In computer science, a lock or mutex (from mutual exclusion) is a synchronization primitive that prevents state from being modified or accessed by multiple threads of execution at once. Locks enforce mutual exclusion concurrency control policies, and with a variety of possible methods there exist multiple unique implementations for different applications.
Types Generally, locks are advisory locks, where each thread cooperates by acquiring the lock before accessing the corresponding data. Some systems also implement mandatory locks, where attempting unauthorized access to a locked resource will force an exception in the entity attempting to make the access. The simplest type of lock is a binary semaphore. It provides exclusive access to the locked data. Other schemes also provide shared access for reading data. Other widely implemented access modes are exclusive, intend-to-exclude and intend-to-upgrade. Another way to classify locks is by what happens when the lock strategy prevents the progress of a thread. Most locking designs block the execution of the thread requesting the lock until it is allowed to access the locked resource. With a spinlock, the thread simply waits ("spins") until the lock becomes available. This is efficient if threads are blocked for a short time, because it avoids the overhead of operating system process rescheduling. It is inefficient if the lock is held for a long time, or if the progress of the thread that is holding the lock depends on preemption of the locked thread. Locks typically require hardware support for efficient implementation. This support usually takes the form of one or more atomic instructions such as "test-and-set", "fetch-and-add" or "compare-and-swap". These instructions allow a single process to test if the lock is free, and if free, acquire the lock in a single atomic operation. Uniprocessor architectures have the option of using uninterruptible sequences of instructions—using special instructions or instruction prefixes to disable interrupts temporarily—but this technique does not work for multiprocessor shared-memory machines. Proper support for locks in a multiprocessor environment can require quite complex hardware or software support, with substantial synchronization issues. The reason an atomic operation is required is because of concurrency, where more than one task executes the same logic. For example, consider the following C code:
The above example does not guarantee that the task has the lock, since more than one task can be testing the lock at the same time. Since both tasks will detect that the lock is free, both tasks will attempt to set the lock, not knowing that the other task is also setting the lock. Dekker's or Peterson's algorithm are possible substitutes if atomic locking operations are not available. Careless use of locks can result in deadlock or livelock. A number of strategies can be used to avoid or recover from deadlocks or livelocks, both at design-time and at run-time. (The most common strategy is to standardize the lock acquisition sequences so that combinations of inter-dependent locks are always acquired in a specifically defined "cascade" order.) Some languages do support locks syntactically. An example in C# follows:
C# introduced System.Threading.Lock in C# 13 on .NET 9. The code lock(this) can lead to problems if the instance can be accessed publicly. Similar to Java, C# can also synchronize entire methods, by using the MethodImplOptions.Synchronized attribute.
Granularity Before being introduced to lock granularity, one needs to understand three concepts about locks:
lock overhead: the extra resources for using locks, like the memory space allocated for locks, the CPU time to initialize and destroy locks, and the time for acquiring or releasing locks. The more locks a program uses, the more overhead associated with the usage; lock contention: this occurs whenever one process or thread attempts to acquire a lock held by another process or thread. The more fine-grained the available locks, the less likely one process/thread will request a lock held by the other. (For example, locking a row rather than the entire table, or locking a cell rather than the entire row); deadlock: the situation when each of at least two tasks is waiting for a lock that the other task holds. Unless something is done, the two tasks will wait forever. There is a tradeoff between decreasing lock overhead and decreasing lock contention when choosing the number of locks in synchronization. An important property of a lock is its granularity. The granularity is a measure of the amount of data the lock is protecting. In general, choosing a coarse granularity (a small number of locks, each protecting a large segment of data) results in less lock overhead when a single process is accessing the protected data, but worse performance when multiple processes are running concurrently. This is because of increased lock contention. The more coarse the lock, the higher the likelihood that the lock will stop an unrelated process from proceeding. Conversely, using a fine granularity (a larger number of locks, each protecting a fairly small amount of data) increases the overhead of the locks themselves but reduces lock contention. Granular locking where each process must hold multiple locks from a common set of locks can create subtle lock dependencies. This subtlety can increase the chance that a programmer will unknowingly introduce a deadlock. In a database management system, for example, a lock could protect, in order of decreasing granularity, part of a field, a field, a record, a data page, or an entire table. Coarse granularity, such as using table locks, tends to give the best performance for a single user, whereas fine granularity, such as record locks, tends to give the best performance for multiple users.
Database locks
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