Locks

Locks:

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

Types of locks:

In general, locks are advisory locks, where each of the thread cooperates by acquiring the lock before accessing the corresponding data. Some of the systems also implement compulsory locks, where attempting unauthorized access to a locked resource will force an exception in the entity trying to make the access.

Locks concepts:

Lock overhead:

The additional resources for using locks, such as the memory space allocated for locks, the CPU time to initialize and destroy locks, and the time for acquiring locks or releasing locks. The more locks a program utilizes, the more overhead connected with the usage.

Lock contention:

It occurs whenever one process or thread attempts to acquire a lock held by another process or thread. The more granular the obtainable locks, the less likely one process/thread will request a lock held by the other. (For instance, locking a row instead of the entire table, or locking a cell instead of the entire row.)

Deadlock:

The situation when each of two tasks is waiting for a lock that the other task holds. Unless something is finished, the two tasks will wait forever.

A vital property of a lock is its granularity. It is a measure of the amount of data the lock is protecting. Generally, selecting a coarse granularity (a small number of locks, each protecting a high segment of data) results in less lock overhead while a single process is accessing the protected data, but worse performance while multiple processes are running concurrently. It is due to increased lock contention. The more coarse the lock, the higher the probability that the lock will stop an unrelated process from proceeding. On the other hand, by using a fine granularity (a larger number of locks, each of the protecting a rather small amount of data) increases the overhead of the locks themselves but decrease lock contention. Granular locking where each of the process must hold multiple locks from a common set of locks can create subtle lock dependencies. This subtlety can improve the chance that a programmer will unknowingly introduce a deadlock.

Database locks:

It can be utilized as a means of ensuring transaction synchronicity. I.e. while making transaction processing concurrent (interleaving transactions), by using 2-phased locks makes sure that the concurrent execution of the transaction turns out equivalent to some serial ordering of the transaction. Though, deadlocks become an unfortunate side-effect of locking in databases. Deadlocks are either stop by pre-determining the locking order between transactions or are detected by using waits-for graphs. An alternating to locking for database synchronicity whereas avoiding deadlocks involves the use of totally ordered global timestamps.

Pessimistic locking:

A user who reads a record, with the purpose of updating it, put an exclusive lock on the record to avoid other users from manipulating it. It means no one else can manipulate that record till the user releases the lock. The downside is that users can be locked out for a very long period, therefore slowing the whole system response and causing frustration.

Optimistic locking:

It allows multiple concurrent users access to the database while the system keeps a copy of the initial-read made by each user. While a user desire to update a record, the application determines whether another user has modified 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 modification made to the record. Any discrepancy between the initial-read and the database record violate concurrency rules and therefore cause the system to disregard any update request. An error message is produced and the user is asked to begin the update process again. It enhanced database performance by decreasing the amount of locking required, thereby decreasing the load on the database server. It efficiently works with tables that require restricted updates since no users are locked out. Though, some updates may fail. The downside is constant update failures because of high volumes of update requests from multiple concurrent users - it can be frustrating for users.

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