Spring Boot is a popular framework for building web services in Java, but it also comes with some challenges when it comes to performance tuning. In this article, you will learn some of the best practices and techniques for optimizing your Spring Boot web services, such as:
Spring Boot supports different embedded servers, such as Tomcat, Jetty, and Undertow. Each one has its own advantages and disadvantages in terms of performance, scalability, and configuration. You should choose the server that best suits your needs and test it under realistic load conditions. For example, Tomcat is widely used and well-documented, but it may not be the fastest option for some scenarios. Jetty is lightweight and easy to embed, but it may have some compatibility issues with some libraries. Undertow is a newer and faster server, but it may require more tuning and customization.
The thread pool is a crucial component of your web service, as it determines how many concurrent requests your server can handle. You should configure the thread pool size according to your expected workload and the available resources of your machine. You can use the spring-boot-starter-actuator dependency to monitor the thread pool metrics and adjust the settings accordingly. For example, you can use the server.tomcat.max-threads property to set the maximum number of threads for Tomcat, or the server.undertow.worker-threads property for Undertow.
Compression and caching are two effective ways to reduce the network bandwidth and latency of your web service. Compression reduces the size of the response body by applying a compression algorithm, such as gzip or deflate. Caching stores the response data in a cache layer, such as the browser or a proxy server, so that subsequent requests can be served faster. You can enable compression and caching in Spring Boot by using the server.compression.enabled and spring.cache.type properties, respectively. You can also customize the compression and caching strategies by using other properties or annotations.
The database access is often the bottleneck of your web service, as it involves network communication, query execution, and data processing. You should optimize the database access by using the appropriate data source, connection pool, and query methods. You can use the spring-boot-starter-data-jpa dependency to simplify the database access with JPA and Hibernate. You can also use the spring-boot-starter-jdbc dependency to access the database with JDBC and a template class. You should configure the data source and connection pool properties to match your database settings and performance requirements. You should also use prepared statements, batch updates, and pagination to optimize the query methods.
-In my opinion for good performance our code does not depend on any framework, depending on how to tune our queries. -Use correctly the fetch size for retrieving large data from db. with Spring JDBC or JPA etc. -If we have many requests at the same time one method in Spring Data JPA we can use @Transaccional in this case one connection is open when all transactions finishes then connection is closed.
Testing and profiling are essential steps for identifying and resolving the performance issues of your web service. You should test your web service with different tools and methods, such as load testing, stress testing, and benchmarking. You should also profile your web service with different tools and techniques, such as logging, tracing, and profiling. You can use the spring-boot-starter-test dependency to write and run unit tests and integration tests for your web service. You can also use the spring-boot-devtools dependency to enable the live reload and remote debug features for your web service. You should also use other tools and frameworks, such as JMeter, Gatling, and Visual Studio Code, to test and profile your web service.
Here are a few more things to consider: - Code Profiling - Hibernate and JPA Optimization: - Profile and Optimize Dependencies - Connection Pooling - Asynchronous Processing - Optimize Logging - HTTP Compression - Monitoring and Metrics - Optimized DTOs