DBMS > EJDB vs. Fauna vs. Google BigQuery vs. TimesTen vs. Trafodion

System Properties Comparison EJDB vs. Fauna vs. Google BigQuery vs. TimesTen 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

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.

Large scale data warehouse service with append-only tables

In-Memory RDBMS compatible to Oracle

Transactional SQL-on-Hadoop DBMS

Primary database model

Document store

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Relational DBMS

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

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

github.com/Softmotions/ejdb

fauna.com

cloud.google.com/bigquery

www.oracle.com/database/technologies/related/timesten.html

trafodion.apache.org

Technical documentation

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

docs.fauna.com

cloud.google.com/bigquery/docs

docs.oracle.com/database/timesten-18.1

trafodion.apache.org/documentation.html

Developer

Softmotions

Fauna, Inc.

Google

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Apache Software Foundation, originally developed by HP

Initial release

2012

2014

2010

1998

2014

Current release

11 Release 2 (11.2.2.8.0)

2.3.0, February 2019

License

Commercial or Open Source

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

server-less

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

in-process shared library

RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

ADO.NET
JDBC
ODBC

Supported programming languages

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

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Java
PL/SQL

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

user defined functions

in JavaScript

PL/SQL

Java Stored Procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

consistent hashing

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

Multi-source replication
Source-replica replication

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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

Identity management, authentication, and access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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