Java -class library performance optimization based on the Mill Scalalib framework

Java -class library performance optimization based on the Mill Scalalib framework Overview: Mill SCALALIB is a powerful construction tool to improve the construction experience of the SCALA project.However, although Mill SCALALIB provides strong features, we may face performance challenges when dealing with large projects.This article will introduce some optimization skills to help you improve the performance of the Java library when using the Mill Scalalib framework. 1. Reduce dependencies: When using Mill Scalalib, we may introduce a large number of dependencies.However, excessive dependencies will lead to extended compilation time and decline in performance.Therefore, it is recommended to introduce only the real dependencies and delete the useless dependencies under possible situations. 2. Construction of parallelization: Mill Scalalib supports parallelization, which can significantly improve the performance of the construction process.It can be built in parallelization by setting the `ParallelexEcution` option in the` build.sc` file.For example: scala // Enable parallelization construction def parallelBuild = T { // Set to True to enable parallelization construction Result.True } // Apply in the ROOT module to build parallelization object root extends ScalaModule { override def millSourcePath: Path = T.sources("src") ... def ivyDeps = T{ ... } ... // Set as Parallelbuild, enable parallelization to build override def parallelExecution = T { parallelBuild() } } 3. Cache construction results: Mill SCALALIB has a built -in cache function and can cache the result of the built.This is very useful in incremental construction, because only the changeable files will be re -compiled.You can enable the cache by setting the `Persist` option in the` build.sc` file.For example: scala // Enable the persistence cache of the construction result def persistBuild = T { // Set to True Enable Cache Result.True } // Apply a cache in the root module object root extends ScalaModule { override def millSourcePath: Path = T.sources("src") ... def ivyDeps = T{ ... } ... // Set as PersistBuild, enable the cache override def persist = T { persistBuild() } } 4. Parallelization test: Mill SCALALIB supports parallel testing, which can accelerate the test operation time.It can be performed in parallel testing by setting the `test.parallel -options in the` build.sc` file.For example: scala // Enable parallelization test def parallelTest = T { // Set to TRUE enable parallelization test Result.True } // Apply a parallelization test in the root module object root extends ScalaModule { override def millSourcePath: Path = T.sources("src") ... def ivyDeps = T{ ... } ... // Set as ParallelTest, enable parallelization test override def testParallel = T { parallelTest() } } 5. Avoid unnecessary repetitive calculations: When we use Mill Scalalib to write complex tasks, we may involve some large calculation operations.To improve performance, we can avoid unnecessary repeated calculations.You can use the variable to slowly use the calculated result in the variable, and then use the variable directly to optimize the performance. The above are some Java -class library performance optimization techniques based on the Mill Scalalib framework.By reducing dependencies, parallelization construction, cache construction results, parallelization testing, and avoiding unnecessary repeated calculations, we can significantly improve the performance of the Java library when using the Mill Scalalib framework.I hope these optimization techniques can help you use Mill Scalalib more efficiently and get a better performance experience. Please note that the above example code is based on the SCALA language, but you can make corresponding conversion and modification according to your needs to adapt to the Java code.