DBMS > Badger vs. Ehcache vs. Heroic vs. IBM Cloudant vs. Microsoft Azure Cosmos DB

System Properties Comparison Badger vs. Ehcache vs. Heroic vs. IBM Cloudant vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

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

A widely adopted Java cache with tiered storage options

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

Database as a Service offering based on Apache CouchDB

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Key-value store

Time Series DBMS

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

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

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

github.com/dgraph-io/badger

www.ehcache.org

github.com/spotify/heroic

www.ibm.com/products/cloudant

azure.microsoft.com/services/cosmos-db

Technical documentation

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

www.ehcache.org/documentation

spotify.github.io/heroic

cloud.ibm.com/docs/Cloudant

learn.microsoft.com/azure/cosmos-db

Developer

DGraph Labs

Terracotta Inc, owned by Software AG

Spotify

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Microsoft

Initial release

2017

2009

2014

2010

2014

Current release

3.10.0, March 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

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

Erlang

Server operating systems

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

hosted

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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.

Secondary indexes

yes

via Elasticsearch

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like query language

APIs and other access methods

JCache

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

RESTful HTTP/JSON API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

Java

C#
Java
JavaScript
Objective-C
PHP
Ruby

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

JavaScript

Triggers

yes

Cache Event Listeners

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

by using Terracotta Server

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

by using Terracotta Server

yes

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

none

Tunable Consistency (Strong, Eventual, Weak)

Eventual Consistency
Immediate Consistency

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

supports JTA and can work as an XA resource

atomic operations within a document possible

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

Durability

Support for making data persistent

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

Access rights for users can be defined per database

Access rights can be defined down to the item level

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