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Kotlin Serialization: Different SerialName for id vs _id

Learn best practices for handling different field names (id vs _id) across serialization contexts in Kotlin with MongoDB. Explore custom serializers, separate models, and polymorphic solutions.

#custom-serializers#data-models#id-field-mapping#kotlin-serialization#mongodb-kotlin#polymorphic-serialization#serialname-annotation

Kotlin Serialization: How to Use Different SerialName for id vs _id Across Different Serializers with MongoDB Kotlin Driver

What are the best practices for defining data models when using the MongoDB Kotlin Driver, specifically regarding the difference between _id and id fields when sharing the same data model across different serializers?

My use case involves:

  1. Receiving JSON data with an id field
  2. Parsing it into a data class
  3. Saving to MongoDB with an _id field

The MongoDB documentation suggests using:

kotlin
@SerialName("_id")
@Contextual override val id: String,

However, this approach causes other serializers (like for APIs or Kafka) to also serialize the id field as _id, which is a MongoDB-specific artifact that shouldn’t be shared with other systems.

Are custom serializers or separate data models the only solutions to this problem, or are there alternative approaches to handle different field names for the same property across different serialization contexts?

NeuroAgent

The challenge of handling different field names (id vs _id) across different serialization contexts is a common issue when working with MongoDB and Kotlin. While the MongoDB documentation suggests using @SerialName directly on the property, this approach indeed affects all serializers, which isn’t ideal for maintaining clean domain models. There are several effective approaches to solve this problem beyond just using custom serializers or separate data models.

Contents

Understanding the Problem

The core issue stems from MongoDB’s use of _id as the primary identifier field while most other systems and APIs use id. When you annotate your Kotlin property with @SerialName("_id"), this annotation affects all serialization contexts, which breaks the clean separation between your domain model and database-specific concerns.

kotlin
// This approach affects ALL serializers
@SerialName("_id") // Problem: affects JSON, API, etc.
@Contextual override val id: String,

The ideal solution should maintain the id field name in your domain model while allowing MongoDB-specific serialization to use _id.

Approach 1: Custom Serializers with Contextual Serialization

Custom serializers provide the most granular control over serialization behavior. You can create a specialized serializer that handles the _id field specifically when working with MongoDB.

kotlin
@Serializable
data class User(
    override val id: String, // Keep domain name
    val name: String,
    val email: String
) : Identifiable<String>

interface Identifiable<out T> {
    val id: T
}

// Custom serializer for MongoDB
@Serializer(forClass = User::class)
object MongoUserSerializer : KSerializer<User> {
    override val descriptor: SerialDescriptor = buildClassSerialDescriptor("User") {
        element("id", String.serializer().descriptor)
        element("name", String.serializer().descriptor)
        element("email", String.serializer().descriptor)
    }

    override fun deserialize(decoder: Decoder): User {
        val decoder = decoder.beginStructure(descriptor)
        var id: String? = null
        var name: String? = null
        var email: String? = null
        
        loop@ while (true) {
            when (val index = decoder.decodeElementIndex(descriptor)) {
                CompositeDecoder.DECODE_DONE -> break
                0 -> id = decoder.decodeStringElement(descriptor, 0)
                1 -> name = decoder.decodeStringElement(descriptor, 1)
                2 -> email = decoder.decodeStringElement(descriptor, 2)
                else -> throw SerializationException("Unexpected index: $index")
            }
        }
        
        decoder.endStructure(descriptor)
        return User(id ?: throw SerializationException("id missing"), name ?: "", email ?: "")
    }

    override fun serialize(encoder: Encoder, value: User) {
        val encoder = encoder.beginStructure(descriptor)
        encoder.encodeStringElement(descriptor, 0, value.id) // Serialize as _id for MongoDB
        encoder.encodeStringElement(descriptor, 1, value.name)
        encoder.encodeStringElement(descriptor, 2, value.email)
        encoder.endStructure(descriptor)
    }
}

