Elasticsearch PostgreSQL Comparison: 6 Critical Differences

Data is now considered to be one of the most valuable assets of any organization. It makes transactions within a business easier and facilitates a smooth flow of operations. Data also acts as a key decision-making tool as organizations are relying on evidence-based decision-making more than ever before. As a result, every organization is looking for a way to store their data in the most efficient way possible.

When choosing a modern database, companies usually find themselves in a puzzle on whether to pick an SQL database like PostgreSQL or NoSQL Database like Elasticsearch. Although the two are viable options for businesses, there are a few key differences between them that must be considered. After taking these differences into consideration, an organization should be able to tell which database would be suitable for their requirements. 

This article will help you understand the various PostgreSQL Elasticsearch differences that can help you make an informed decision for your unique business and data requirements

What is Elasticsearch?

Elasticsearch can be defined as a free, distributed, and Open-Source Search and Analytics engine that can be used to handle numerous types of data such as numeric, text, structured, unstructured, etc. The first version of Elasticsearch, released in 2010, was built on a Search Engine software library called Apache Lucene. Elasticsearch is now well-known for its simple REST API, speed, distributed nature, and scalability. Elasticsearch is now also the central component of the Elastic Stack, a set of free and Open-Source tools that allow users to seamlessly perform Data Ingestion and Enrichment, Data Analysis, and Visualization.

Elasticsearch stores data as a collection of documents that are related to each other and hence can be considered a Document-Oriented Search Engine that can be leveraged to store, manage and retrieve Structured, Semi-structured, or Unstructured data. Elasticsearch stores data as JSON documents which means that each document consists of a set of keys along with their corresponding values.

Elasticsearch makes use of a data structure called Inverted Index that gives it the ability to perform exceptionally fast full-text searches. Elasticsearch stores all documents and builds an Inverted Index during the indexing process allowing it to make the document data searchable in real-time.

More information on Elasticsearch can be found here.

Understanding the Key Features of Elasticsearch

The key features of Elasticsearch are as follows:

• Fast Data Access: All Documents in Elasticsearch are stored in close proximity to their corresponding metadata in the Index. This reduces the number of read operations required for your data, thereby bringing down the overall search result response time.
• Automatic Node Recovery: If a Node leaves the Elasticsearch Cluster for any reason such as Node failure, intentional removal, etc., the Master Node takes the necessary actions by replacing the Node with its replica and rebalancing all Shards to manage the load automatically.
• Upgrade Assistant API: The Upgrade Assistant API enables users to check the upgrade status of their Elasticsearch Cluster and reindex Indices that were created in previous versions of Elasticsearch. The assistant helps users prepare for the next major versions of Elasticsearch.
• Index Lifecycle Management: Elasticsearch Index Lifecycle Management (ILM) lets users define and automate numerous policies that help control how long an Elasticsearch Index should live in each phase. It also allows users to set actions to be taken on the Index in each phase.
• Scalability of the Search Engine: Elasticsearch implements a Distributed Architecture that enables it to scale up to thousands of Servers and handle petabytes of data without facing any performance issues. This distributed design is dealt with automatically by Elasticsearch, and hence, the customers can focus on performing their required operations.

What is PostgreSQL?

PostgreSQL is a free and Open-source database. It is now considered to be one of the most powerful Relational Database Management Systems (RDBMS) available in the market. It incorporates SQL and adds a set of new features that allow PostgreSQL to be used for Transactional Databases and as a Data Warehouse for analytical purposes. 

One of the most significant advantages of using PostgreSQL and why it’s becoming the preferred choice for most businesses using Relational Databases is its ability to support the Object Relational Model, which allows users to define custom data types depending on the use case in their application.

More information on PostgreSQL can be found here.

Understanding the Key Features of PostgreSQL

The key features of PostgreSQL are as follows:

• Data Integrity: PostgreSQL ensures Data Integrity by giving users the ability to create Primary and Foreign Keys, Unique and Not Null constraints, Explicit and Advisory Locks, Exclusion Constraints, etc.
• Multiple Data Types: PostgreSQL houses support for a wide variety of data types, including Primitive data types such as Integer, String, Boolean, etc., Structured data types such as an array, date, time, etc., and Document data types such as XML, JSON, etc.
• Highly extensible: PostgreSQL is considered highly extensible due to its support for various Procedural Languages such as PL/pgSQL, Perl, Python, etc., JSON/SQL Path Expressions, Foreign Data Wrappers that can be leveraged to connect to different databases with a standard SQL interface.
• Robust Security: PostgreSQL houses a robust Access Control System along with several secure authentications, including Lightweight Directory Access Protocol(LDAP), SCRAM-SHA-256, etc., making it one of the most secure Relational Database Management Systems (RDBMS) available.
• Highly Reliable: PostgreSQL supports several disaster recovery techniques such as Active Standbys, Point In Time Recovery (PITR), Tablespaces, along with numerous types of Replications such as Logical, Synchronous, and Asynchronous.

