It looks like plain string interpolation...
val user: User? = kueryClient .sql { +"SELECT * FROM users WHERE user_id = $userId" } .singleOrNull()
...but it executes as a parameterized query. A Kotlin compiler plugin rewrites the interpolated runtime value into named parameter binding at compile time — so you get the readability of raw SQL without the risk of SQL injection:
SELECT * FROM users WHERE user_id = :p0 -- :p0 = userId, bound as a named parameter
♥️ Love SQL- ORM libraries are convenient, but they each require learning their own DSL, which we believe is a steep cost. Kuery Client emphasizes writing SQL as it is.
- 🛡️ Safe by design
- Interpolated runtime values are converted into bind parameters by the compiler plugin, so the normal SQL-building path never concatenates them into the SQL text.
- ✅ Compile-time checks
- The compiler plugin warns when a SQL string cannot be converted safely, and can optionally validate SQL syntax — mistakes surface at compile time, not in production.
- 🍃 Built on Spring Data
- Kuery Client is implemented on top of
spring-data-r2dbcandspring-data-jdbc. Use whichever you prefer. You can keep using Spring's ecosystem as is, such as@Transactional.
- Kuery Client is implemented on top of
- 🔭 Observability
- It supports Micrometer Observation, so you can collect and customize metrics, tracing, and logging.
- 🧩 Extensible
- When dealing with complex data schemas, you often want to share common query logic. Kotlin's extension functions make this easy.
A Taste of the DSL
Dynamic SQL is just Kotlin — if / when / loops, no template syntax to learn.
Whether using R2DBC or JDBC, the way of writing is almost the same.
data class User(...) class UserRepository(private val kueryClient: KueryClient) { suspend fun findById(userId: Int): User? = kueryClient .sql { +"SELECT * FROM users WHERE user_id = $userId" } .singleOrNull() suspend fun search(status: String, vip: Boolean?): List<User> = kueryClient .sql { +""" SELECT * FROM users WHERE status = $status """ if (vip != null) { +"AND vip = $vip" } } .list() suspend fun insertMany(users: List<User>): Long = kueryClient .sql { +"INSERT INTO users (username, email)" // useful helper function values(users) { listOf(it.username, it.email) } } .rowsUpdated() }
There are only two things you need to remember:
- Concatenate SQL strings using
+(unaryPlus). Express logic such asifdirectly in Kotlin. - Bind parameters using string interpolation.
Motivation
We have used numerous ORM libraries, but in the end, we preferred libraries like MyBatis that allow writing SQL directly.
To construct SQL dynamically, custom template syntax (such as if/foreach) is often used, but we prefer to write logic using the syntax provided by the programming language as much as possible. We want to write dynamic SQL using Kotlin syntax, similar to kotlinx.html.
To meet these needs, we implemented Kuery Client.
Kuery Client simply provides the SQL builder shown above on top of the well-established
spring-data-r2dbc / spring-data-jdbc. It is designed to be usable alongside plain
spring-data code, so you can start small.
Getting Started
Kuery Client requires Java 17 or later and Gradle 8.4 or later.
Install
Replace <version> with the latest version, which you can find in the Maven Central badge above or on
the Getting Started page.
plugins {
id("dev.hsbrysk.kuery-client") version "<version>"
}
dependencies {
implementation("dev.hsbrysk.kuery-client:kuery-client-spring-data-r2dbc:<version>")
// or, implementation("dev.hsbrysk.kuery-client:kuery-client-spring-data-jdbc:<version>")
}Build KueryClient
For kuery-client-spring-data-r2dbc:
val connectionFactory: ConnectionFactory = ... val kueryClient = SpringR2dbcKueryClient.builder() .connectionFactory(connectionFactory) .build()
For kuery-client-spring-data-jdbc:
val dataSource: DataSource = ... val kueryClient = SpringJdbcKueryClient.builder() .dataSource(dataSource) .build()
Let's Use It
val userId = "..." val user: User? = kueryClient .sql { +"SELECT * FROM users WHERE user_id = $userId" } .singleOrNull()
Documentation
More details are on the document site:
- Getting Started — choose R2DBC or JDBC and run the first query
- Building SQL — interpolation, collections, dynamic SQL, and reusable fragments
- Fetching Results — terminal operations, streaming, generated values, and statement options
- Compile-Time Checks — the compiler diagnostics that catch unsafe SQL, and the strict option
- SQL Syntax Check — opt-in compile-time validation of your SQL syntax
- Transaction / Type Conversion / Row Mapping
- Observation — Micrometer-based metrics, tracing, and logging
- Configuration — Gradle plugin and runtime builder options
- Supported Platforms / Compatibility — tested databases and supported Kotlin / Spring versions
For AI assistants and LLM-based tools, the entire documentation is also available in the llms.txt format: llms.txt (index) and llms-full.txt (full content in a single file).
Examples
Runnable example projects live in examples/: