The value of horizontal scaling is more clear in the constantly changing world of data management. With the aid of the open-source Citus extension, PostgreSQL is given the capacity of scalable, distributed tables. It combines a distributed database management system's scalability and performance with PostgreSQL's strong data integrity. This blog explores Citus in-depth, illuminating how it manages massive amounts of data.

Introduction
SQL and PostgreSQL, two well-known relational databases, excel in upholding data integrity and providing robust querying capabilities. One of the most well-liked open-source database management systems is PostgreSQL in particular. When dealing with extraordinarily huge datasets that necessitate high concurrency, it runs against constraints. By presenting an architectural solution that distributes data over numerous nodes, Citus responds to these issues. Performance is increased and hardware resource management is improved with this method.

Citus Architecture
Citus is built around PostgreSQL servers that are part of a Citus cluster and each have the Citus extension installed in addition to other extensions. It makes use of PostgreSQL's extension APIs in two major ways to alter the behavior of the database:

• Replication of database items across all servers, including custom types and functions.

• Two new table kinds are introduced, both of which are enhanced for more servers.

• Citus uses a technology called sharding to provide scalability. It shards or chunks big databases into smaller pieces and distributes these pieces across many nodes. By routing questions to the appropriate nodes and collecting the results, query management is handled intelligently.

Key Elements
Citus has a number of appealing qualities:

• In contrast to vertical scaling, which involves boosting the performance of already-existing machines, horizontal scaling facilitates scaling out by adding more machines to the cluster.

• Utilizing the aggregate query processing capability of all nodes, Citus conducts queries in parallel with data spread across numerous nodes to greatly speed up performance.

• High Throughput: Citus is designed for large-scale data applications, processing enormous volumes of data and queries quickly and effectively by avoiding bottlenecks and utilizing resources to their fullest potential.

• Multi-Tenancy: It enables the development of applications with several tenants in which data is spread across various distributed tables.

• Simpler learning curve for Citus, especially for those who are already familiar with PostgreSQL, is made possible by its compatibility with PostgreSQL, which enables users to use familiar PostgreSQL tools, extensions, and approaches.

Conclusion
Citus gives relational databases the flexibility to scale horizontally, making it a great choice for applications needing strong query capabilities, the capacity to manage massive datasets, and the capacity to support concurrent users. Real-time analytics and large-scale applications are two of its main use cases where Citus's horizontal scalability exceeds alternatives' vertical scaling.