Lock (computer science)


In computer science, a lock or mutex is a synchronization mechanism for enforcing limits on access to a resource in an environment where there are many threads of execution. A lock is designed to enforce a mutual exclusion concurrency control policy.

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 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 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 re-scheduling. 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 uninterruptable 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:

if

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.
Some languages do support locks syntactically. An example in C# follows:

public class Account // This is a monitor of an account

The code lock can lead to problems if the instance can be accessed publicly.
Similar to Java, C# can also synchronize entire methods, by using the MethodImplOptionsSynchronized attribute.

public void SomeMethod

Granularity

Before being introduced to lock granularity, one needs to understand three concepts about locks:
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 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 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

can be used as a means of ensuring transaction synchronicity. i.e. when making transaction processing concurrent, using 2-phased locks ensures that the concurrent execution of the transaction turns out equivalent to some serial ordering of the transaction. However, deadlocks become an unfortunate side-effect of locking in databases. Deadlocks are either prevented by pre-determining the locking order between transactions or are detected using waits-for graphs. An alternate to locking for database synchronicity while avoiding deadlocks involves the use of totally ordered global timestamps.
There are mechanisms employed to manage the actions of multiple concurrent users on a database—the purpose is to prevent lost updates and dirty reads. The two types of locking are pessimistic locking and optimistic locking:
Lock-based resource protection and thread/process synchronization have many disadvantages:
Some concurrency control strategies avoid some or all of these problems. For example, a funnel or serializing tokens can avoid the biggest problem: deadlocks. Alternatives to locking include non-blocking synchronization methods, like lock-free programming techniques and transactional memory. However, such alternative methods often require that the actual lock mechanisms be implemented at a more fundamental level of the operating software. Therefore, they may only relieve the application level from the details of implementing locks, with the problems listed above still needing to be dealt with beneath the application.
In most cases, proper locking depends on the CPU providing a method of atomic instruction stream synchronization. Therefore, an application can often be more robust when it recognizes the burdens it places upon an operating system and is capable of graciously recognizing the reporting of impossible demands.

Lack of composability

One of lock-based programming's biggest problems is that "locks don't compose": it is hard to combine small, correct lock-based modules into equally correct larger programs without modifying the modules or at least knowing about their internals. Simon Peyton Jones gives the following example of a banking application:
design a class that allows multiple concurrent clients to deposit or withdraw money to an account; and give an algorithm to transfer money from one account to another. The lock-based solution to the first part of the problem is:
class Account:
member balance : Integer
member mutex : Lock
method deposit
mutex.lock
balance ← balance + n
mutex.unlock
method withdraw
deposit
The second part of the problem is much more complicated. A routine that is correct for sequential programs would be
function transfer
from.withdraw
to.deposit
In a concurrent program, this algorithm is incorrect because when one thread is halfway through, another might observe a state where has been withdrawn from the first account, but not yet deposited into the other account: money has gone missing from the system. This problem can only be fixed completely by taking locks on both account prior to changing any of the two accounts, but then the locks have to be taken according to some arbitrary, global ordering to prevent deadlock:
function transfer
if from < to // arbitrary ordering on the locks
from.lock
to.lock
else
to.lock
from.lock
from.withdraw
to.deposit
from.unlock
to.unlock
This solution gets more complicated when more locks are involved, and the function needs to know about all of the locks, so they cannot be hidden.

Language support

Programming languages vary in their support for synchronization: