DBMS > Cassandra vs. HBase vs. IBM Cloudant vs. PlanetScale

System Properties Comparison Cassandra vs. HBase vs. IBM Cloudant vs. PlanetScale

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

Wide-column store based on Apache Hadoop and on concepts of BigTable

Database as a Service offering based on Apache CouchDB

Scalable, distributed, serverless MySQL database platform built on top of Vitess

Primary database model

Wide column store

Document store

Relational DBMS

Secondary database models

Vector DBMS

starting with release V5

Document store
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	1.59
Rank	#151	Overall
	#70	Relational DBMS

Website

cassandra.apache.org

hbase.apache.org

www.ibm.com/products/cloudant

planetscale.com

Technical documentation

cassandra.apache.org/doc/latest

hbase.apache.org/book.html

cloud.ibm.com/docs/Cloudant

planetscale.com/docs

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

Apache Software Foundation

Apache top-level project, originally developed by Powerset

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

PlanetScale

Initial release

2008

2010

2020

Current release

4.1.3, July 2023

2.3.4, January 2021

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache version 2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

Erlang

Server operating systems

BSD
Linux
OS X
Windows

Linux
Unix
Windows

using Cygwin

hosted

Docker
Linux
macOS

Data scheme

schema-free

schema-free, schema definition possible

schema-free

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

restricted

only equality queries, not always the best performing solution

yes

SQL

Support of SQL

SQL-like SELECT, DML and DDL statements (CQL)

yes

with proprietary extensions

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

Java API
RESTful HTTP API
Thrift

RESTful HTTP/JSON API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

C#
Java
JavaScript
Objective-C
PHP
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

Coprocessors in Java

View functions (Map-Reduce) in JavaScript

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency or Eventual Consistency

Eventual Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Atomicity and isolation are supported for single operations

Single row ACID (across millions of columns)

atomic operations within a document possible

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access rights for users can be defined per object

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Access rights for users can be defined per database

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
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Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
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Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
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Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
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Market metrics

Cassandra is used by 40% of the Fortune 100.
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Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
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