code + SQL)
Scenario: N+1 in a loop
List<User> users = userRepository.findAll(); // 1 query
for (User u : users) {
System.out.println(u.getCompanies().size()); // triggers 1 query per user
}
SQL produced (simplified):
-- Query 1
SELECT id, name FROM users;
-- Query 2..N+1
SELECT id, name, user_id FROM companies WHERE user_id = 1;
SELECT id, name, user_id FROM companies WHERE user_id = 2;
-- ...
Eliminate N+1 with JOIN FETCH
@Query("select u from User u left join fetch u.companies where u.id = :id")
User findUserWithCompanies(@Param("id") Long id);
SQL (single query):
SELECT u.*, c.*
FROM users u
LEFT JOIN companies c ON c.user_id = u.id
WHERE u.id = ?;
4. Why N+1 is bad — cost analysis
Each SQL query has network latency, DB parse/planning and execution overhead. If each query costs ~5–20ms, 100 queries add 0.5–2s. For user-facing endpoints, that latency is unacceptable. N+1 also increases DB CPU, connection churn and risk of locks.
| Cost Component | Effect |
|---|---|
| Network round-trip | Dominant cost when queries are many |
| DB CPU / planning | Repeated small queries increase load |
| Connection overhead | More connections/longer transactions |
5. Detection: how to spot N+1 in your app
- Enable SQL logging in dev and look for repeated similar queries.
- Use APM (New Relic, Datadog) to inspect many DB calls per request.
- Instrument tests to assert query counts (use
datasource-proxyor similar). - Code review: loops that access associations after fetching parents are suspicious.
6. Fixes & mitigation techniques
Rule of thumb: apply the minimal, local fix that satisfies the feature. Don’t change global fetch strategies.
6.1 JOIN FETCH
Use for specific queries where you need parent + children together.
@Query("select distinct u from User u left join fetch u.companies where u.id = :id")
User findUserWithCompanies(@Param("id") Long id);
Pros: single query, explicit. Cons: duplicates, pagination issues, memory blowups if collections are huge.
6.2 @EntityGraph
@EntityGraph(attributePaths = {"companies"})
Optional<User> findById(Long id);
Declarative and reusable. Same caveats as fetch joins.
6.3 DTO / projection queries
Return only the fields the view needs. Works well with pagination.
@Query("select new com.example.dto.UserSummary(u.id, u.name, count(c)) " +
"from User u left join u.companies c group by u.id")
Page<UserSummary> findUsersSummary(Pageable pageable);
6.4 Batch fetching (@BatchSize)
Instruct Hibernate to load children in batches, reducing N queries to ~N/batchSize.
@OneToMany(mappedBy = "owner")
@BatchSize(size = 20)
private List<Company> companies;
6.5 Manual initialization
User u = repo.findById(id).orElseThrow();
Hibernate.initialize(u.getCompanies()); // inside a transaction
6.6 Caching
Second-level or query caching can reduce DB hits for hot data but introduces cache invalidation complexity.
7. Caveats, pitfalls and tradeoffs
Pagination + JOIN FETCH
Fetching collections and paginating in the same query leads to wrong pagination because DB rows correspond to parent-child pairs. Solutions: two-step fetch (IDs page → fetch associations), or DTOs.
Duplicate parent rows & DISTINCT
JPQL can return duplicate parent objects at the SQL level. Use SELECT DISTINCT u or rely on Hibernate's in-memory dedupe. DISTINCT may add DB cost.
Multiple bag fetch exception
Hibernate throws MultipleBagFetchException when attempting to JOIN FETCH more than one collection mapped as List. Use Set, DTOs, or separate queries.
Memory blowups
Eagerly loading huge collections can blow heap. Stream results or limit fetch sizes for bulk exports.
8. Use cases — when to use each solution
| Use case | Recommended approach |
|---|---|
| Single user profile with companies | JOIN FETCH or @EntityGraph |
| Paginated user list with company counts | DTO/projection (aggregate) |
| Background bulk export | Streaming + manual fetch with batching |
| High-read mostly-static data | Second-level cache + read-only DTOs |
9. Checklist / quick reference
- Enable SQL logs in dev to reproduce issues.
- Find repeated
SELECT ... WHERE fk = ?patterns. - Prefer query-level fixes:
JOIN FETCH,@EntityGraph, DTOs. - For paginated endpoints do two-step fetch: IDs page → associations for IDs.
- Use
@BatchSizefor incremental improvements with low code churn. - Write tests that assert query counts on critical endpoints.
10. Summary & recommended patterns
Keep collections lazy by default. Detect N+1 with logs and tests. Fix locally with targeted queries (JOIN FETCH / EntityGraph) or use DTOs for paginated read endpoints. Use batch fetching as a pragmatic middle ground and reserve caching for mostly-static hot data.
Recommended pattern examples
Profile page
Repository method: findUserWithCompanies(Long id) using JOIN FETCH.
Users list (paged)
Use DTO projection that returns aggregated values (counts) or do two-step fetch using IDs paging + batch fetch of associations.
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