DBMS > Badger vs. IRONdb vs. Microsoft Azure Cosmos DB vs. PouchDB

System Properties Comparison Badger vs. IRONdb vs. Microsoft Azure Cosmos DB vs. PouchDB

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Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Globally distributed, horizontally scalable, multi-model database service

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Key-value store

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Secondary database models

Spatial DBMS

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

Score	0.14
Rank	#328	Overall
	#48	Key-value stores

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	2.18
Rank	#114	Overall
	#21	Document stores

Website

github.com/dgraph-io/badger

www.circonus.com/solutions/time-series-database/

azure.microsoft.com/services/cosmos-db

pouchdb.com

Technical documentation

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

docs.circonus.com/irondb/category/getting-started

learn.microsoft.com/azure/cosmos-db

pouchdb.com/guides

Developer

DGraph Labs

Circonus LLC.

Microsoft

Apache Software Foundation

Initial release

2017

2014

2012

Current release

V0.10.20, January 2018

7.1.1, June 2019

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

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

C and C++

JavaScript

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

hosted

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

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

All properties auto-indexed by default

yes

via views

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like query language

APIs and other access methods

HTTP API

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

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

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

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

JavaScript

Server-side scripts

Stored procedures

yes, in Lua

JavaScript

View functions in JavaScript

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic, metric affinity per node

Sharding

Implicit feature of the cloud service

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

none

configurable replication factor, datacenter aware

yes

Implicit feature of the cloud service

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate consistency per node, eventual consistency across nodes

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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 can be defined down to the item level

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