Java concurrency is the discipline of running multiple tasks safely at the same time. The central challenge is not merely starting threads: shared data must remain correct, updates must become visible to other threads, and work must be coordinated and cancelled predictably. The DZone Refcard Core Java Concurrency provides a practical map of these ideas and the standard Java APIs used to implement them.
What is Java concurrency?
Concurrency lets multiple units of work make progress during overlapping periods. On a multicore machine they may execute simultaneously; on a single core the runtime can interleave them. Either way, the result depends on the ordering of reads and writes unless the program establishes the required guarantees.
Two properties must be separated:
- Atomicity: an operation, or protected group of operations, appears indivisible to other threads.
- Visibility: a thread is entitled to observe another thread’s completed writes.
A race condition occurs when the result changes with the timing or ordering of actions. A data race is the narrower case of conflicting accesses to shared, non-final state without suitable synchronization. A counter increment, for example, is a read followed by a write; two unsynchronized increments can both read the same old value and lose an update.
What does happens-before mean?
The Java Memory Model’s happens-before relation is a reasoning rule about ordering and visibility. If action A happens-before action B, B is entitled to observe the effects that the memory model makes visible from A. It is not a claim that every instruction executes in source order or that all operations are globally serialized.
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Important happens-before relationships
- Actions before starting a thread happen-before actions in the started thread.
- Unlocking (releasing) a monitor happens-before a later lock (acquisition) of that same monitor.
- A write to a volatile field happens-before a subsequent read of that field.
- Actions in a thread happen-before another thread successfully returns from
join()on it.
These relationships let you justify a visibility claim. Without one of them, a compiler, processor, or runtime may legally reorder or cache operations in ways that make an apparently obvious communication pattern unreliable.
How do I make shared state thread-safe?
First identify the invariant: which fields must change together, and which operations are compound? Then select the smallest abstraction that provides the needed guarantee.
| Tool | Primary guarantee | Best fit | Important limit |
|---|---|---|---|
synchronized |
Mutual exclusion plus monitor-based visibility | Protecting a critical section or multi-field invariant | Only code using the same monitor is excluded |
volatile |
Visibility and ordering for one field | Status flags, publication of a latest value | Does not make check-then-act or other multi-step logic atomic |
| Atomic classes | Atomic operations such as compare-and-set on an individual value | Counters, state transitions, and lock-free single-value updates | Do not automatically protect relationships among several variables |
Lock implementations |
Mutual exclusion with extra control | Timed, interruptible, or condition-based locking | Requires disciplined unlock handling |
| Concurrent collections | Thread-safe collection operations | Shared maps, queues, and sets with library-defined semantics | Compound workflows may still need external coordination |
Use synchronized for compound invariants
Use a monitor when several reads and writes must be treated as one transaction. Keep the critical section focused and ensure every access to the protected state follows the same locking policy. Monitor release and subsequent acquisition also establish the visibility needed by the Java Memory Model.
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Use volatile for communication, not transactions
A volatile stop flag is suitable when one thread publishes a simple state and another polls it. It is not sufficient for code such as “if balance is at least amount, then subtract amount,” because the check and update can interleave with another thread’s operation. Protect that invariant with a monitor, a suitable lock, or an atomic algorithm.
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Atomic variables provide operations such as increment and compare-and-set without a traditional monitor. They are useful when the state fits one value and the transition can be expressed through the class’s atomic operations. If correctness depends on multiple fields changing together, use a lock or another higher-level design.
Waiting, notification, and interruption
wait and notify
A thread may call wait() only while holding that object’s monitor, normally inside a synchronized method or block. Always wait in a loop that rechecks the condition:
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synchronized (queue) {
while (queue.isEmpty()) {
queue.wait();
}
Item item = queue.remove();
}The loop handles spurious wakeups and notifications that do not establish the condition your code needs. A producer changes the state while holding the same monitor and then calls notify() or notifyAll(). For new designs, higher-level blocking queues and coordination utilities usually express this protocol more safely.
Interruption
Interruption is a cooperative cancellation request, not a forced thread termination. If a method can pass the interruption to its caller, propagate InterruptedException. If you handle it locally or translate it into another result, restore the flag so higher-level code can still detect cancellation:
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try {
queue.take();
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
return;
}Task execution with java.util.concurrent
Prefer library abstractions over manually creating and coordinating threads when their behavior matches the problem.
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ExecutorService, Runnable, Callable, and Future
Runnablerepresents work with no result.Callable<T>represents work that returns a value and may throw an exception.ExecutorServicemanages task submission and worker execution.Future<T>represents a pending result and supports waiting, cancellation, and status inspection.
Choose an executor configuration according to workload and lifecycle. Always arrange shutdown, define how rejected work is handled, and avoid unbounded queues or pools that can overwhelm downstream services.
CompletableFuture
CompletableFuture models dependent and combined asynchronous stages. Continuations can transform results, recover from failures, and combine independent operations. The execution context matters: non-async stages may run in the thread that completes the prior stage, while async stages use an executor selected by the API unless you supply one explicitly. Pass an explicit executor when placement, capacity, or isolation is important.
Locks and read-write locks
Explicit Lock implementations add capabilities such as tryLock(), timed acquisition, and interruptible acquisition. Use try/finally so every successful lock acquisition is paired with an unlock. A read-write lock can allow concurrent readers while excluding writers, but its overhead and fairness behavior mean it is not automatically faster than a simple monitor.
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Coordination utilities and concurrent collections
The concurrency library includes queues, latches, barriers, semaphores, and other coordination mechanisms. These classes encode tested waiting and signaling rules. Select one based on the protocol: a blocking queue for producer-consumer flow, a latch for waiting until a fixed set of events completes, or a semaphore for limiting concurrent access.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Safe publication, immutability, and ThreadLocal
An object is safely published when other threads receive a reference through a happens-before mechanism, such as a lock, volatile field, thread start, or completion boundary. Immutable objects are easier to share because their state cannot change after construction; final fields receive special visibility guarantees when construction is not leaked prematurely.
ThreadLocal gives each thread its own value and can eliminate sharing for request-scoped or temporary state. In pooled threads, call remove() when the value should not survive into a later task, especially in server code.
Are virtual threads faster?
No. Oracle’s Java SE 21 documentation states: “Virtual threads are not faster threads; they do not run code any faster than platform threads.” Virtual threads target scalability and throughput for applications with very many concurrent tasks that spend substantial time waiting, such as server work performing blocking I/O.
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Quick Recap
When virtual threads are a good fit
- Large numbers of mostly waiting tasks.
- Blocking-style code where simpler thread-per-task structure is valuable.
- Services whose bottleneck is waiting on I/O rather than CPU execution.
When they are not a speed solution
- CPU-bound algorithms already limited by available processor capacity.
- Code that assumes unlimited downstream capacity.
- Work requiring a lower algorithmic latency rather than more concurrent waiting tasks.
A practical decision checklist
- List every shared mutable field and the invariant it participates in.
- Decide whether the requirement is visibility, mutual exclusion, an atomic single-value update, or task coordination.
- Identify compound operations such as check-then-act and protect the entire sequence.
- Choose a standard library abstraction before designing custom wait/notify protocols.
- Define interruption, cancellation, executor shutdown, queue bounds, and failure handling.
- Verify the API and behavior against the Java release your application actually runs.
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