What is the CAP theorem, and how does it affect databases?
Big data professionals who work processing large volumes of information must know the CAP theorem because it will allow them to choose the most appropriate database for each project.
The fact that the users of a website have fast, correct, and error-free access to the results of the searches they carry out will depend to a large extent on the choice of databases that have been made.
It is vital to know the CAP theorem, also known as Brewer’s theorem since the concepts of this postulate make up the so-called quality requirements that are essential to define at the beginning of a project.
Computer scientist Eric Brewer in what became known as the Brewer conjecture, stated that “as applications become more web-based.
You have to stop worrying about data consistency because if you want high availability in these new applications, it is not possible to guarantee the consistency of the information”.
Two years after uttering these words, in 2002, Seth Gilbert and Nancy Lynch of MIT formally proved him correct, and Brewer’s theorem or CAP theorem was born.
The 3 factors that makeup the CAP theorem
CAP is an acronym for Consistency, Availability, and Partition tolerance, and what it stands for is that it is impossible to simultaneously provide more than two of those three factors.
Therefore, the CAP theorem holds in distributed systems it is not possible to simultaneously guarantee these three characteristics.
Quality that makes it possible for any reader to receive the most recent write as a response and never provide obsolete data. That is, all nodes must provide the same information at the same time.
Property for which any type of request receives a non-erroneous response in a reasonable time, even if it is not the most recent deed.
Attribute that allows the system to continue working even when there are communication failures or partial crashes.
Main attribute combinations
As is evident, no distributed system is safe from a network failure, and taking into account the CAP theorem, there are three possible options for attribute pair combinations that can be guaranteed at the same time.
CA: consistency and availability
In this case, access to the information is assured, the value of the data is the same (consistent) for all requests attended, and if there were changes, it would be displayed immediately.
AP: partition availability and tolerance
The availability of the information is ensured, and the system is capable of managing the partitioning of the nodes, but the consistency of the data is neglected.
CP: consistency and partition tolerance
The consistency of the data is safeguarded, and the partitioning of the nodes is tolerated availability is sacrificed with which the system may fail or take time to offer a response at the user’s request.
How does the CAP theorem affect databases?
It is essential to take into account the CAP theorem when choosing the database that best suits the project to be developed.
Thus, professionals will be able to select databases that have the important attributes that the system must guarantee.
Database management systems can be classified into:
They are traditional relational database models that, of the three attributes of the CAP theorem, prioritize consistency and availability, which is why they are called CA type.
The main databases with these characteristics include SQL, MariaDB, MySql, and Oracle.
In this case, these are non-relational database models that have better performance when it comes to processing large volumes of data that come from different sources, as is often the case with databases used in Big Data.
Here, tolerance to network partitions is supported, and depending on the chosen system or manager, one of the other two attributes available according to the CAP theorem will be prioritized: consistency or availability. They will therefore be CP or AP models.
Among the CP databases would be BigTable, HBase, Redis, or MongoDB, and focused on the AP model would be Cassandra, Dynamo, KAI, CouchDB, or Riak.
They are the most suitable systems for applications and unstructured data that involve huge volumes of information, for example, everything related to big data.
These types of databases are a renewed version of relational data models and could be said to combine features of traditional managers and NoSQL.
Examples of this type of database would be Apache Trafodion, TokuDB, NuoDB, CockroachDB, or ClustixDB.
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