Database Concept

ACID Properties  – 1, 2, 3, 4

In order to perform a Transaction in a database system and to make sure it works without any issues, there are few rules a Database Transaction should follow. These rules are the standards across all Relational Database systems (RDBMS) and are called ACID rules.

ACID stands for Atomicity, Consistency, Isolation and Durability. So let’s check what all these Rules states.

Atomicity − This property states that a transaction must be treated as an atomic unit, that is, either all of its operations are executed or none. There must be no state in a database where a transaction is left partially completed. States should be defined either before the execution of the transaction or after the execution/abortion/failure of the transaction.
Isolation – 

Of the four ACID properties in a DBMS (Database Management System), the isolation property is the one most often relaxed. When attempting to maintain the highest level of isolation, a DBMS usually acquires locks on data or implements multiversion concurrency control, which may result in a loss of concurrency. This requires adding logic for the application to function correctly.

Most DBMSs offer a number of transaction isolation levels, which control the degree of locking that occurs when selecting data. For many database applications, the majority of database transactions can be constructed to avoid requiring high isolation levels (e.g. SERIALIZABLE level), thus reducing the locking overhead for the system. The programmer must carefully analyze database access code to ensure that any relaxation of isolation does not cause software bugs that are difficult to find. Conversely, if higher isolation levels are used, the possibility of deadlock is increased, which also requires careful analysis and programming techniques to avoid.

The isolation levels defined by the ANSI/ISO SQL standard are listed as follows.

Durability –

In database systems, durability is the ACID property which guarantees that transactions that have committed will survive permanently. For example, if a flight booking reports that a seat has successfully been booked, then the seat will remain booked even if the system crashes.

Consistency – 

Consistency in database systems refers to the requirement that any given database transaction must change affected data only in allowed ways. Any data written to the database must be valid according to all defined rules, including constraints, cascades, triggers, and any combination thereof. This does not guarantee correctness of the transaction in all ways the application programmer might have wanted (that is the responsibility of application-level code) but merely that any programming errors cannot result in the violation of any defined rules.

Consistency is one of the four guarantees that define ACID transactions; however, significant ambiguity exists about the nature of this guarantee. It is defined variously as:

  • The guarantee that any transactions started in the future necessarily see the effects of other transactions committed in the past[1][2]
  • The guarantee that database constraints are not violated, particularly once a transaction commits[3][4][5][6]
  • The guarantee that operations in transactions are performed accurately, correctly, and with validity, with respect to application semantics[7]

As these various definitions are not mutually exclusive, it is possible to design a system that guarantees “consistency” in every sense of the word, as most relational database management systems in common use today arguably do.

CAP Theoremwiki

Youtube 1 , ** Youtube 2

In theoretical computer science, the CAP theorem, also named Brewer’s theorem after computer scientist Eric Brewer, states that it is impossible for a distributed computer system to simultaneously provide more than two out of three of the following guarantees:[1][2][3]

Consistency Availability Partition tolerance
Every read receives the most recent write or an error Every request receives a (non-error) response – without guarantee that it contains the most recent write The system continues to operate despite an arbitrary number of messages being dropped (or delayed) by the network between nodes

Note that consistency as defined in the CAP Theorem is quite different from the consistency guaranteed in ACID database transactions.

No distributed system is safe from network failures, thus network partitioning generally has to be tolerated. In the presence of a partition, one is then left with two options: consistency or availability. When choosing consistency over availability, the system will return an error or a time out if particular information cannot be guaranteed to be up to date due to network partitioning. When choosing availability over consistency, the system will always process the query and try to return the most recent available version of the information, even if it cannot guarantee it is up to date due to network partitioning.[4]

In the absence of network failure – that is, when the distributed system is running normally – both availability and consistency can be satisfied.

CAP is frequently misunderstood as if one had to choose to abandon one of the three guarantees at all times. In fact, the choice is really between consistency and availability for when a partition happens only; at all other times, no trade-off has to be made.[5]

Database systems designed with traditional ACID guarantees in mind such as RDBMS choose consistency over availability, whereas systems designed around the BASE philosophy, common in the NoSQL movement for example, choose availability over consistency

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