Elasticsearch PostgreSQL Key Differences

Although Elasticsearch and PostgreSQL are both well-known Database Management Systems, there are numerous differences between them which are as follows:

• Elasticsearch PostgreSQL Key Differences: Database Model
• Elasticsearch PostgreSQL Key Differences: Transaction Support
• Elasticsearch PostgreSQL Key Differences: Schema Flexibility
• Elasticsearch PostgreSQL Key Differences: CAP Theorem Implementation
• Elasticsearch PostgreSQL Key Differences: Security
• Elasticsearch PostgreSQL Key Differences: Cloud-based Offerings

1) Elasticsearch PostgreSQL Key Differences: Database Model

PostgreSQL is a Relational Database Management System (RDBMS) and hence, it stores data in the form of rows and columns, across numerous tables. It also gives users the ability to form relationships between tables. PostgreSQL is a type of SQL Database and allows the usage of Structured Query Language(SQL) for querying data. A sample PostgreSQL database is as follows:

Elasticsearch is a NoSQL Distributed Document Store. This means that instead of storing data in tables, Elasticsearch stores complex data structures, serialized as JSON documents. These documents are distributed across multiple Nodes in a Cluster and can be accessed immediately from any Node if required.

2) Elasticsearch PostgreSQL Key Differences: Transaction Support

Elasticsearch was built to provide high-speed database operations to its users. Since performing database functions as a transaction requires complex operations that would slow down the process,  Elasticsearch does not house transaction support in a typical sense. Hence, there is no way to roll back a document that has been submitted or submit a group of documents and have either all or none of them indexed in Elasticsearch. Instead, Elasticsearch houses a write-ahead-log that can only help ensure the durability of all database operations without performing any commits. Users can also choose to specify the consistency level of the Index operations regarding how many replicas must acknowledge the database operation before returning.

On the other hand, PostgreSQL houses support for a robust transaction mechanism. Transaction in PostgreSQL bundles multiple steps into one, and either all of those steps are performed, or none of them are. Users can leverage the BEGIN and COMMIT command to bundle operations together and the ROLLBACK and SAVEPOINT command to roll back operations to a given point.

3) Elasticsearch PostgreSQL Key Differences: Schema Flexibility

Users do not have to specify the schema for an Elasticsearch Index upfront. Elasticsearch can automatically infer the data type by analyzing the data a user is trying to store. It does a reasonably good job at identifying numeric, boolean, and timestamps. It leverages the Standard Analyzer for identifying strings.

However, PostgreSQL implements a rigid schema. This means that schemas have to include predefined tables with typed columns. The rigid schema allows PostgreSQL to offer a rich set of functionality that would otherwise have been impossible. 

4) Elasticsearch PostgreSQL Key Differences: CAP Theorem Implementation

The three characteristics that any Database Management System can offer are as follows:

• Consistency: All clients connected to a database see the same data, which means that once any data is written or updated in the database, the same operations should be performed across all its replicas as well.
• Availability: Any request from a client gets at least some response from the database.
• Partition Tolerance: The cluster continues to perform the required operations even if few Nodes go down.

The CAP Theorem states that any database is capable of providing only two out of the three CAP properties.

PostgreSQL can only provide its users with Consistency and Availability but not Partition Tolerance, whereas Elasticsearch can provide its users with Availability and Partition Tolerance. ElasticSearch, however, only ensures per-document consistency, which means that all writes will automatically be executed on the “document owner” Shard and will be replicated on the Replica Shards eventually.

5) Elasticsearch PostgreSQL Key Differences: Security

Elasticsearch does not house any in-built features to ensure User Authentication or Authorization. This means that any user that is able to connect to an Elasticsearch cluster will have Admin rights. Hence, users will have to configure an Authorization and Authentication mechanism in their Application Layer since Elasticsearch will treat every user as a superuser.

PostgreSQL houses a robust Access Control System along with several secure authentications, including Lightweight Directory Access Protocol(LDAP), SCRAM-SHA-256, etc., making it one of the most secure Relational Database Management Systems (RDBMS) available.

6) Elasticsearch PostgreSQL Key Differences: Cloud-based Offerings

Elasticsearch offers its users numerous official Cloud-based offerings across various tiers. The pricing and features offered in each tier are as follows:

PostgreSQL does not offer any official Cloud-based offerings or solutions. Hence, users will have to rely on third-party vendors recommended by the developers of PostgreSQL. These third-party vendors include 2ndQuadrant, Aiven, Amazon Web Services, and many more.

Conclusion

This article provided you with an in-depth understanding of Elasticsearch and PostgreSQL along with the various Elasticsearch PostgreSQL differences. It cannot be said that one database is better than the other unless the requirements are known. Hence, you can make a final choice depending on your business use case and data requirements after understanding various Elasticsearch PostgreSQL differences.

Most modern businesses today use multiple databases for their operations. This results in a complex situation since it can be a complicated task if a common analysis integrating data from all these databases has to be performed. A data integration solution has to be built first that can integrate all data from these databases and store it in a centralized location. Businesses can either choose to make their own data integration solutions or use existing automated No-code platforms like Hevo Data.

Hevo helps you directly transfer data from a source of your choice to a Data Warehouse, Business Intelligence, or desired destination in a fully automated and secure manner without having to write the code. It will make your life easier and make data migration hassle-free. It is User-Friendly, Reliable, and Secure.

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