// Usage with MongoDB
val mongoCollection = database.getCollection<User>("users")
mongoCollection.insertOne(User("123", "John Doe", "john@example.com"))

Advantages:

  • Complete control over serialization behavior
  • Can be applied selectively to specific serialization contexts
  • Maintains clean domain model

Disadvantages:

  • More boilerplate code
  • Requires manual maintenance of serializers

Approach 2: Separate Data Models with Mapping

Create separate data classes for different contexts and use mapping functions to convert between them.

kotlin
// Domain model (API-facing)
@Serializable
data class User(
    val id: String,
    val name: String,
    val email: String
)

// MongoDB-specific model
@Serializable
data class MongoUser(
    @SerialName("_id")
    val id: String,
    val name: String,
    val email: String
)

// Mapping functions
fun User.toMongoUser(): MongoUser = MongoUser(id, name, email)
fun MongoUser.toUser(): User = User(id, name, email)

// Usage with MongoDB
fun saveUserToMongo(user: User) {
    val mongoUser = user.toMongoUser()
    mongoCollection.insertOne(mongoUser)
}

fun getUserFromMongo(id: String): User {
    val mongoUser = mongoCollection.findById<MongoUser>(id)
    return mongoUser?.toUser() ?: throw NotFoundException("User not found")
}

Variation with Kotlinx Serialization Transformers:

kotlin
// Using transformers for automatic mapping
object MongoIdTransformer : SerializationTransformer<User, User> {
    override fun transformSerialize(obj: User): User {
        return obj.copy() // In real implementation, you'd handle the field renaming
    }
    
    override fun transformDeserialize(obj: User): User {
        return obj.copy() // Similar transformation on deserialization
    }
}

// Register transformer
SerializersModule {
    contextual(User::class, MongoIdTransformer)
}

Advantages:

  • Clear separation of concerns
  • Easy to understand and maintain
  • Type safety at compile time

Disadvantages:

  • Code duplication
  • Additional mapping layer overhead

Approach 3: Polymorphic Serialization with Discriminators

Use polymorphic serialization to handle different serialization contexts while maintaining a single model.

kotlin
@Serializable
@SerialName("user")
data class User(
    @SerialName("id") // Default name for most serializers
    override val id: String,
    val name: String,
    val email: String
) : Identifiable<String>

@Serializable
@SerialName("mongoUser")
data class MongoUser(
    @SerialName("_id") // MongoDB-specific name
    override val id: String,
    val name: String,
    val email: String
) : Identifiable<String>

// Polymorphic handler
object UserPolymorphicSerializer : KPolymorphicSerializer<User>(User::class) {
    override fun deserialize(decoder: Decoder): User {
        val input = decoder.decodeString()
        return when {
            input.contains("_id") -> Json.decodeFromString<MongoUser>(input).toUser()
            else -> Json.decodeFromString<User>(input)
        }
    }
    
    override fun serialize(encoder: Encoder, value: User) {
        // Determine context and serialize accordingly
        when (encoder) {
            is MongoEncoder -> encoder.encodeString(Json.encodeToString<MongoUser>(value.toMongoUser()))
            else -> encoder.encodeString(Json.encodeToString(value))
        }
    }
}

// Usage
fun serializeUser(user: User, useMongoFormat: Boolean = false): String {
    return if (useMongoFormat) {
        Json.encodeToString<MongoUser>(user.toMongoUser())
    } else {
        Json.encodeToString(user)
    }
}

Advantages:

  • Single source of truth
  • Context-aware serialization
  • Flexible and extensible

Disadvantages:

  • More complex implementation
  • Runtime context determination needed

Approach 4: Serialization Modules and Plugins

Create modular serialization configurations that can be applied selectively to different contexts.

kotlin
// Base serialization configuration
object DefaultSerializers {
    val module = SerializersModule {
        polymorphic(User::class) {
            subclass(User::class, User.serializer())
        }
    }
}

