SQL, NOSQL and CAP theorem

Relational database

A SQL (Structured Query Language) database, also known as a relational database, is a type of database management system (DBMS) that is designed to store and manage structured data using a relational model. In a relational database, data is organized into tables, with each table consisting of rows (also known as records or tuples) and columns (also known as attributes or fields). Tables can be related to each other using foreign keys, which allow data from multiple tables to be linked and queried together.

SQL databases are characterized by their use of a structured query language (SQL) for defining and manipulating the data in the database. SQL is a standardized language that is used to create, modify, and query the data in a SQL database.

SQL databases are widely used in a variety of applications, including business applications, financial systems, and web-based applications. They are known for their support for transactions, which allow multiple updates to be made to the database as a single unit of work, and their ability to enforce data integrity constraints.

Some popular SQL databases include MySQL, Oracle, Microsoft SQL Server, and PostgreSQL.

Popular distributed systems for relational database

There are several popular distributed systems for relational databases:

1. MySQL Cluster: MySQL Cluster is a distributed database management system designed to provide high availability and real-time performance for web, cloud, and telecommunications applications. It uses a shared-nothing architecture and utilizes synchronous replication to ensure data consistency and high availability.
   
2. Oracle RAC: Oracle Real Application Clusters (RAC) is a distributed database system that enables multiple instances of Oracle to run concurrently on different servers, sharing a single database. RAC uses a shared-disk architecture and supports both synchronous and asynchronous replication.
   
3. Microsoft SQL Server Always On: Microsoft SQL Server Always On is a high availability and disaster recovery solution that provides multiple copies of a database on different servers. It uses synchronous replication to ensure data consistency and supports both active-passive and active-active configurations.
   
4. PostgreSQL BDR: PostgreSQL BDR (Bi-Directional Replication) is a set of extensions to PostgreSQL that enables multi-master replication between PostgreSQL databases. BDR uses logical decoding and triggers to replicate data and supports both synchronous and asynchronous replication.
   

These are just a few examples of distributed systems for relational databases. There are many other options available, each with their own unique features and capabilities.

There are several popular cloud-based distributed systems for relational databases:

1. Amazon Aurora: Amazon Aurora is a fully managed, MySQL and PostgreSQL-compatible, relational database engine that is designed to be highly available, scalable, and fault-tolerant. It uses a distributed, shared-nothing architecture and automatically replicates data across multiple availability zones to ensure high availability.
   
2. Google Cloud Spanner: Google Cloud Spanner is a fully managed, horizontally scalable, distributed database that is designed to be highly available and consistent. It uses a shared-nothing architecture and uses a combination of Paxos-based replication and lock server architecture to ensure data consistency and high availability.
   
3. Azure SQL Database: Azure SQL Database is a fully managed, cloud-based, relational database service that is designed to be highly available and scalable. It supports both active-passive and active-active configurations and uses a combination of synchronous and asynchronous replication to ensure data consistency.
   
4. Oracle Cloud Infrastructure Database: Oracle Cloud Infrastructure Database is a fully managed, cloud-based, relational database service that is designed to be highly available, scalable, and secure. It supports both Oracle RAC and Oracle Database In-Memory and uses synchronous replication to ensure data consistency.
   

These are just a few examples of cloud-based distributed systems for relational databases. There are many other options available, each with their own unique features and capabilities.

Nosql database

NoSQL databases (short for "not only SQL") are a category of databases that are designed to handle large volumes of data that is distributed across a wide number of servers. They are typically used in situations where traditional relational databases may not be suitable, such as when dealing with very large datasets, high levels of data volatility, or complex relationships between data elements.

NoSQL databases are characterized by their ability to handle unstructured or semi-structured data, their horizontal scalability, and their use of distributed architectures. They offer a variety of data models, including key-value stores, document databases, column-family databases, and graph databases.

Some popular NoSQL databases include MongoDB, Cassandra, Redis, and Couchbase.

NoSQL databases are often used in big data and real-time web applications, as well as for storing large amounts of unstructured data, such as log files and social media data. They are also sometimes used as a data store for microservices architectures.

Some NoSQL databases do support the ability to perform joins, although the way in which joins are implemented can vary depending on the specific database and data model being used.

For example, some document databases, such as MongoDB, support the use of embedded documents and arrays to store related data in a single document, which can then be queried using the database's aggregation framework. This can provide a way to perform "joins" between documents in a way that is similar to how joins are performed in a relational database.

Other NoSQL databases, such as Cassandra, do not support traditional SQL-style joins, but do provide other ways to perform related data queries, such as using denormalized data models or materialized views.

It's important to note that the way in which NoSQL databases handle related data can be quite different from how relational databases do it, and as such, the concept of a "join" may not be directly applicable in all cases.

