DBMS > Atos Standard Common Repository vs. Badger vs. BigchainDB vs. Ehcache vs. Heroic

System Properties Comparison Atos Standard Common Repository vs. Badger vs. BigchainDB vs. Ehcache vs. Heroic

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.

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

A widely adopted Java cache with tiered storage options

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Primary database model

Document store
Key-value store

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	4.64
Rank	#68	Overall
	#8	Key-value stores

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Website

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

github.com/dgraph-io/badger

www.bigchaindb.com

www.ehcache.org

github.com/spotify/heroic

Technical documentation

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

bigchaindb.readthedocs.io/en/latest

www.ehcache.org/documentation

spotify.github.io/heroic

Developer

Atos Convergence Creators

DGraph Labs

Terracotta Inc, owned by Software AG

Spotify

Initial release

2016

2017

2016

2009

2014

Current release

1703

3.10.0, March 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

AGPL v3

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

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

Python

Java

Server operating systems

Linux

BSD
Linux
OS X
Solaris
Windows

Linux

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

via Elasticsearch

SQL

Support of SQL

APIs and other access methods

LDAP

CLI Client
RESTful HTTP API

JCache

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Supported programming languages

All languages with LDAP bindings

Go
Haskell
Java
JavaScript
Python
Ruby

Java

Server-side scripts

Stored procedures

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

Sharding

by using Terracotta Server

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

selectable replication factor

yes

by using Terracotta Server

yes

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

Tunable Consistency (Strong, Eventual, Weak)

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

yes

supports JTA and can work as an XA resource

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

yes

using a tiered cache-storage approach

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

LDAP bind authentication

yes

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