Smart Thinking- New Series for this Blog: common performance bottleneck with complex SQL queries involving multiple left joins.
- Mark Kendall
- Apr 2
- 6 min read
It sounds like you're hitting a common performance bottleneck with complex SQL queries involving multiple left joins. Breaking down the data retrieval into a series of smaller, more focused queries executed in your Spring Boot application is a good strategy. Let's explore how you can achieve this in code and discuss the pros and cons of different approaches.
Can you just use the async keyword and chain them together?
Yes, you can definitely use the `async` keyword in Spring Boot to achieve a form of sequential, non-blocking data fetching. Here's a basic idea:
```java
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;
import org.springframework.transaction.annotation.Transactional;
import java.util.List;
import java.util.concurrent.CompletableFuture;
@Service
public class DataService {
@Autowired
private Repository1 repository1;
@Autowired
private Repository2 repository2;
@Autowired
private Repository3 repository3;
@Async
public CompletableFuture<DataResult> fetchDataSeriallyAsync(Long id) {
return repository1.findById(id)
.thenCompose(entity1 -> {
if (entity1.isPresent()) {
return repository2.findByForeignKey1(entity1.getId())
.thenCompose(entities2 -> {
// Process entities2 if needed
if (!entities2.isEmpty()) {
return repository3.findByForeignKey2In(entities2.stream().map(Entity2::getId).toList())
.thenApply(entities3 -> {
// Combine and transform the data
return new DataResult(entity1.get(), entities2, entities3);
});
} else {
return CompletableFuture.completedFuture(new DataResult(entity1.get(), List.of(), List.of()));
}
});
} else {
return CompletableFuture.completedFuture(null); // Or handle the absence of entity1
}
});
}
public DataResult fetchDataSeriallyBlocking(Long id) {
Entity1 entity1 = repository1.findById(id).orElse(null);
if (entity1 != null) {
List<Entity2> entities2 = repository2.findByForeignKey1(entity1.getId());
List<Long> ids2 = entities2.stream().map(Entity2::getId).toList();
List<Entity3> entities3 = repository3.findByForeignKey2In(ids2);
return new DataResult(entity1, entities2, entities3);
}
return null;
}
}
```
Explanation:
* `@Async`: This annotation marks the `fetchDataSeriallyAsync` method as asynchronous. Spring will execute this method in a separate thread from its thread pool.
* `CompletableFuture`: This is the core of asynchronous programming in Java. Each repository call returns a `CompletableFuture` representing the eventual result of the operation.
* `thenCompose()`: This method allows you to chain asynchronous operations. It takes the result of the previous `CompletableFuture` and returns a new `CompletableFuture` that depends on that result. This is crucial for sequential execution where the second query needs data from the first.
* `thenApply()`: This method allows you to transform the result of a `CompletableFuture` synchronously. In this case, it's used to combine the results of the final query.
Do you have to use WebFlux?
No, you do not have to use WebFlux to implement this approach with asynchronous operations and `CompletableFuture`. The `@Async` annotation and `CompletableFuture` are part of the standard Java concurrency utilities and can be used in a traditional Spring MVC application.
WebFlux (Reactive Spring) is an alternative, but it's a more significant shift:
*Reactive Programming:** WebFlux leverages reactive programming concepts with Project Reactor (`Mono` and `Flux`). This provides a more declarative and potentially more efficient way to handle asynchronous data streams.
*Non-Blocking I/O:** WebFlux is built on non-blocking I/O, which can lead to better resource utilization, especially under high concurrency.
*Different Programming Model:** Adopting WebFlux requires a shift in your programming paradigm. You'll work with reactive streams instead of traditional imperative code.
Chaining them together and then getting the results and transforming:
As shown in the `fetchDataSeriallyAsync` example, `thenCompose()` is the mechanism for chaining the asynchronous operations. The final `thenApply()` (or subsequent `thenCompose()` calls for further data fetching) is where you would perform the transformation and combine the results from the different queries.
What's the better way here?
