DBMS > Citus vs. Faircom EDGE vs. Google BigQuery vs. JanusGraph vs. ToroDB

System Properties Comparison Citus vs. Faircom EDGE vs. Google BigQuery vs. JanusGraph vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

Large scale data warehouse service with append-only tables

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Graph DBMS

Document store

Secondary database models

Document store

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

Score	2.15
Rank	#117	Overall
	#56	Relational DBMS

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Website

www.citusdata.com

www.faircom.com/products/faircom-edge

cloud.google.com/bigquery

janusgraph.org

github.com/torodb/server

Technical documentation

docs.citusdata.com

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

cloud.google.com/bigquery/docs

docs.janusgraph.org

Developer

FairCom Corporation

Google

Linux Foundation; originally developed as Titan by Aurelius

8Kdata

Initial release

2010

1979

2010

2017

2016

Current release

8.1, December 2018

V3, October 2020

0.6.3, February 2023

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

commercial

Restricted, free version available

commercial

Open Source

Apache 2.0

Open Source

AGPL-V3

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

ANSI C, C++

Java

Server operating systems

Linux

Android
Linux

ARM, x86
Raspbian
Windows

hosted

Linux
OS X
Unix
Windows

All OS with a Java 7 VM

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

string, integer, double, boolean, date, object_id

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

yes

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

yes

ANSI SQL queries

yes

APIs and other access methods

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

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

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

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Clojure
Java
Python

Server-side scripts

Stored procedures

user defined functions

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

yes

.Net, JavaScript, C/C++

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning

Customizable business rules for partitioning

none

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

yes

Synchronous and asynchronous realtime replication based on transaction logs

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

Relationships in graphs

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

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

fine grained access rights according to SQL-standard

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

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

Google Cloud Identity & Access Management (IAM)

User authentification and security via Rexster Graph Server

Access rights for users and roles

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