DBMS > Atos Standard Common Repository vs. Cachelot.io vs. Drizzle vs. Lovefield vs. Stardog

System Properties Comparison Atos Standard Common Repository vs. Cachelot.io vs. Drizzle vs. Lovefield vs. Stardog

Editorial information provided by DB-Engines

Name

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

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

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

In-memory caching system

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Embeddable relational database for web apps written in pure JavaScript

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Document store
Key-value store

Key-value store

Relational DBMS

Graph DBMS
RDF store

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

Score	0.04
Rank	#388	Overall
	#62	Key-value stores

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

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

cachelot.io

google.github.io/lovefield

www.stardog.com

Technical documentation

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

docs.stardog.com

Developer

Atos Convergence Creators

Drizzle project, originally started by Brian Aker

Google

Stardog-Union

Initial release

2016

2015

2008

2014

2010

Current release

1703

7.2.4, September 2012

2.1.12, February 2017

7.3.0, May 2020

License

Commercial or Open Source

commercial

Open Source

Simplified BSD License

Open Source

GNU GPL

Open Source

Apache 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

JavaScript

Java

Server operating systems

Linux

FreeBSD
Linux
OS X

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

tested with Chrome, Firefox, IE, Safari

Linux
macOS
Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

schema-free and OWL/RDFS-schema support

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

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language

via JavaScript builder pattern

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

LDAP

Memcached protocol

JDBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

All languages with LDAP bindings

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

C
C++
Java
PHP

JavaScript

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

hooks for callbacks inside the server can be used.

Using read-only observers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Multi-source replication
Source-replica replication

none

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

none

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

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

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

yes

User concepts

Access control

LDAP bind authentication

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users and roles

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