Impossible to make a cached thread pool with a size limit?

java

thread-pool

concurrency

best-practices

byAnton Shumikhin·Aug 11, 2024

Let's create a ThreadPoolExecutor with a dynamic range, and hold it to a maximum pool size, like so:

ThreadPoolExecutor executor = new ThreadPoolExecutor(
    0,                         // Like Schrodinger's Threads -- they exist only when observed (needed).
    10,                        // The Spartan limit -- only the strongest 10 survive!
    60L,                       // Like a mayfly, the idle threads have a lifespan (in seconds).
    TimeUnit.SECONDS,          // Time units, because everything's relative (right, Einstein?)
    new LinkedBlockingQueue<>() // The backlog bureau -- where tasks queue up.
);

With this, your thread pool has a ceiling on its growth, making it a cached thread pool with a size limit.

The How-Tos of thread pools

Let's pull the strings of ThreadPoolExecutor by understanding its levers:

Core and maximum pool sizes: The lower and upper bounds of threads.
Keep-alive time: Determining the lifespan of idle threads above the core pool size.
Work queue types: Choose from SynchronousQueue for a game of hot-potato, LinkedBlockingQueue for a good-old queue line, or custom queues per your fancy.
Rejection policies: Sets the red tape (rules) for handling tasks in rush hour. CallerRunsPolicy lets the task cut the queue by being run by the calling thread.

The 'Wolf of Thread Street' guide to optimization

Consider these Wallstreet-style tactics for pool optimization:

Bounded bureaucracy: A finite queue size keeps things under control, avoiding memory overruns.
Idle time management: allowCoreThreadTimeOut(boolean) lets core threads time out, avoiding resource hogging.
Dynamic pool morphing: Using setCorePoolSize(int) and setMaximumPoolSize(int) allows your pool to adapt to changing workload.
Managerial-level rejection handling: RejectedExecutionHandler implementation lets you define what to do when the pool is overflowing.

Handling the `ThreadPoolExecutor` beast

Every choice has a price:

Too small or too big queue: Unbounded LinkedBlockingQueue might lead to memory issues, while a very small queue might lead to rejected tasks.
Resource balancing: An unlimited pool size might burn out your resources while an overly conservative pool might slow things down.
Rejection Policies: They are like emergency hatch handle pulls on high load, choose wisely.

Guru-level techniques and pitfalls to avoid

Adjusting queue capacity: Tuning capability gives SynchronousQueue and LinkedBlockingQueue a rush, remember to balance!
Thread cycle overhead: A pool with low core size could lead to thread cycles, affecting performance.
Future JDK considerations: Project Loom introduces lightweight threads (fibers), thereby changing concurrency best practices.
Libraries and Dependencies: Always check the concurrency requirements of libraries like Apache’s HTTP client to configure the correct pool settings.