ACID
In computer science, ACID is a set of properties of database transactions intended to guarantee data validity despite errors, power failures, and other mishaps. For example, a transfer of funds from one bank account to another, involving multiple changes such as debiting one account and crediting another, is a single transaction.
In 1983, Andreas Reuter and Theo Härder coined the acronym ACID, building on earlier work by Jim Gray who named atomicity, consistency, and durability, but not isolation, when characterizing the transaction concept. These four properties are the major guarantees of the transaction paradigm, which has influenced many aspects of development in database systems.
According to Gray and Reuter, the IBM Information Management System supported ACID transactions as early as 1973.
BASE stands for basically available, soft state, and eventually consistent: the acronym highlights that BASE is opposite of ACID, like their chemical equivalents. ACID databases prioritize consistency over availability — the whole transaction fails if an error occurs in any step within the transaction; in contrast, BASE databases prioritize availability over consistency: instead of failing the transaction, users can access inconsistent data temporarily: data consistency is achieved, but not immediately. A database either leans towards ACID or BASE, but it cannot be both. For example, SQL databases are structured over the ACID model, while NoSQL databases use the BASE architecture. However, some NoSQL databases might exhibit certain ACID traits.
Characteristics
The characteristics of these four properties as defined by Reuter and Härder are as follows:Atomicity
Transactions are often composed of multiple statements. Atomicity guarantees that each transaction is treated as a single "unit", which either succeeds completely or fails completely: if any of the statements constituting a transaction fails to complete, the entire transaction fails and the database is left unchanged. An atomic system must guarantee atomicity in each and every situation, including power failures, errors, and crashes. A guarantee of atomicity prevents updates to the database from occurring only partially, which can cause greater problems than rejecting the whole series outright. As a consequence, the transaction cannot be observed to be in progress by another database client. At one moment in time, it has not yet happened, and at the next, it has already occurred in whole.Consistency
Consistency ensures that a transaction can only bring the database from one consistent state to another, preserving database invariants: any data written to the database must be valid according to all defined rules, including constraints, cascades, triggers, and any combination thereof. This prevents database corruption by an illegal transaction. An example of a database invariant is referential integrity, which guarantees the primary key–foreign key relationship.Isolation
Transactions are often executed concurrently. Isolation ensures that concurrent execution of transactions leaves the database in the same state that would have been obtained if the transactions were executed sequentially. Isolation is the main goal of concurrency control; depending on the isolation level used, the effects of an incomplete transaction might not be visible to other transactions.Durability
Durability guarantees that once a transaction has been committed, it will remain committed even in the case of a system failure. This usually means that completed transactions are recorded in non-volatile memory.Examples
The following examples further illustrate the ACID properties. In these examples, the database table has two columns, A and B. An integrity constraint requires that the value in A and the value in B must sum to 100. The following SQL code creates a table as described above:Atomicity
Atomicity is the guarantee that series of database operations in an atomic transaction will either all occur, or none will occur. The series of operations cannot be separated with only some of them being executed, which makes the series of operations "indivisible". A guarantee of atomicity prevents updates to the database from occurring only partially, which can cause greater problems than rejecting the whole series outright. In other words, atomicity means indivisibility and irreducibility. Alternatively, we may say that a logical transaction may be composed of several physical transactions. Unless and until all component physical transactions are executed, the logical transaction will not have occurred.An example of an atomic transaction is a monetary transfer from bank account A to account B. It consists of two operations, withdrawing the money from account A and depositing it to account B. We would not want to see the amount removed from account A before we are sure it has also been transferred into account B. Performing these operations in an atomic transaction ensures that the database remains in a consistent state, that is, money is neither debited nor credited if either of those two operations fails.
Consistency failure
Consistency is a very general term, which demands that the data must meet all validation rules. In the previous example, the validation is a requirement that. All validation rules must be checked to ensure consistency. Assume that a transaction attempts to subtract 10 from without altering. Because consistency is checked after each transaction, it is known that before the transaction begins. If the transaction removes 10 from successfully, atomicity will be achieved. However, a validation check will show that, which is inconsistent with the rules of the database. The entire transaction must be canceled and the affected rows rolled back to their pre-transaction state. If there had been other constraints, triggers, or cascades, every single change operation would have been checked in the same way as above before the transaction was committed. Similar issues may arise with other constraints. We may have required the data types of both and to be integers. If we were then to enter, say, the value 13.5 for, the transaction will be canceled, or the system may give rise to an alert in the form of a trigger. Another example would be integrity constraints, which would not allow us to delete a row in one table whose primary key is referred to by at least one foreign key in other tables.Isolation failure
To demonstrate isolation, we assume two transactions execute at the same time, each attempting to modify the same data. One of the two must wait until the other completes in order to maintain isolation.Consider two transactions:
- T1 transfers 10 from A to B.
- T2 transfers 20 from B to A.
- T1 subtracts 10 from A.
- T1 adds 10 to B.
- T2 subtracts 20 from B.
- T2 adds 20 to A.
By interleaving the transactions, the actual order of actions might be:
- T1 subtracts 10 from A.
- T2 subtracts 20 from B.
- T2 adds 20 to A.
- T1 adds 10 to B.
Durability failure
Consider a transaction that transfers 10 from A to B. First, it removes 10 from A, then it adds 10 to B. At this point, the user is told the transaction was a success. However, the changes are still queued in the disk buffer waiting to be committed to disk. Power fails and the changes are lost, but the user assumes that the changes persist.Implementation
Processing a transaction often requires a sequence of operations that is subject to failure for a number of reasons. For instance, the system may have no room left on its disk drives, or it may have used up its allocated CPU time. There are two popular families of techniques: write-ahead logging and shadow paging. In both cases, locks must be acquired on all information to be updated, and depending on the level of isolation, possibly on all data that may be read as well. In write ahead logging, durability is guaranteed by writing the prospective change to a persistent log before changing the database. That allows the database to return to a consistent state in the event of a crash. In shadowing, updates are applied to a partial copy of the database, and the new copy is activated when the transaction commits.Locking vs. multiversioning
Many databases rely upon locking to provide ACID capabilities. Locking means that the transaction marks the data that it accesses so that the DBMS knows not to allow other transactions to modify it until the first transaction succeeds or fails. The lock must always be acquired before processing data, including data that is read but not modified. Non-trivial transactions typically require a large number of locks, resulting in substantial overhead as well as blocking other transactions. For example, if user A is running a transaction that has to read a row of data that user B wants to modify, user B must wait until user A's transaction completes. Two-phase locking is often applied to guarantee full isolation.An alternative to locking is multiversion concurrency control, in which the database provides each reading transaction the prior, unmodified version of data that is being modified by another active transaction. This allows readers to operate without acquiring locks, i.e., writing transactions do not block reading transactions, and readers do not block writers. Going back to the example, when user A's transaction requests data that user B is modifying, the database provides A with the version of that data that existed when user B started his transaction. User A gets a consistent view of the database even if other users are changing data. One implementation, namely snapshot isolation, relaxes the isolation property.