Citus vs Hadoop: What are the differences?

Introduction

In the world of big data processing, Citus and Hadoop are two popular solutions that offer distributed computing capabilities for handling large volumes of data. However, there are key differences between these two technologies. In this article, we will explore and compare those differences.

1. Architecture: The fundamental difference lies in their architecture design. Hadoop follows the distributed file system (HDFS) model, where data is stored in a distributed manner across multiple nodes, and processing is performed by MapReduce jobs. On the other hand, Citus is an extension to the Postgres database that distributes and parallelizes data across multiple nodes, enabling distributed query processing.

2. Data Processing Paradigm: While Hadoop is primarily designed for batch processing of data, Citus provides real-time performance by leveraging the massively parallel processing (MPP) capabilities of Postgres. Citus allows for concurrent reads and writes, making it suitable for online transaction processing (OLTP) workloads.

3. Query Language Support: Hadoop uses its own query language called HiveQL, which is based on SQL but has some variations. In contrast, Citus leverages the full power of SQL as it is built as an extension to Postgres. This allows users to leverage their existing SQL skills and tools while working with Citus.

4. Data Storage: Hadoop utilizes a distributed file system where data is stored across multiple nodes. Data is typically stored in a schemaless format, such as Hadoop Distributed File System (HDFS) or Apache Parquet. In Citus, data is stored in a traditional relational database format, following the table-based structure of Postgres.

5. Ease of Deployment and Administration: Hadoop clusters require complex setup and configuration, involving various components like HDFS, YARN, and MapReduce. Additionally, Hadoop clusters often involve managing multiple specialized machines. In contrast, Citus can be easily deployed as an extension to an existing Postgres database, reducing the need for separate cluster management and simplifying administration.

6. Maturity and Ecosystem: Hadoop has been around for a longer time and has a more mature ecosystem with a wide range of tools and technologies built around it, such as Hive, Pig, and Spark. Citus, being an extension to Postgres, benefits from the extensive ecosystem and tooling that exists for Postgres, including various SQL extensions, connectors, and integration options.

In summary, Citus and Hadoop differ in their architecture, data processing paradigms, query language support, data storage models, ease of deployment, administration, and the maturity of their ecosystems. These differences allow organizations to choose the right technology based on their specific requirements and use cases.