DBMS > Amazon DocumentDB vs. Citus vs. EJDB vs. Fauna vs. Trafodion

System Properties Comparison Amazon DocumentDB vs. Citus vs. EJDB vs. Fauna vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

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

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

Transactional SQL-on-Hadoop DBMS

Primary database model

Document store

Relational DBMS

Document store

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Document store

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	1.52
Rank	#153	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Website

aws.amazon.com/documentdb

www.citusdata.com

github.com/Softmotions/ejdb

fauna.com

trafodion.apache.org

Technical documentation

aws.amazon.com/documentdb/resources

docs.citusdata.com

github.com/Softmotions/ejdb/blob/master/README.md

docs.fauna.com

trafodion.apache.org/documentation.html

Developer

Softmotions

Fauna, Inc.

Apache Software Foundation, originally developed by HP

Initial release

2019

2010

2012

2014

Current release

8.1, December 2018

2.3.0, February 2019

License

Commercial or Open Source

commercial

Open Source

AGPL, commercial license also available

Open Source

GPLv2

commercial

Open Source

Apache 2.0

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.

Implementation language

Scala

C++, Java

Server operating systems

hosted

Linux

server-less

hosted

Linux

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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.

yes

specific XML type available, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

in-process shared library

RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

user defined functions

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

horizontal partitioning

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

Source-replica replication

other methods possible by using 3rd party extensions

none

Multi-source replication

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

Atomic single-document operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

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.

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

Identity management, authentication, and access control

fine grained access rights according to SQL-standard

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