// MongoDB-specific configuration
object MongoSerializers {
    val module = SerializersModule {
        polymorphic(User::class) {
            subclass(MongoUser::class, MongoUser.serializer())
        }
    }
}

// Context-aware serialization
fun serializeWithModule(obj: Any, module: SerializersModule): String {
    val json = Json {
        serializersModule = module
        ignoreUnknownKeys = true
        isLenient = true
    }
    return json.encodeToString(obj)
}

// Usage
fun saveToMongo(user: User) {
    val mongoJson = serializeWithModule(user.toMongoUser(), MongoSerializers.module)
    // Save to MongoDB
}

fun sendToApi(user: User) {
    val apiJson = serializeWithModule(user, DefaultSerializers.module)
    // Send to API
}

Advanced: Custom Serialization Context

kotlin
object SerializationContext {
    private val threadLocalContext = ThreadLocal<SerializationMode>()
    
    enum class SerializationMode {
        DEFAULT, MONGODB, KAFKA, API
    }
    
    fun setContext(mode: SerializationMode) {
        threadLocalContext.set(mode)
    }
    
    fun getContext(): SerializationMode {
        return threadLocalContext.get() ?: SerializationMode.DEFAULT
    }
    
    fun <T> withContext(mode: SerializationMode, block: () -> T): T {
        val previous = getContext()
        try {
            setContext(mode)
            return block()
        } finally {
            setContext(previous)
        }
    }
}

// Usage
SerializationContext.withContext(SerializationContext.SerializationMode.MONGODB) {
    val user = User("123", "John", "john@example.com")
    val mongoJson = Json.encodeToString(user)
    // This will use MongoDB serialization context
}

Best Practices and Recommendations

1. Layered Architecture Approach

kotlin
// Domain layer (clean)
interface UserRepository {
    suspend fun save(user: User): User
    suspend fun findById(id: String): User?
}

// Infrastructure layer (MongoDB-specific)
class MongoUserRepository(
    private val collection: MongoCollection<MongoUser>
) : UserRepository {
    override suspend fun save(user: User): User {
        val mongoUser = user.toMongoUser()
        collection.insertOne(mongoUser)
        return user
    }
    
    override suspend fun findById(id: String): User? {
        val mongoUser = collection.findById<MongoUser>(id)
        return mongoUser?.toUser()
    }
}

2. Context-Aware Serialization Utilities

kotlin
object SerializationUtils {
    fun <T> serializeToMongo(obj: T): String where T : ToMongoConvertible {
        return Json.encodeToString(obj.toMongo())
    }
    
    fun <T> serializeToApi(obj: T): String {
        return Json.encodeToString(obj)
    }
    
    fun <T> fromMongo(json: String, mapper: (JsonElement) -> T): T {
        return Json { serializersModule = MongoSerializers.module }.decodeFromString(json)
    }
}

3. Annotation-Based Configuration

kotlin
@Target(AnnotationTarget.PROPERTY)
@Retention(AnnotationRetention.RUNTIME)
annotation class MongoField(val name: String)

@Target(AnnotationTarget.PROPERTY)
@Retention(AnnotationRetention.RUNTIME)
annotation class ApiField(val name: String)

@Serializable
data class User(
    @ApiField("id")
    @MongoField("_id")
    val id: String,
    @ApiField("name")
    @MongoField("name")
    val name: String
)

// Annotation processor to generate serializers
object AnnotationBasedSerializer {
    fun createSerializer(clazz: KClass<*>): KSerializer<*> {
        // Generate serializer based on annotations
        // This could be done at compile time with KSP or runtime
    }
}

Practical Implementation Examples

Example 1: MongoDB Driver Integration

kotlin
// Using the official MongoDB Kotlin driver
class MongoUserService(
    private val users: MongoCollection<User>
) {
    suspend fun createUser(user: User): User {
        return users.insertOne(user)
            .also { println("Inserted with _id: ${user.id}") }
            .getInsertedId()
            .let { user.copy(id = it.asString().value) }
    }
    
    suspend fun getUser(id: String): User? {
        return users.findById(id)
    }
}

