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What the library problem is asking you to protect
Imagine a catalog whose shared state is a map keyed by book ID. Threads may search for a book or list entries, while other threads add, remove, or update inventory. The concurrency problem is to let independent inspections proceed together without letting a reader observe a partially applied change or allowing overlapping writes to corrupt the state.
State the safety contract explicitly: more than one thread may hold the read lock at the same time only while the write lock is not held; a thread holding the write lock excludes both other writers and readers. A successful read-lock acquisition also observes updates made before a previous write-lock release, as specified by Java’s ReadWriteLock API documentation.
Define the lock boundaries and invariants
Use one lock consistently for every access to the mutable shared inventory. A read operation holds the read lock for the entire time it inspects the state; an operation that changes the inventory holds the write lock for the entire mutation. Do not return a mutable internal collection and then let callers use it after the lock has been released. Return an immutable copy or provide a separately synchronized interface instead.
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- Read invariant: a lookup or enumeration does not mutate the protected state and holds the read lock throughout inspection.
- Write invariant: every add, remove, or update holds the exclusive write lock for the full change.
- Encapsulation invariant: callers cannot mutate protected internals outside these lock boundaries.
These boundaries protect the shared data, but they do not automatically make other application state, external side effects, or business rules thread-safe. Keep the critical section focused and include every relevant shared-state access in the same synchronization design.
Implement the catalog with ReentrantReadWriteLock
The following sketch protects a map and returns a snapshot rather than exposing its mutable contents. It omits catalog-specific validation and exception policy; those should be added without moving state access outside the lock.
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import java.util.HashMap;
import java.util.Map;
import java.util.concurrent.locks.Lock;
import java.util.concurrent.locks.ReentrantReadWriteLock;
final class LibraryCatalog {
private final Map<String, Integer> copiesByBookId = new HashMap<>();
private final ReentrantReadWriteLock rwLock = new ReentrantReadWriteLock();
private final Lock readLock = rwLock.readLock();
private final Lock writeLock = rwLock.writeLock();
Integer findCopies(String bookId) {
readLock.lock();
try {
return copiesByBookId.get(bookId);
} finally {
readLock.unlock();
}
}
Map<String, Integer> snapshot() {
readLock.lock();
try {
return Map.copyOf(copiesByBookId);
} finally {
readLock.unlock();
}
}
void setCopies(String bookId, int copies) {
writeLock.lock();
try {
copiesByBookId.put(bookId, copies);
} finally {
writeLock.unlock();
}
}
void removeBook(String bookId) {
writeLock.lock();
try {
copiesByBookId.remove(bookId);
} finally {
writeLock.unlock();
}
}
}Acquiring and releasing in try/finally ensures the lock is released if the operation exits exceptionally. The example’s use of Map.copyOf requires a Java version that provides that method; for an older target JDK, use an appropriate defensive-copy approach and make the returned copy unmodifiable if callers must not edit it.
Choose a fairness policy deliberately
ReentrantReadWriteLock is nonfair by default. The Java SE 18 class documentation says that under continuous contention a nonfair lock may indefinitely postpone a reader or writer, although it will normally have higher throughput than fair mode.
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Fair mode uses an approximately arrival-order policy, not a strict FIFO promise for every operation. A longest-waiting writer can be granted the write lock; a group of readers may be granted the read lock when they have waited longer than all waiting writers. The untimed tryLock methods do not honor the fairness setting. Choose fairness when limiting long delays matters more than maximizing potential throughput, and treat it as a policy tradeoff rather than a starvation-proof guarantee.
Avoid read-to-write upgrade deadlock
A thread holding the read lock cannot successfully acquire the write lock while retaining its read lock. If several readers all try to upgrade, each can wait for the others to release their reads, and none can proceed. The class documentation describes the supported direction as write-to-read downgrading, not read-to-write upgrading.
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If a check performed under a read lock finds that a cached or catalog value needs updating, release the read lock first, acquire the write lock, then check the condition again before changing state. The second check is essential: another thread could have fixed or changed the state in the gap.
- Acquire the read lock and inspect the condition.
- If a write is needed, release the read lock.
- Acquire the write lock and recheck the condition.
- Perform the update only if the rechecked condition still requires it, then release the write lock.
To downgrade safely, a writer may acquire the read lock while still holding the write lock, then release the write lock. This leaves the thread with read access without opening a gap in which another writer could intervene. Oracle’s Java SE 18 examples show both the recheck pattern for cache validity and the write-to-read downgrade approach.
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Decide whether a read-write lock fits the workload
A read-write lock is a workload-dependent optimization. Oracle’s Java SE 8 interface documentation says suitability depends on read frequency versus modification frequency, operation duration, contention, and multiprocessor access patterns. Very short reads can be outweighed by lock overhead; frequent writes reduce the opportunity for concurrent readers. A simple mutual-exclusion lock may be easier to reason about and perform as well or better in those cases.
Assess the choice against the actual application rather than assuming that a catalog is read-heavy:
- Read-to-write mix: Are reads meaningfully more frequent than modifications?
- Critical-section duration: Do read operations take long enough for concurrent readers to matter, or are they just brief map lookups?
- Contention and hardware: Are enough threads competing, and can the runtime execute useful read work in parallel?
- Delay requirements: Is potential reader or writer postponement acceptable, or is fair-mode behavior worth its tradeoff?
- Complexity: Can the team reliably maintain lock boundaries and avoid holding a lock during slow unrelated work?
Start with the simplest correct synchronization, then profile and measure the representative workload. Oracle’s API puts it plainly: “Ultimately, only profiling and measurement will establish whether the use of a read-write lock is suitable for your application.” Do not claim a speedup without measurements for the workload being discussed.
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