The CAP theorem, which stands for Consistency, Availability, and Partitions, outlines the trade-off between consistency and availability in distributed systems. It states that when a system is partitioned, it can only guarantee the concurrent delivery of two of the three attributes.
As it was first communicated by U.C. Berkley computer science professor Eric Brewer, this theorem is also known as Brewer’s theorem.
To clarify, what is the CAP Theorem?
Based on this theorem, it can be concluded that a distributed system composed of multiple computers working in tandem would benefit from maintaining a single shared state. Furthermore, this shared system must be capable of managing data across all nodes, whether they are virtual or physical.
We will investigate the CAP theorem and the differences between NoSQL and SQL databases, as well as examine when and how to construct each type of database in order to gain a deeper understanding of its application in system architecture.
Comprehending Consistency, Availability, and Partitioning
Take a look at this diagram of a simple distributed system with only two nodes to get started.
It can be assumed that the nodes are interconnected, meaning that the user’s system is likely storing data on one node and retrieving it from another. This raises the question: what is the next logical step?
The CAP system is defined.
Let’s look at a concrete case to learn about the CAP theorem’s application to system design.
As he works on the footage, a video editor reads it from one node or database and saves the edited version to another.
In this case, CAP provides you with three different attributes to play with:
- Continuous Availability and Reliability (CA database)
- Similarity and Separate Acceptance (CP Database)
- Readiness and the Capacity to Accept Partitions (AP Database)
A bit later on, we’ll take a closer look at each of them.
Before we get to those uses, however, let’s take a closer look at each of these features individually to see why the CAP theorem is useful.
A distributed system is said to be consistent if all users may read and write data reliably from any node in the system.
Any new information should be replicated across all nodes, and the read action should provide the exact same data for all users.
For the purpose of this discussion, let us assume that you have made changes to your online purchase after submitting it. Should you decide to cancel your purchase at a later date, the customer service agent you speak to will be aware of the alterations that have been made to your order.
The evidence indicates that the written data from the updates was uniform throughout all connected nodes. As a result, when the new representative attempted to access your data, it returned the most up-to-date written information.
Availability refers to the capability of a distributed network to continue to transfer data, even when some individual nodes experience failures. The functionality of the network as a whole must remain intact, even if particular components are not functioning properly.
In a dispersed network with availability, user systems shouldn’t be concerned with the specifics of the information they may get.
If you have placed an online order and subsequently made alterations to it, the customer service team will still be able to access the necessary data, even if multiple servers are down.
Despite the system’s focus on ensuring availability, there is no guarantee that the data received is up-to-date and reflects the most recent writing activity.
The CAP theorem states that a distributed system must possess partition tolerance in order to be able to handle temporary disconnections between its nodes. This means that the system must remain operational and allow read/write activities to take place, despite the fact that some nodes may be unavailable. This is necessary in order to ensure that the distributed system can still function in the event of such an interruption.
Given the desired level of database integrity, it is virtually essential to ensure partition tolerance. Consequently, this is the primary rationale for why linked systems can only ever utilise two of the three database attributes (Consistency, Availability, and Partition tolerance).
Depending on the type of node failure and the data requirements of the user’s systems, the network can be configured to provide either data integrity or accessibility to all users. This ensures that users have access to the data they need while also ensuring the integrity of the data.
Let’s have a look at how the user’s system makes use of these database attributes.
This graphic illustrates the fundamental steps in the process of accessing a database. The CAP model, which is commonly employed in many industries, follows a sequential approach that combines two of these components.
Regularity and accessibility (CA database)
It is essential that data be both consistent and accessible across all nodes in a networked database. However, when there is no partitional tolerance, this means that if one node fails, the data stored on it will not be accessible. This is a serious issue that can lead to data loss.
Since node failures are inevitable in any kind of networked system, CA databases become mostly unnecessary.
We will go through several SQL databases that support CA, including PostgreSQL, in the following sections.
Let’s take a look at CAP theorem’s characteristics and how they interact in different contexts.
Stability and tolerance of partitions (CP Database)
Databases that prioritise consistency and partition tolerance across all nodes in a network will shut off any nodes that are not consistent in the event of a partition or node failure. This ensures that the network remains stable and secure, even when individual nodes are not functioning properly.
In order to ensure the accuracy of data across all active nodes, it is essential that the data is replicated identically. As a general principle, the data on the main nodes is duplicated so that backup nodes can take over in the event of a failure. However, until the primary node is repaired, write operations are postponed as availability is not the main priority.
NoSQL databases, such as CP databases, have the capability to make updates independently of the principal node asynchronously. This makes them increasingly popular with organisations seeking an effective database management system (DBMS). MongoDB is among the most widely used NoSQL DBMSs.
