DBMS > Atos Standard Common Repository vs. Badger vs. etcd vs. Oracle Coherence

System Properties Comparison Atos Standard Common Repository vs. Badger vs. etcd vs. Oracle Coherence

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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

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

A distributed reliable key-value store

Oracles in-memory data grid solution

Primary database model

Document store
Key-value store

Key-value store

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Website

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

github.com/dgraph-io/badger

etcd.io
github.com/etcd-io/etcd

www.oracle.com/java/coherence

Technical documentation

godoc.org/github.com/dgraph-io/badger

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

docs.oracle.com/en/middleware/standalone/coherence

Developer

Atos Convergence Creators

DGraph Labs

Oracle

Initial release

2016

2017

2007

Current release

1703

3.4, August 2019

14.1, August 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

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

Server operating systems

Linux

BSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
Windows

experimental

All OS with a Java VM

Data scheme

Schema and schema-less with LDAP views

schema-free

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

APIs and other access methods

LDAP

gRPC
JSON over HTTP

JCache
JPA
RESTful HTTP API

Supported programming languages

All languages with LDAP bindings

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

.Net
C++
Java

Server-side scripts

Stored procedures

Triggers

yes

yes, watching key changes

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

yes, with selectable consistency level

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

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

In-memory capabilities

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

yes

User concepts

Access control

LDAP bind authentication

authentification to access the cache via certificates or http basic authentication

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