DBMS > Amazon DocumentDB vs. Apache Cassandra vs. Quasardb vs. Riak TS

System Properties Comparison Amazon DocumentDB vs. Apache Cassandra vs. Quasardb vs. Riak TS

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

Distributed, high-performance timeseries database

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Document store

Wide column store

Time Series DBMS

Secondary database models

Vector DBMS

starting with release V5

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

Score	1.91
Rank	#124	Overall
	#22	Document stores

Score	98.94
Rank	#11	Overall
	#1	Wide column stores

Score	0.13
Rank	#332	Overall
	#28	Time Series DBMS

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Website

aws.amazon.com/documentdb

cassandra.apache.org

quasar.ai

Technical documentation

aws.amazon.com/documentdb/resources

cassandra.apache.org/doc/latest

doc.quasar.ai/master

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

quasardb

Open Source, formerly Basho Technologies

Initial release

2019

2008

2009

2015

Current release

5.0-rc1, July 2024

3.14.1, January 2024

3.0.0, September 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

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

C++

Erlang

Server operating systems

hosted

BSD
Linux
OS X
Windows

Linux
OS X

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

yes

restricted

only equality queries, not always the best performing solution

yes

with tags

restricted

SQL

Support of SQL

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

SQL-like query language

yes, limited

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

Proprietary protocol

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

HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

no "single point of failure"

Sharding

consistent hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

selectable replication factor

Representation of geographical distribution of servers is possible

Source-replica replication with selectable replication factor

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

links between datasets can be stored

Transaction concepts

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

Atomic single-document operations

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Access rights for users and roles

Access rights for users can be defined per object

Cryptographically strong user authentication and audit trail

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
» more

Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
» more

Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
» more

Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
» more

Market metrics

Apache Cassandra is used by 40% of the Fortune 100.
» more

Licensing and pricing models

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

News

How to Create Vector Embeddings in Node.js
25 September 2024

Glean and DataStax Partner to Help Developers Harness Powerful Enterprise Search
23 September 2024

DataStax Named a Leader in The Forrester Wave™: Vector Databases, Q3 2024
23 September 2024

How to Build a Notion Clone with Astra DB and Mongoose
20 September 2024

How to Monitor DataStax-Powered RAG Applications with Fiddler
19 September 2024

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.