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Table of contents
Elasticsearch: An Overview
PostgreSQL: An Overview
Comparing Elasticsearch and PostgreSQL
Use Cases
Frequently Asked Questions
Can Elasticsearch replace PostgreSQL completely?
Can PostgreSQL handle full-text search like Elasticsearch?
Which is better for a transactional application, Elasticsearch or PostgreSQL?
Last Updated: Mar 27, 2024

Elasticsearch vs Postgresql

Author Nikunj Goel
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Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM


Data management and storage are critical aspects of the digital world. Two popular tools in this domain are Elasticsearch and PostgreSQL, each with distinct functionalities and use-cases. This article will present a comparative study of these two technologies, shedding light on their features, strengths, and potential applications.

Elasticsearch vs Postgresql

Elasticsearch: An Overview

Elasticsearch is an open-source, distributed search and analytics engine. It is designed for horizontal scalability, maximum reliability, and easy management. Elasticsearch provides real-time search and analytics along with the capability to scale with data growth.

Elasticsearch is typically used for full-text search, structured search, analytics, and all in real time. It treats data as documents and indexes them in a scalable way. Here's an example of creating a document in Elasticsearch:

PUT /my_index/_doc/1
  "name": "John Doe",
  "age": 30,
  "interests": ["football", "basketball"]
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PostgreSQL: An Overview

PostgreSQL, also known as Postgres, is a powerful, open-source object-relational database system. It emphasizes extensibility and standards compliance. It can handle workloads ranging from small single-machine applications to large internet-facing applications with many concurrent users.

In Postgres, data is stored in tables and it uses SQL for querying. Here's a basic SQL query example in PostgreSQL:

INSERT INTO employees (name, age, interests)
VALUES ('John Doe', 30, '{"football", "basketball"}');

Comparing Elasticsearch and PostgreSQL

Elasticsearch and PostgreSQL are both robust technologies, but they cater to different needs and use-cases.

Use Cases

Elasticsearch: It's primarily used for use-cases like full-text search, log or event data analysis and real-time application monitoring. Its ability to search through massive amounts of data quickly makes it an excellent choice for big data applications.

PostgreSQL: This is a general-purpose object-relational database management system, perfect for traditional SQL based applications. It's used widely for systems that require execution of complex queries, data warehousing, and online transaction processing (OLTP).


Elasticsearch: Elasticsearch is known for its speed. It can search and retrieve data in near real-time, making it ideal for quick data retrieval use cases.

PostgreSQL: While not as fast as Elasticsearch for search-related queries, PostgreSQL is extremely powerful when executing complex queries due to its advanced optimization algorithms and indexing.


Elasticsearch: It can scale horizontally easily, meaning you can add more machines to distribute the load and store more data.

PostgreSQL: Scaling PostgreSQL is more traditional, with vertical scaling (adding more power to the existing machine) being straightforward, but horizontal scaling (adding more machines) being more complex.

Frequently Asked Questions

Can Elasticsearch replace PostgreSQL completely?

No, Elasticsearch is designed for search and analytics, while PostgreSQL is a general-purpose database. Their use-cases are different.

Can PostgreSQL handle full-text search like Elasticsearch?

PostgreSQL does support full-text search but it's not as performant or feature-rich as Elasticsearch.

Which is better for a transactional application, Elasticsearch or PostgreSQL?

For transactional applications, PostgreSQL is usually the better choice due to its ACID compliance.


In conclusion, both Elasticsearch and PostgreSQL are powerful tools in their respective domains. Elasticsearch excels in search and analytics, offering fast, real-time results. On the other hand, PostgreSQL is a robust and reliable option for traditional database applications, particularly those that require complex queries and transaction processing. It's important to understand the requirements of your specific project before choosing between these two technologies. Remember, the best tool is the one that fits your needs the most effectively.

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