DBMS > Amazon DocumentDB vs. Cassandra vs. HEAVY.AI vs. Quasardb

System Properties Comparison Amazon DocumentDB vs. Cassandra vs. HEAVY.AI vs. Quasardb

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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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Distributed, high-performance timeseries database

Primary database model

Secondary database models

Vector DBMS

starting with release V5

Spatial DBMS

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

aws.amazon.com/documentdb

cassandra.apache.org

github.com/heavyai/heavydb
www.heavy.ai

quasar.ai

Technical documentation

aws.amazon.com/documentdb/resources

cassandra.apache.org/doc/latest

docs.heavy.ai

doc.quasar.ai/master

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

HEAVY.AI, Inc.

quasardb

Initial release

2019

2008

2016

2009

Current release

4.1.3, July 2023

5.10, January 2022

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Apache Version 2; enterprise edition available

commercial

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

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++ and CUDA

C++

Server operating systems

hosted

BSD
Linux
OS X
Windows

Linux

BSD
Linux
OS X
Windows

Data scheme

schema-free

yes

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

SQL

Support of SQL

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

yes

SQL-like query language

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

Proprietary protocol

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

JDBC
ODBC
Thrift
Vega

HTTP API

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

All languages supporting JDBC/ODBC/Thrift
Python

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

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

no "single point of failure"

Sharding

Round robin

Sharding

consistent hashing

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

Multi-source replication

Source-replica replication with 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

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

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

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

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

fine grained access rights according to SQL-standard

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...
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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.
» more

Licensing and pricing models

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