In computer science, ACID (Atomicity, Consistency, Isolation, Durability) is a set of properties of database transactions intended to guarantee validity even in the event of errors, power failures, etc. In the context of databases, a sequence of database operations that satisfies the ACID properties, and thus can be perceived as a single logical operation on the data, is called a transaction. For example, a transfer of funds from one bank account to another, even 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 as shorthand for Atomicity, Consistency, Isolation, and Durability, building on earlier work by Jim Gray who enumerated Atomicity, Consistency, and Durability but left out Isolation when characterizing the transaction concept. These four properties describe the major guarantees of the transaction paradigm, which has influenced many aspects of development in database systems.
According to Gray and Reuter, IMS supported ACID transactions as early as 1973 (although the term ACID came later).
Video ACID
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.
Consistency
Consistency ensures that a transaction can only bring the database from one valid state to another, maintaining 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 transactions, but does not guarantee that a transaction is correct.
Isolation
Transactions are often executed concurrently (eg. reading and writing to multiple tables at the same time). Isolation ensures that concurrent execution of transactions leaves the database in the same state that would've been obtained if the transactions were executed sequentially. Isolation is the main goal of concurrency control; depending on the method used, the effects of an incomplete transaction might not even 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 (eg. power outage or crash). This usually means that completed transactions (or their effects) are recorded in non-volatile memory.
Maps ACID
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 failure
In database systems, atomicity (or atomicness; from Greek a-tomos, undividable) is one of the ACID transaction properties. A 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 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 made of, or composed of, one or more (several), Physical transactions. Unless and until all component Physical transactions are executed, the Logical transaction will not have occurred - to the effects of the database. Say our Logical transaction consists of transferring funds from account A to account B. This Logical transaction may be composed of several Physical transactions consisting of first removing the amount from account A as a first Physical transaction and then, as a second transaction, depositing said amount in account B. We would not want to see the amount removed from account A before we are sure it has been transferred into account B. Then, unless and until both transactions have happened and the amount has been transferred to account B, the transfer has not, to the effects of the database, occurred.
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 A + B = 100. All validation rules must be checked to ensure consistency. Assume that a transaction attempts to subtract 10 from A without altering B. Because consistency is checked after each transaction, it is known that A + B = 100 before the transaction begins. If the transaction removes 10 from A successfully, atomicity will be achieved. However, a validation check will show that A + B = 90, which is inconsistent with the rules of the database. The entire transaction must be cancelled 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 A,B to be integers. If we were then to enter, say, the value 13.5 for A, the transaction will be cancelled, or the system may give rise to an alert in the form of a trigger ( if/when the trigger has been written to this effect ). Another example would be with 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 10 from B to A. Combined, there are four actions:
- T1 subtracts 10 from A.
- T1 adds 10 to B.
- T2 subtracts 10 from B.
- T2 adds 10 to A.
If these operations are performed in order, isolation is maintained, although T2 must wait. Consider what happens if T1 fails halfway through. The database eliminates T1's effects, and T2 sees only valid data.
By interleaving the transactions, the actual order of actions might be:
- T1 subtracts 10 from A.
- T2 subtracts 10 from B.
- T2 adds 10 to A.
- T1 adds 10 to B.
Again, consider what happens if T1 fails while modifying B (step 4). By the time T1 fails, T2 has already modified A; it cannot be restored to the value it had before T1 without leaving an invalid database. This is known as a write-write failure, because two transactions attempted to write to the same data field. In a typical system, the problem would be resolved by reverting to the last known good state, canceling the failed transaction T1, and restarting the interrupted transaction T2 from the good state.
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. The user assumes (understandably) 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, atomicity is guaranteed by copying the original (unchanged) data to a 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.
Distributed transactions
Guaranteeing ACID properties in a distributed transaction across a distributed database, where no single node is responsible for all data affecting a transaction, presents additional complications. Network connections might fail, or one node might successfully complete its part of the transaction and then be required to roll back its changes because of a failure on another node. The two-phase commit protocol (not to be confused with two-phase locking) provides atomicity for distributed transactions to ensure that each participant in the transaction agrees on whether the transaction should be committed or not. Briefly, in the first phase, one node (the coordinator) interrogates the other nodes (the participants) and only when all reply that they are prepared does the coordinator, in the second phase, formalize the transaction.
See also
- Eventual consistency
- CAP theorem
- Concurrency control
- Java Transaction API
- Open Systems Interconnection
- Transactional NTFS
- Two-phase commit protocol
- CRUD
References
Source of article : Wikipedia
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