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Querying with JPA

Go beyond basic repository methods to master JPQL, the Criteria API, pagination, and performance pitfalls like the N+1 select problem.

Data LayerIntermediate11 min readJul 10, 2026
Analogies

JPQL: Querying Objects, Not Tables

JPQL (Jakarta Persistence Query Language) looks like SQL but operates on entities and their fields rather than tables and columns — SELECT b FROM Book b WHERE b.author.lastName = :lastName references the Book entity and navigates the author association directly, and Hibernate translates it into the appropriate SQL joins against the underlying books and authors tables. This object-oriented querying is what lets JPQL remain portable across database vendors, since the same JPQL string produces correct SQL whether the underlying database is PostgreSQL, MySQL, or Oracle.

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Cricket analogy: JPQL is like describing a fielding instruction in cricket terms ('move point square') rather than GPS coordinates on the ground — the abstraction is portable across any stadium, just as JPQL is portable across any database vendor.

The Criteria API for Dynamic Queries

When a query's shape depends on runtime conditions — an optional search filter here, an optional date range there — building a JPQL string with conditional concatenation becomes error-prone and hard to read. The JPA Criteria API solves this by letting you build a query programmatically using CriteriaBuilder, CriteriaQuery, and Root<T> objects, adding Predicate objects to a list only when the corresponding filter is actually present, and combining them with cb.and(predicates.toArray(new Predicate[0])) at the end.

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Cricket analogy: The Criteria API is like a captain setting a field plan dynamically overs before the game, adding a slip only if the pitch is seaming and a deep square leg only if the batter is a puller — assembled conditionally rather than fixed in advance.

java
public List<Book> search(String titleKeyword, BookStatus status, Integer sinceYear) {
    CriteriaBuilder cb = entityManager.getCriteriaBuilder();
    CriteriaQuery<Book> query = cb.createQuery(Book.class);
    Root<Book> book = query.from(Book.class);

    List<Predicate> predicates = new ArrayList<>();
    if (titleKeyword != null) {
        predicates.add(cb.like(cb.lower(book.get("title")), "%" + titleKeyword.toLowerCase() + "%"));
    }
    if (status != null) {
        predicates.add(cb.equal(book.get("status"), status));
    }
    if (sinceYear != null) {
        predicates.add(cb.greaterThanOrEqualTo(book.get("publishedYear"), sinceYear));
    }

    query.select(book).where(cb.and(predicates.toArray(new Predicate[0])));
    return entityManager.createQuery(query).getResultList();
}

Pagination and Sorting

Repository methods that accept a Pageable parameter — built with PageRequest.of(pageNumber, pageSize, Sort.by("title").ascending()) — return a Page<T> that carries not just the current page's content but also totalElements, totalPages, and hasNext(), which is exactly what a paginated REST API or UI needs. Under the covers, Spring Data JPA issues two queries for a Page result: the main query with LIMIT/OFFSET (or the vendor equivalent) for the current page's rows, and a separate COUNT query to compute the total, which is why returning Slice<T> instead (which only knows whether a next page exists, not the total count) can be a meaningful performance optimization when you don't need the total.

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Cricket analogy: A Page<T> result is like a ball-by-ball highlights reel that also tells you the total overs remaining in the innings, whereas a Slice<T> is like a highlights reel that only tells you whether there's another over coming, without needing the full innings total upfront.

For read-only queries, annotate the service method with @Transactional(readOnly = true). This lets Hibernate skip dirty-checking on loaded entities, and many database drivers use it as a hint to route the query to a read replica, both of which can meaningfully improve throughput on read-heavy endpoints.

Avoiding the N+1 Select Problem

The N+1 select problem occurs when you fetch a list of N parent entities with one query, and then, because an association is lazily loaded, Hibernate issues one additional query per parent to fetch each one's related collection — turning what should be a couple of queries into N+1 round trips to the database. The standard fixes are JOIN FETCH in a JPQL query (SELECT DISTINCT b FROM Book b JOIN FETCH b.author WHERE ...), an @EntityGraph annotation on the repository method naming which associations to eagerly fetch for that specific call, or, for collections, Hibernate's @BatchSize annotation which fetches related rows in batches instead of one query per parent.

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Cricket analogy: The N+1 problem is like a scorer fetching the full career stats for one batter, then realizing they need to make a separate lookup for each of the other ten players individually instead of pulling the whole team's stats in one query.

JOIN FETCH combined with a to-many association and Pageable together can silently break pagination — Hibernate has to load the entire result set into memory and paginate in application code (with a warning logged) rather than pushing LIMIT/OFFSET to the database, since a SQL-level LIMIT on a joined, duplicated row set would cut rows mid-collection. Use @EntityGraph or a separate count query strategy instead when you need both eager fetching and pagination together.

  • JPQL queries entities and their fields, not tables and columns directly, keeping queries portable across database vendors.
  • The Criteria API builds queries programmatically, ideal for dynamic filters that vary based on runtime conditions.
  • Page<T> includes total count and page metadata, while Slice<T> avoids the extra COUNT query for lighter-weight infinite scroll.
  • @Transactional(readOnly = true) allows Hibernate to skip dirty checking and can enable read-replica routing.
  • The N+1 select problem arises from lazy-loaded associations triggering one extra query per parent entity.
  • JOIN FETCH, @EntityGraph, and @BatchSize are the standard fixes for the N+1 select problem.
  • Combining JOIN FETCH on a collection with Pageable can break SQL-level pagination; prefer @EntityGraph in that case.

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