The capacity to tolerate availability and partitioning (AP Database)
In the event of a partition or node failure in an Availability Partition (AP) database, the highest priority is to maintain the system’s availability. This is critical to ensure that, even if one of the nodes is taken offline, the network can still operate. It is important to note, however, that the data stored on these disconnected nodes may not reflect the most recent information available.
NoSQL databases like Apache Cassandra and MongoDB are both accessible on all nodes simultaneously because of the AP database model.
Once the split has been fixed, the user may synchronise their data to ensure continuity.
The Meaning of Databases and How to Create One
Before diving into the specifics of how each DBMS handles each sort of database, let’s get a handle on what they are and how they work in general.
The two main types of databases are called ACID and BASE. Understanding the nature of your data is crucial when attempting to define your database.
PH and ACID/BASe
ACID indicates that:
- Atomicity – This is a term for a database management system’s transaction criteria.
- Consistency – it means that all of the nodes in the network share the same accurate database.
- Isolation – indicates the separation of different user systems to avoid making duplicates of data.
- Durability – describes the DBMS’s built-in capacity to make copies of data in case of an error.
BASE Basic Availability, Soft State, and Eventually Consistent (BASE) is a feature that is exclusive to non-relational databases. As partitions can lead to inconsistencies between nodes, these databases cannot be viewed as being Atomicity, Consistency, Isolation, and Durability (ACID) compliant.
Ultimately, uniformity must be maintained throughout the system to ensure that data remains accessible to the end user, as previously discussed. This may come at a cost, but it is a necessary one in order to maintain the data’s accessibility.
Now we’ll compare SQL with NoSQL. Both relational and non-relational databases need special languages to interface with them.
SQL and Non-Relational Databases
SQL Structured Query Language (SQL) is a programming language specifically designed to work with relational databases. It is commonly used to perform CRUD (Create, Read, Update, and Delete) operations. These operations are the most basic and essential functions of database management. In summary, the acronym CRUD is a useful way to remember the functions of SQL.
SQL might seem limiting since it needs all data to adhere to a certain structure or parameters, although this has many practical applications.
However, this requires careful preparation before the database is released.
NoSQL is an acronym for “Not Only Structured Query Language” and is a query language used mostly with non-relational databases.
NoSQL databases, in contrast to SQL databases, do not need predefined schemas because of its architecture’s adaptability.
Let’s use this data to learn about the value of different kinds of databases.
Using PostgreSQL for CA’s Data Warehouse
PostgreSQL is a database management system (DBMS) that allows users to connect with databases via the use of structured query language.
It abides by the ACID standards, making it suitable for use in applications like banking where data consistency and availability are paramount.
In addition, PostgreSQL allows foreign keys, which facilitates communication across databases.
Nonetheless, this calls for careful preparation, since information is built according to specified tables.
Using MongoDB for CP Databases
If you are in search of a NoSQL database management system that focuses on documents, MongoDB is the perfect choice for you. Unlike a SQL database, MongoDB does not require extensive pre-planning as it is designed to be schema-less.
The CAP theorem is a highly valuable asset in the arsenal of big data analysis, due to its flexibility in accommodating modifications and updates. As such, it is a necessity for any individual or organisation that wishes to stay ahead of the curve in their respective fields.
MongoDB employs documents for storage and BSON files for query processing. Through conversion to JSON files, users are able to interact with the BSON files and make changes to the database beneath. This has been previously established.
Although MongoDB was originally designed with a BASE orientation, it has now incorporated parts of the ACID standards.
Despite the fact that there are still some unresolved issues and the database does not strictly comply with the ACID principles, this could potentially be advantageous in certain contexts.
Databases for Advanced Placement using Cassandra
Cassandra is often used to administer AP databases, which prioritise availability above consistency.
In contrast to databases which employ a primary and secondary node architecture to account for the potential of multiple node failures, this system is designed to be a peer-to-peer network; this means all participants are responsible for handling any potential node failures that may occur.
If a node in a Cassandra system experiences failure, the system will generate replicas of the data stored on the failed node automatically on other nodes within the system. This process is based on the replication factor that is configured by the user when setting up the system initially.
The nodes will replicate data clockwise to an additional n+1 nodes from the initial node if the replication factor is 2.
This feature ensures that data will remain consistent over time, but it will take some time until the most current information is accessible.
The CAP theorem is of paramount importance when it comes to the data management of corporations of all sizes. When implemented properly, these technologies can reduce the amount of data entry that is duplicated and help to reduce the potential for errors that are caused by human error.
It is important to note that there are a variety of different database structures and administration systems available for users to choose from in order to ensure that they can select the most suitable solution for their specific needs. Furthermore, it is important to consider the type of data that needs to be processed and the scale of operations when selecting an appropriate database structure and administration system.