---
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`.
- Be explicit about mutable fields and lazy associations when discussing equality.

### Broken: `data class` Entity

```kotlin
// WRONG: data class generates equals/hashCode from ALL fields
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

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

    @Column(nullable = false)
    var total: BigDecimal
) {
    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    val id: Long = 0

    override fun equals(other: Any?): Boolean {
        if (this === other) return true
        if (other !is Order) return false
        return id != 0L && 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; `id` is `val` with default

## 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

```kotlin
@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

```kotlin
@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)
    val id: Long = 0
}

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(
                "Reservation already exists for variant=$variantId, order=$orderId"
            )
        }
        return 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.