The "better" way depends on your specific needs and constraints:
1. Asynchronous with `@Async` and `CompletableFuture` (in a traditional Spring MVC application):
Pros:
*Improved Responsiveness:** The main request thread is not blocked while waiting for each database query to complete. This can significantly improve the responsiveness of your API, especially under load.
*Relatively Easier to Adopt:** If you're already familiar with Spring MVC and basic concurrency, this approach is generally easier to implement than a full migration to WebFlux.
*Clear Sequential Logic:** The `thenCompose()` calls clearly define the order of operations.
Cons:
*Thread Context Switching:** There's still overhead associated with thread context switching managed by Spring's task executor.
*Potential for Callback Hell (if not careful):** Deeply nested `thenCompose()` calls can become difficult to read and maintain. Consider extracting logic into separate methods.
*Blocking Operations Within Async Tasks:** Ensure that the repository methods you are calling are themselves non-blocking (if possible) or that the thread pool used by `@Async` is appropriately sized to handle potential blocking.
2. Reactive with WebFlux:
Pros:
*Higher Concurrency and Scalability:** Non-blocking I/O and the reactive programming model can lead to better resource utilization and higher throughput under heavy load.
*More Declarative Asynchronous Code:** Reactive streams offer powerful operators for composing and transforming asynchronous data flows.
*Backpressure Handling:** Reactive streams have built-in mechanisms for handling backpressure, preventing your application from being overwhelmed by upstream data sources.
Cons:
*Steeper Learning Curve:** Reactive programming concepts and the WebFlux framework can have a steeper learning curve compared to traditional Spring MVC.
*Ecosystem Considerations:** You might need to ensure that your data access layer (e.g., reactive Spring Data repositories) and other dependencies are also reactive.
*Debugging Complexity:** Debugging reactive applications can sometimes be more challenging.
3. Staying with Blocking Calls (your `fetchDataSeriallyBlocking` example):
Pros:
*Simpler Code:** The code is straightforward and easy to understand.
*No Need for Asynchronous Concepts:** You don't need to deal with `CompletableFuture` or reactive streams.
Cons:
*Performance Bottleneck:** The main request thread will be blocked while waiting for each database query, leading to poor scalability and responsiveness under load. This is likely the issue you're trying to solve.
Recommendation:
For your situation, starting with asynchronous operations using `@Async` and `CompletableFuture` within your existing Spring MVC application is likely the most pragmatic first step. It allows you to achieve non-blocking data fetching and improve responsiveness without a complete architectural overhaul.
Steps to Implement the `@Async` Approach:
*Enable Asynchronous Processing:** Add the `@EnableAsync` annotation to one of your configuration classes (e.g., your main application class).
*Create Asynchronous Service Methods:** Mark your data fetching service methods with `@Async` and have them return `CompletableFuture<T>`.
*Chain Asynchronous Operations:** Use `thenCompose()` to sequentially execute dependent queries and `thenApply()` to transform and combine the results.
*Handle Results in Your Controller:** In your REST controller, you can return the `CompletableFuture` directly. Spring will handle the asynchronous execution and return the result when it becomes available (typically wrapped in a `ResponseEntity`).
When to Consider WebFlux:
If you anticipate very high concurrency, need more sophisticated asynchronous data stream processing, or are building a fully reactive application, then migrating to WebFlux might be beneficial in the long run. However, it's a more significant undertaking.
Important Considerations:
*Error Handling:** Implement proper error handling within your `CompletableFuture` chains using methods like `exceptionally()` or `handle()`.
*Transaction Management:** Be mindful of transaction boundaries when performing multiple database operations. You might need to manage transactions programmatically if the operations span multiple asynchronous calls.
*Thread Pool Configuration:** You can customize the thread pool used by `@Async` if needed.
*Data Transformation Logic:** Keep your transformation logic clear and well-tested.
By breaking down your complex queries into smaller, sequential asynchronous operations in your Spring Boot application, you can significantly improve the performance and responsiveness of your REST APIs. Start with the `@Async` and `CompletableFuture` approach as it offers a good balance of benefits with a manageable learning curve.
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