Updated May 30, 2023
Difference Between PostgreSQL vs Elasticsearch
The PostgreSQL vs Elasticsearch is defined as the PostgreSQL is a powerful, open-source, object-relational database management system that is available for free, it is an advanced version of SQL that supports various user-defined data types and the functions which also support different functions such as foreign keys, subqueries, and triggers, whereas, the elasticsearch is the highly scalable, free and open-source document-oriented search engine with HTTP web interface, in which it has a complex search feature so that all types of data can be searched including text, numeric and it allows to store and managed big volume of data quickly in real-time.
Head to Head Comparison Between PostgreSQL vs Elasticsearch (Infographics)
Below are the top 10 differences between PostgreSQL vs Elasticsearch:
Key Difference Between PostgreSQL vs Elasticsearch
Let us discuss some of the major key differences between PostgreSQL vs Elasticsearch:
- Database Model: PostgreSQL is an SQL database, specifically a relational database management system (RDBMS). It stores data in tables with rows and columns, enabling users to establish relationships between tables. It utilizes SQL for querying and manipulating data, whereas elasticsearch does not use SQL for querying purpose, and also it does not store data in tables; instead of tables, it holds complex data structure in record or document form, so in a cluster, documents are distributed across different nodes, and if any node is required that can be accessed immediately, in this way they differ in database modelling.
- Flexibility: The flexibility between PostgreSQL and Elasticsearch depends on the specification of the schema so Elasticsearch can automatically gather and analyzed data that the user is trying to store; we do not have to specify an index for it; It actively identifies numeric, boolean, and timestamp data types, and it enforces the identification of strings as well. On the other hand, Elasticsearch can execute a fixed schema, meaning that predefined tables with typed columns must be included in schemas. This fixed schema allows Elasticsearch to offer a rich set of functionalities.
- Security: PostgreSQL has in-built secure authentication that can give control access of the user, its controls at light-weight directory access protocol, and SCRAM-SHA-256, etc., to make RDBMS one of the most secure, whereas elasticsearch does not have in-built secure authentication. Hence, it means any user who wishes to connect with it will have admin rights. When a user has an application, every user needs to authorize the authentication, every user is a superuser for it, and they have provided this way of security.
- Transaction Support: The strong transaction mechanism has been supported by PostgreSQL; transactions in it are in multiple steps, it may perform all steps or none, the user can influence BEGIN and COMMIT commands on huge operations together, and it rollback by using ROLLBACK and SAVEPOINT command at its given point, whereas elasticsearch is specially designed to give high-speed to the user for database operations, as it supports transaction then it requires complex operations due to that the process may slow down, it does not have transaction in-built hence it cannot allow to rollback a document which the user has submitted instead of it has the write-ahead-log feature to which ensure the durability of all the database operations hence there is no need to perform commit, During an index operation in Elasticsearch, the database operations must be acknowledged by multiple replicas before returning.
- Cloud-Based Offering: This is another key difference between PostgreSQL and Elasticsearch in that PostgreSQL does not support the cloud-based offering, so users have to depend on the third party which the developer recommended, and third-party vendors, aiven, amazon web services, etc., whereas elasticsearch has supported the cloud-based offering, the users of it are across the various tiers.
PostgreSQL vs Elasticsearch Comparison Table
Let’s discuss the top comparison between PostgreSQL vs Elasticsearch:
|PostgreSQL is a relational database management system (RDBMS), an advanced version of SQL.
|Whereas it stores complex data as it is not a SQL-based system.
|In PostgreSQL, you actively store data across multiple tables in rows and columns.
|Whereas there are many ways to store data in elasticsearch, it does not store data in tables instead, it stores complex data structures serialized as JSON documents.
|It can handle large volumes of data, is very efficient, and has good data management, so the demand for it is high.
|Whereas it uses natural language and s mainly used for searching text, it works well in searching.
|PostgreSQL is flexible in terms of data because it supports a wide range of data types.
|Whereas it is flexible about its data model because we can nest fields as deep as we want and as long as it can stay at the same data type.
|It uses SQL for querying and is the best tool for relational data; when it comes to data building in new tables, the loading of data is consistent.
|In elasticsearch, we do not have to learn any query language to add data or perform some operations in Elasticsearch.
|It does not have a free trial; it is paid.
|Whereas it has a free trial.
|The performance of it is less than the elasticsearch.
|On the other hand, we can search more data sizes, giving better performance.
|As it is a traditional RDBMS, it does its full-text searches.
|Where it is specially designed to search enormous text and records.
|If we want full-text, then PostgreSQL will do the best featuring.
|Whereas, if we want to pre-process the posts into some fields and indexes, storing them first in elasticsearch is good.
|If we have a small amount of data to search, we can choose PostgreSQL.
|On the other hand, when we want a large transaction and data repository in the future, then this is beneficial.
In this article, we have concluded some differences between PostgreSQL and Elasticsearch in detail; we can say that integrating data from all databases and storing it in a centralized location is most important, and also the database is better than the other unless the choice between them depends on our business data requirement.
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