HBase vs RabbitMQ

Overview

HBase

Stacks511

Followers498

Votes15

GitHub Stars5.5K

Forks3.4K

RabbitMQ

Stacks21.8K

Followers18.9K

Votes558

GitHub Stars13.2K

Forks4.0K

HBase vs RabbitMQ: What are the differences?

Developers describe HBase as "The Hadoop database, a distributed, scalable, big data store". 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. On the other hand, RabbitMQ is detailed as "A messaging broker - an intermediary for messaging". RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

HBase can be classified as a tool in the "Databases" category, while RabbitMQ is grouped under "Message Queue".

"Performance" is the primary reason why developers consider HBase over the competitors, whereas "It's fast and it works with good metrics/monitoring" was stated as the key factor in picking RabbitMQ.

HBase and RabbitMQ are both open source tools. RabbitMQ with 5.88K GitHub stars and 1.73K forks on GitHub appears to be more popular than HBase with 2.87K GitHub stars and 1.98K GitHub forks.

According to the StackShare community, RabbitMQ has a broader approval, being mentioned in 921 company stacks & 532 developers stacks; compared to HBase, which is listed in 54 company stacks and 18 developer stacks.

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Advice on HBase, RabbitMQ

Pulkit

Software Engineer

Oct 30, 2020

Needs adviceon

Django

Amazon SQS

RabbitMQ

Hi! I am creating a scraping system in Django, which involves long running tasks between 1 minute & 1 Day. As I am new to Message Brokers and Task Queues, I need advice on which architecture to use for my system. ( Amazon SQS, RabbitMQ, or Celery). The system should be autoscalable using Kubernetes(K8) based on the number of pending tasks in the queue.

474k views474k

Comments

mediafinger

Feb 13, 2019

Reviewon

Kafka

RabbitMQ

The question for which Message Queue to use mentioned "availability, distributed, scalability, and monitoring". I don't think that this excludes many options already. I does not sound like you would take advantage of Kafka's strengths (replayability, based on an even sourcing architecture). You could pick one of the AMQP options.

I would recommend the RabbitMQ message broker, which not only implements the AMQP standard 0.9.1 (it can support 1.x or other protocols as well) but has also several very useful extensions built in. It ticks the boxes you mentioned and on top you will get a very flexible system, that allows you to build the architecture, pick the options and trade-offs that suite your case best.

For more information about RabbitMQ, please have a look at the linked markdown I assembled. The second half explains many configuration options. It also contains links to managed hosting and to libraries (though it is missing Python's - which should be Puka, I assume).

159k views159k

Comments

Shantha

Sep 30, 2020

Needs adviceon

RabbitMQ

Celery

MongoDB

I am just a beginner at these two technologies.

Problem statement: I am getting lakh of users from the sequel server for whom I need to create caches in MongoDB by making different REST API requests.

Here these users can be treated as messages. Each REST API request is a task.

I am confused about whether I should go for RabbitMQ alone or Celery.

If I have to go with RabbitMQ, I prefer to use python with Pika module. But the challenge with Pika is, it is not thread-safe. So I am not finding a way to execute a lakh of API requests in parallel using multiple threads using Pika.

If I have to go with Celery, I don't know how I can achieve better scalability in executing these API requests in parallel.

334k views334k

Comments

Detailed Comparison

HBase	RabbitMQ
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.	RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.
-	Robust messaging for applications;Easy to use;Runs on all major operating systems;Supports a huge number of developer platforms;Open source and commercially supported
Statistics
GitHub Stars 5.5K	GitHub Stars 13.2K
GitHub Forks 3.4K	GitHub Forks 4.0K
Stacks 511	Stacks 21.8K
Followers 498	Followers 18.9K
Votes 15	Votes 558
Pros & Cons
Pros 9 Performance 5 OLTP 1 Fast Point Queries	Pros 235 It's fast and it works with good metrics/monitoring 80 Ease of configuration 60 I like the admin interface 52 Easy to set-up and start with 22 Durable Cons 9 Too complicated cluster/HA config and management 6 Needs Erlang runtime. Need ops good with Erlang runtime 5 Configuration must be done first, not by your code 4 Slow

What are some alternatives to HBase, RabbitMQ?

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.

MySQL

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

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.

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

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.