![]() Lock (computer science) - Wikipedia. In computer science, a lock or mutex (from mutual exclusion) 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 exclusionconcurrency control policy. 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 ("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 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". In computer science, a lock or mutex (from mutual exclusion) is a synchronization mechanism for enforcing limits on access to a resource in an environment where there. Microsoft Access and Microsoft SQL Server are both database applications. The major difference between the two is in how the software is used. Microsoft Access is. When and How to Upsize Microsoft Access Databases to SQL Server Background. Microsoft Access databases often start as a small solution for one user. ![]() 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(lock==0){// lock free, set itlock=my. PID; }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: class. Account{// this is a monitor of an accountlongval=0; objectthis. Lock=newobject(); publicvoiddeposit(constlongx){lock(this. Lock){// only one thread at a time may execute this statementval+=x; }}publicvoidwithdraw(constlongx){lock(this. Lock){// only one thread at a time may execute this statementval- =x; }}}The code lock(this) can lead to problems if the instance can be accessed publicly.[1]Similar to Java, C# can also synchronize entire methods, by using the Method. Impl. Options. Synchronized attribute.[2][3][Method. Impl(Method. Impl. Options. Synchronized)]publicvoidsome. Method(){// do stuff}Granularity[edit]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.[citation needed]In a database management system, for example, a lock could protect, in order of increasing 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[edit]Database locks can be used as a means of ensuring transaction synchronicity. 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: Pessimistic locking: a user who reads a record with the intention of updating it places an exclusive lock on the record to prevent other users from manipulating it. This means no one else can manipulate that record until the user releases the lock. The downside is that users can be locked out for a very long time, thereby slowing the overall system response and causing frustration. Where to use pessimistic locking: this is mainly used in environments where data- contention (the degree of users request to the database system at any one time) is heavy; where the cost of protecting data through locks is less than the cost of rolling back transactions, if concurrency conflicts occur. Pessimistic concurrency is best implemented when lock times will be short, as in programmatic processing of records. Pessimistic concurrency requires a persistent connection to the database and is not a scalable option when users are interacting with data, because records might be locked for relatively large periods of time. It is not appropriate for use in Web application development. Optimistic locking: this allows multiple concurrent users access to the database whilst the system keeps a copy of the initial- read made by each user. When a user wants to update a record, the application determines whether another user has changed the record since it was last read. The application does this by comparing the initial- read held in memory to the database record to verify any changes made to the record.
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