There are several popular NoSQL databases:

1. MongoDB: MongoDB is a document-oriented database that uses JSON-like documents to store data. It is designed to be scalable, flexible, and easy to use.
   
2. Cassandra: Cassandra is a distributed database that is designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure.
   
3. Redis: Redis is an in-memory data structure store that is often used as a database, cache, and message broker. It is known for its high performance and versatility.
   
4. Couchbase: Couchbase is a document-oriented database that is designed to be scalable, flexible, and easy to use. It offers support for multiple data models, including key-value, document, and graph.
   
5. DynamoDB: DynamoDB is a fully managed, cloud-based NoSQL database service that is designed to be scalable, highly available, and flexible. It offers a variety of data models, including key-value and document.
   

These are just a few examples of popular NoSQL databases. There are many other options available, each with their own unique features and capabilities.

Deciding whether to use a SQL or a NoSQL database depends on the specific needs of your application. Here are some general guidelines for when each type of database might be more suitable:

• Use a SQL database when:
• You need to store structured data with a well-defined schema
• You need to support transactions and ACID (atomicity, consistency, isolation, durability) guarantees
• You need to perform complex queries using SQL
• You need to enforce referential integrity using foreign keys
• Use a NoSQL database when:
• You need to store large volumes of data that does not have a fixed schema
• You need to scale horizontally to support large numbers of concurrent users or high levels of data ingestion
• You need to support flexible and agile development by allowing changes to the data model without requiring schema updates
• You need to work with unstructured or semi-structured data, such as log files or social media data

It's worth noting that SQL and NoSQL databases are not mutually exclusive, and it's often possible to use a combination of both in a single application. For example, you might use a SQL database for storing structured data and a NoSQL database for storing large volumes of unstructured data. The choice of database should be based on the needs of the application and the data it needs to store and manage.

CAP theorem

In mathematics, the cap theorem, also known as Brewer's theorem, states that it is impossible for a distributed computer system to simultaneously provide more than two of the following three guarantees:

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

The theorem states that a distributed system can satisfy any two of these guarantees at the same time, but not all three. This means that in a distributed system, it is impossible to achieve strong consistency (guaranteeing that every read receives the most recent write) and availability (guaranteeing that every request receives a response) while also being able to tolerate network partitions (meaning that the system can continue to operate even when some nodes are disconnected).

The cap theorem has important implications for the design of distributed systems, as it means that trade-offs must be made between consistency, availability, and partition tolerance.

Here are some popular SQL databases, grouped by the type of consistency, availability, and partition tolerance (CAP) guarantees they provide:

• Strong consistency and high availability:
• Amazon RDS (cloud)
• Google Cloud SQL (cloud)
• Microsoft SQL Server (non-cloud)
• Oracle Database (non-cloud)
• PostgreSQL (non-cloud)
• High availability and partition tolerance:
• Amazon Aurora (cloud)
• Google Cloud Spanner (cloud)
• Microsoft SQL Server Always On (non-cloud)
• Oracle Real Application Clusters (RAC) (non-cloud)
• PostgreSQL BDR (non-cloud)
• Strong consistency and partition tolerance:
• Amazon RDS (cloud)
• Google Cloud SQL (cloud)
• Microsoft SQL Server (non-cloud)
• Oracle Database (non-cloud)
• PostgreSQL (non-cloud)

It's worth noting that the CAP theorem is a general guideline, and the specific CAP guarantees provided by each database may vary depending on the specific configuration and usage patterns. Some SQL databases, such as Microsoft SQL Server and Oracle Database, offer tunable consistency options that allow users to choose the level of consistency they need for their application.

Here are some popular NoSQL databases, grouped by the type of consistency, availability, and partition tolerance (CAP) guarantees they provide:

• Strong consistency and high availability:
• Amazon DynamoDB (cloud)
• Google Cloud Bigtable (cloud)
• Azure Cosmos DB (cloud)
• MongoDB (non-cloud)
• Couchbase (non-cloud)
• High availability and partition tolerance:
• Amazon Cassandra (cloud)
• Google Cloud Bigtable (cloud)
• Azure Cosmos DB (cloud)
• Cassandra (non-cloud)
• Strong consistency and partition tolerance:
• Amazon Neptune (cloud)
• Google Cloud Bigtable (cloud)
• Azure Cosmos DB (cloud)
• Redis (non-cloud)

It's worth noting that the CAP theorem is a general guideline, and the specific CAP guarantees provided by each database may vary depending on the specific configuration and usage patterns. Some NoSQL databases, such as Azure Cosmos DB, offer tunable consistency options that allow users to choose the level of consistency they need for their application.