kotlin-backend-jpa-entity-mapping

name	kotlin-backend-jpa-entity-mapping
description	Model Kotlin persistence code correctly for Spring Data JPA and Hibernate. Covers entity design, identity and equality, uniqueness constraints, relationships, fetch plans, and common ORM (Object-Relational Mapping) traps specific to Kotlin. Use when creating or reviewing JPA (Java Persistence API) entities, diagnosing N+1 or LazyInitializationException, placing indexes and uniqueness rules, or preventing Kotlin-specific bugs such as data class entities and broken equals/hashCode.
license	Apache-2.0
metadata	{"author":"JetBrains","version":"1.0.0"}

JPA Entity Mapping for Kotlin

Kotlin's data class is natural for DTOs but dangerous for JPA entities. Hibernate relies on identity semantics that data class breaks: equals/hashCode over all fields corrupts Set/Map membership after state changes, and auto-generated copy() creates detached duplicates of managed entities.

This skill teaches correct entity design, identity strategies, and uniqueness constraints for Kotlin + Spring Data JPA projects.

Entity Design Rules

• Never use data class for JPA entities. Use a regular class. Keep data class for DTOs.
• Keep transport DTOs and persistence entities separate unless the project clearly uses a shared model.
• Model required columns as non-null only when object construction and persistence lifecycle make it safe.
• Use lateinit only when the project already accepts that tradeoff and the lifecycle is safe.
• Verify kotlin("plugin.jpa") or equivalent no-arg support when JPA entities exist.
• Verify classes and members are compatible with proxying where needed.

Identity and Equality

• Never accept all-field equals/hashCode generated by data class on an entity.
• Follow project conventions when they already define an identity strategy.
• If no convention exists, use ID-based equality with a stable hashCode.
• For DB-generated IDs, model the unsaved state with nullable var id: Long? = null and a protected set; do not use 0L as a sentinel value.
• Be explicit about mutable fields and lazy associations when discussing equality.

Broken: data class Entity

// WRONG: data class generates equals/hashCode from ALL fields,
// and the generated ID uses a 0 sentinel instead of null
data class Order(
    @Id @GeneratedValue val id: Long = 0,
    var status: String,
    var total: BigDecimal
)
// BUG: order.status = "SHIPPED"; set.contains(order) → false (hash changed)
// BUG: Hibernate proxy.equals(entity) → false (proxy has lazy fields uninitialized)

Correct: Regular Class with ID-Based Identity

@Entity
@Table(name = "orders")
class Order(
    @Column(nullable = false)
    var status: String,

    @Column(nullable = false)
    var total: BigDecimal
) {
    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    var id: Long? = null
        protected set

    override fun equals(other: Any?): Boolean {
        if (this === other) return true
        if (other !is Order) return false
        return id != null && id == other.id
    }

    override fun hashCode(): Int = javaClass.hashCode()

    // toString must NOT reference lazy collections
    override fun toString(): String = "Order(id=$id, status=$status)"
}

Key rules:

• equals compares by ID only — stable under dirty tracking and proxy unwrapping
• hashCode returns class-based constant — avoids Set/Map corruption after persist
• toString excludes lazy-loaded relations — prevents LazyInitializationException
• Constructor params are mutable entity fields; DB-generated id is nullable with a protected setter

Uniqueness Constraints

When an API must be idempotent (e.g., "reserve stock for order X"), enforce uniqueness at both layers: database constraint for correctness, application check for clean errors.

Broken: No Duplicate Guard

@Service
class ReservationService(private val repo: ReservationRepository) {
    @Transactional
    fun createReservation(variantId: Long, orderId: String, qty: Int): Reservation {
        // BUG: no check — duplicates silently accumulate
        return repo.save(Reservation(variantId = variantId, orderId = orderId, quantity = qty))
    }
}

Correct: Database Constraint + Application Guard

@Entity
@Table(
    name = "reservations",
    uniqueConstraints = [
        UniqueConstraint(columnNames = ["variant_id", "order_id"])
    ]
)
class Reservation(
    @Column(name = "variant_id", nullable = false)
    val variantId: Long,

    @Column(name = "order_id", nullable = false)
    val orderId: String,

    @Column(nullable = false)
    var quantity: Int
) {
    @Id @GeneratedValue(strategy = GenerationType.IDENTITY)
    var id: Long? = null
        protected set
}

interface ReservationRepository : JpaRepository<Reservation, Long> {
    fun findByVariantIdAndOrderId(variantId: Long, orderId: String): Reservation?
}

@Service
class ReservationService(private val repo: ReservationRepository) {
    @Transactional
    fun createReservation(variantId: Long, orderId: String, qty: Int): Reservation {
        repo.findByVariantIdAndOrderId(variantId, orderId)?.let {
            throw IllegalStateException(
                
            )
        }
         repo.save(Reservation(variantId = variantId, orderId = orderId, quantity = qty))
    }
}

Key rules:

• Database constraint is mandatory — application checks alone have race conditions
• Application check provides clean error messages — without it, users get raw DataIntegrityViolationException
• Both layers together: application catches the common case, database catches the race
• Spring Data derives findByXAndY queries automatically

Query and Fetch Rules

• Diagnose N+1 by looking at actual query count or SQL logs, not by guessing from annotations.
• Prefer targeted fetch solutions: @EntityGraph, JOIN FETCH, batch fetching, or DTO projection.
• Be careful with collection fetch joins plus pagination — call out the tradeoff.
• Use indexes and uniqueness constraints to support real query patterns.

Common ORM Traps

• Bidirectional associations: maintain both sides in domain methods. Half-updated graphs cause subtle bugs.
• orphanRemoval vs cascade remove: not interchangeable. Explain lifecycle semantics before choosing.
• Lazy load triggers: toString, debug logging, JSON serialization, and IDE inspection can all trigger lazy loads.
• Bulk updates/deletes: bypass persistence context and lifecycle callbacks. Subsequent reads may be stale.
• Multiple bag fetches: can cause Cartesian explosion. Verify the ORM can execute collection-heavy fetch plans safely.
• Set + mutable equality: collection membership can break after entity state changes.
• @Version: the clearest optimistic concurrency mechanism when concurrent updates matter.
• open-in-view disabled: DTO mapping touching lazy fields must happen inside a transaction boundary.

Guardrails

• Do not use data class for JPA entities.
• Do not recommend FetchType.EAGER everywhere to silence lazy loading symptoms.
• Do not expose entities directly through API responses by default.
• Do not claim an N+1 fix without explaining how the fetch plan changes query behavior.