HBase vs MySQL vs PostgreSQL

Overview

MySQL

Stacks129.6K

Followers108.6K

Votes3.8K

GitHub Stars11.8K

Forks4.1K

PostgreSQL

Stacks103.0K

Followers83.9K

Votes3.6K

GitHub Stars19.0K

Forks5.2K

HBase

Stacks511

Followers498

Votes15

GitHub Stars5.5K

Forks3.4K

HBase vs MySQL vs PostgreSQL: What are the differences?

<Write Introduction here>

Data Model: HBase is a NoSQL database that uses a wide-column store data model, while MySQL and PostgreSQL are relational databases that use a tabular data model. In HBase, data is stored in tables with rows having key-value pairs, and columns grouped into column families. MySQL and PostgreSQL store data in tables with rows and columns, following the relational database model.
Scalability: HBase is built to handle large-scale distributed data sets and is designed for horizontal scalability on commodity hardware. On the other hand, MySQL and PostgreSQL are designed with vertical scalability in mind, where you scale up by adding more resources to a single machine, which can lead to performance limitations in handling massive data sets.
Consistency: HBase provides eventual consistency, ensuring that all reads will eventually return the most recent write. In contrast, MySQL and PostgreSQL offer strong consistency guarantees, ensuring that all reads will reflect the latest write at all times. This difference in consistency models affects how these databases are used in applications with specific data consistency requirements.
Data Processing: HBase is optimized for performing real-time, random read/write operations on large data sets, making it suitable for applications that require low latency access to massive amounts of data. MySQL and PostgreSQL are better suited for complex queries and transactional operations where data integrity and consistency are crucial, making them ideal for applications that require ACID compliance.
Schema Flexibility: HBase allows for schema flexibility as it does not enforce a rigid schema on the data, allowing developers to easily modify the structure of data as needed. In contrast, MySQL and PostgreSQL require a predefined schema, where alterations can be time-consuming and risky, especially in production environments with existing data.
Performance: HBase is designed for high-performance read and write operations on large data sets, making it suitable for applications that require low latency and high throughput. MySQL and PostgreSQL are known for their robust query optimization and indexing capabilities, making them ideal for handling complex queries efficiently.

In Summary, HBase differs from MySQL and PostgreSQL in terms of its data model, scalability, consistency, data processing capabilities, schema flexibility, and performance characteristics.

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Advice on MySQL, PostgreSQL, HBase

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k

Comments

Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k

Comments

BrockHerion

May 6, 2020

Needs adviceon

MongoDB

PostgreSQL

Hi everybody, I'm developing an application to be used in a gym setting where athletes fill out a health survey, and coaches can analyze the results. However, due to the dynamic nature of some aspects of the app and more static aspects of the other, I am wondering if/how I would integrate MongoDB with my existing PostgreSQL database. I would like to store things like registrations, license information, and club information in Postgres, while I am thinking about moving things like user surveys, logging, and user settings information over to MongoDB. Some fields on the survey are integers, some large blocks of text, and some are arrays. My thought is, if I moved that data to MongoDB, it would give us greater flexibility in terms of adding and removing fields and data to them, and it would scale a lot easier than Postgres. Not to mention it will be easier to organize that kind of data. Is that overkill or am I approaching this issue the right way? Thank you!

691k views691k

Comments

Detailed Comparison

MySQL	PostgreSQL	HBase
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.	PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.	Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
Statistics
GitHub Stars 11.8K	GitHub Stars 19.0K	GitHub Stars 5.5K
GitHub Forks 4.1K	GitHub Forks 5.2K	GitHub Forks 3.4K
Stacks 129.6K	Stacks 103.0K	Stacks 511
Followers 108.6K	Followers 83.9K	Followers 498
Votes 3.8K	Votes 3.6K	Votes 15
Pros & Cons
Pros 800 Sql 679 Free 562 Easy 528 Widely used 490 Open source Cons 16 Owned by a company with their own agenda 3 Can't roll back schema changes	Pros 765 Relational database 511 High availability 439 Enterprise class database 383 Sql 304 Sql + nosql Cons 10 Table/index bloatings	Pros 9 Performance 5 OLTP 1 Fast Point Queries

What are some alternatives to MySQL, PostgreSQL, HBase?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

CouchDB

Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.

Related Comparisons

HBase vs MySQL vs PostgreSQL: What are the differences?

<Write Introduction here>

Data Model: HBase is a NoSQL database that uses a wide-column store data model, while MySQL and PostgreSQL are relational databases that use a tabular data model. In HBase, data is stored in tables with rows having key-value pairs, and columns grouped into column families. MySQL and PostgreSQL store data in tables with rows and columns, following the relational database model.
Scalability: HBase is built to handle large-scale distributed data sets and is designed for horizontal scalability on commodity hardware. On the other hand, MySQL and PostgreSQL are designed with vertical scalability in mind, where you scale up by adding more resources to a single machine, which can lead to performance limitations in handling massive data sets.
Consistency: HBase provides eventual consistency, ensuring that all reads will eventually return the most recent write. In contrast, MySQL and PostgreSQL offer strong consistency guarantees, ensuring that all reads will reflect the latest write at all times. This difference in consistency models affects how these databases are used in applications with specific data consistency requirements.
Data Processing: HBase is optimized for performing real-time, random read/write operations on large data sets, making it suitable for applications that require low latency access to massive amounts of data. MySQL and PostgreSQL are better suited for complex queries and transactional operations where data integrity and consistency are crucial, making them ideal for applications that require ACID compliance.
Schema Flexibility: HBase allows for schema flexibility as it does not enforce a rigid schema on the data, allowing developers to easily modify the structure of data as needed. In contrast, MySQL and PostgreSQL require a predefined schema, where alterations can be time-consuming and risky, especially in production environments with existing data.
Performance: HBase is designed for high-performance read and write operations on large data sets, making it suitable for applications that require low latency and high throughput. MySQL and PostgreSQL are known for their robust query optimization and indexing capabilities, making them ideal for handling complex queries efficiently.

In Summary, HBase differs from MySQL and PostgreSQL in terms of its data model, scalability, consistency, data processing capabilities, schema flexibility, and performance characteristics.

HBase vs MySQL vs PostgreSQL

Overview