DBMS > Atos Standard Common Repository vs. JanusGraph vs. Lovefield vs. OrientDB vs. Snowflake

System Properties Comparison Atos Standard Common Repository vs. JanusGraph vs. Lovefield vs. OrientDB vs. Snowflake

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

A Graph DBMS optimized for distributed clusters

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

Embeddable relational database for web apps written in pure JavaScript

Multi-model DBMS (Document, Graph, Key/Value)

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Document store
Key-value store

Graph DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store

Relational DBMS

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

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

janusgraph.org

google.github.io/lovefield

orientdb.org

www.snowflake.com

Technical documentation

docs.janusgraph.org

github.com/google/lovefield/blob/master/docs/spec_index.md

www.orientdb.com/docs/last/index.html

docs.snowflake.net/manuals/index.html

Developer

Atos Convergence Creators

Linux Foundation; originally developed as Titan by Aurelius

Google

OrientDB LTD; CallidusCloud; SAP

Snowflake Computing Inc.

Initial release

2016

2017

2014

2010

2014

Current release

1703

0.6.3, February 2023

2.1.12, February 2017

3.2.29, March 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache version 2

commercial

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

Java

JavaScript

Java

Server operating systems

Linux

Linux
OS X
Unix
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

All OS with a Java JDK (>= JDK 6)

hosted

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

optional

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

SQL-like query language, no joins

yes

APIs and other access methods

LDAP

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

Clojure
Java
Python

JavaScript

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Java, Javascript

user defined functions

Triggers

yes

Using read-only observers

Hooks

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

yes

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

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

relationship in graphs

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

LDAP bind authentication

User authentification and security via Rexster Graph Server

Access rights for users and roles; record level security configurable

Users with fine-grained authorization concept, user roles and pluggable authentication

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