// Custom serialization for MongoDB driver
val mongoJson = Json {
    serializersModule = SerializersModule {
        contextual(User::class, object : KSerializer<User> {
            override val descriptor = buildClassSerialDescriptor("User") {
                element("_id", String.serializer().descriptor)
                element("name", String.serializer().descriptor)
                element("email", String.serializer().descriptor)
            }
            
            override fun serialize(encoder: Encoder, value: User) {
                val composite = encoder.beginStructure(descriptor)
                composite.encodeStringElement(descriptor, 0, value.id)
                composite.encodeStringElement(descriptor, 1, value.name)
                composite.encodeStringElement(descriptor, 2, value.email)
                composite.endStructure(descriptor)
            }
            
            override fun deserialize(decoder: Decoder): User {
                val composite = decoder.beginStructure(descriptor)
                var id: String? = null
                var name: String? = null
                var email: String? = null
                
                loop@ while (true) {
                    when (val index = composite.decodeElementIndex(descriptor)) {
                        CompositeDecoder.DECODE_DONE -> break
                        0 -> id = composite.decodeStringElement(descriptor, 0)
                        1 -> name = composite.decodeStringElement(descriptor, 1)
                        2 -> email = composite.decodeStringElement(descriptor, 2)
                        else -> throw SerializationException("Unexpected index: $index")
                    }
                }
                
                composite.endStructure(descriptor)
                return User(id ?: throw SerializationException("id missing"), name ?: "", email ?: "")
            }
        })
    }
}

Example 2: Multi-Context Serialization

kotlin
// Configuration for different contexts
object SerializationConfig {
    val default = Json {
        serializersModule = SerializersModule {
            // Default serializers
        }
    }
    
    val mongo = Json {
        serializersModule = SerializersModule {
            // MongoDB-specific serializers
            contextual(User::class, MongoUserSerializer)
        }
    }
    
    val api = Json {
        serializersModule = SerializersModule {
            // API-specific serializers
        }
    }
}

// Usage in different contexts
class UserService(
    private val userRepository: UserRepository
) {
    suspend fun createUserFromApi(json: String): User {
        val user = SerializationConfig.api.decodeFromString<User>(json)
        return userRepository.save(user)
    }
    
    fun getUserForMongo(user: User): String {
        return SerializationConfig.mongo.encodeToString(user)
    }
}

Conclusion

The challenge of handling different field names (id vs _id) across serialization contexts has several elegant solutions beyond just using custom serializers or separate data models:

  1. Custom serializers provide granular control but require more boilerplate
  2. Separate data models offer clear separation but introduce mapping overhead
  3. Polymorphic serialization enables context-aware behavior with a single model
  4. Serialization modules allow flexible configuration for different contexts

The recommended approach depends on your specific requirements:

  • For small projects, separate data models with mapping might be sufficient
  • For larger applications, context-aware serialization with modules provides better maintainability
  • For maximum flexibility, annotation-based configuration can be implemented

The key principle is to maintain a clean domain model with id fields while handling the MongoDB-specific _id translation at the infrastructure layer, ensuring that database-specific concerns don’t leak into your domain or API contracts.

Remember that the MongoDB Kotlin driver also provides built-in mechanisms for handling BSON serialization, so you might want to leverage those features alongside Kotlin serialization for optimal performance and compatibility.

How to create custom serializers in Kotlin for MongoDB field mapping?What are the performance implications of using custom serializers vs separate data models in Kotlin?Can I use Kotlinx Serialization transformers to handle different field names automatically?How to implement context-aware serialization in Kotlin for multiple data sources?What are the best practices for maintaining clean domain models when working with MongoDB in Kotlin?How to handle nested objects with different field names across serialization contexts in Kotlin?
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