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Table of Contents
1. Use lightweight frameworks to improve processing efficiency
2. Database access optimization is a key link
3. Utilize thread pools and concurrency control policies
4. Monitoring and tuning cannot be ignored
Home Java javaTutorial Building High-Throughput Java Microservices

Building High-Throughput Java Microservices

Jul 16, 2025 am 02:12 AM
java

To build a high-throughput Java microservice, we need to start from architecture design, technology selection and performance tuning, and the core lies in "balancing" response speed, stability and scalability. 1. Use lightweight frameworks such as Spring WebFlux or Micronaut to improve processing efficiency, especially suitable for I/O-intensive tasks; 2. Optimize database access, and use asynchronous drivers, caches, library and tables and batch query strategies to reduce bottlenecks; 3. Properly configure thread pools and concurrent controls, distinguish CPU and I/O-intensive tasks, and choose high-performance IO frameworks to improve underlying efficiency; 4. Use tools such as Prometheus, Micrometer, and ELK Stack to continuously monitor and tune, pay attention to GC logs to optimize memory management, and ensure the long-term stable and efficient operation of the system.

Building High-Throughput Java Microservices

High-throughput Java microservices are not something that can be achieved overnight. They need to be considered from multiple levels including architecture design, technology selection, and performance tuning. The core lies in "balance" - it is necessary to ensure that service response is fast, and it also takes into account stability and scalability.

Building High-Throughput Java Microservices

1. Use lightweight frameworks to improve processing efficiency

Java itself is not slow, but traditional Spring MVC or heavyweight EJB architectures may bring some performance overhead. For high-throughput scenarios, it is recommended to use a lighter framework, such as Spring WebFlux or Micronaut .

  • WebFlux is a non-blocking framework based on Reactor, which can better utilize thread resources.
  • Micronaut completes dependency injection at compile time, which is less runtime burden and is more suitable for cloud-native and container deployment.

If your application requires frequent I/O operations (such as accessing databases or remote services), the non-blocking model can significantly reduce thread waiting time, thereby improving overall throughput.

Building High-Throughput Java Microservices

Microservices usually connect to databases, and databases are often the bottleneck. In order to improve throughput, you can start from the following aspects:

  • Use asynchronous database drivers, such as R2DBC (for WebFlux).
  • Use cache reasonably, such as Redis or Caffeine, to avoid duplicate queries.
  • Separate the database and tables or read and write separation to reduce single point pressure.
  • Avoid N 1 query problems and use JOIN or batch query to optimize data acquisition.

For example, if you need to query user information when processing an order, do not initiate a user query request for each order, but obtain it in batches at one time, which can save a lot of network round trip time.

Building High-Throughput Java Microservices

3. Utilize thread pools and concurrency control policies

Java's thread management has a great impact on throughput. The default thread pool configuration may not be suitable for your business scenario. You need to adjust the thread pool size according to the actual load and set the queue capacity reasonably.

  • Don’t blindly increase the number of threads. Too many threads will lead to an increase in context switching overhead.
  • For CPU-intensive tasks and I/O-intensive tasks, thread pooling should be processed separately.
  • You can use high-performance IO frameworks such as Netty or Undertow to improve the underlying processing efficiency.

For example, if your service mainly does computationally intensive work, such as image processing or data analysis, the thread pool size should be close to the number of CPU cores; and if there are many network requests, the thread pool can be appropriately increased.

4. Monitoring and tuning cannot be ignored

High throughput cannot be done in one tuning, it is a process of continuous observation and adjustment. You can use the following tools:

  • Prometheus Grafana to monitor indicators
  • Use Micrometer or Dropwizard Metrics to expose JVM and application metrics
  • Log analysis tools such as ELK Stack help locate slow requests or abnormal behaviors

It is also important to view GC logs regularly. If Full GC is frequent, it means that there is a problem with memory allocation, which may be because the object life cycle is too short, or the heap memory is insufficient.

Basically that's it. Although high throughput implementation is not complicated, details are easily overlooked, especially concurrent control and database access. Doing well can improve performance by orders of magnitude.

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