DBMS > CouchDB vs. Google BigQuery vs. IRONdb vs. JanusGraph vs. OpenMLDB

System Properties Comparison CouchDB vs. Google BigQuery vs. IRONdb vs. JanusGraph vs. OpenMLDB

Editorial information provided by DB-Engines

Name

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

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Large scale data warehouse service with append-only tables

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

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

An open-source machine learning database that provides a feature platform for training and inference

Primary database model

Secondary database models

Spatial DBMS

using the Geocouch extension

Relational DBMS

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

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Website

couchdb.apache.org

cloud.google.com/bigquery

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

janusgraph.org

openmldb.ai

Technical documentation

docs.couchdb.org/en/stable

cloud.google.com/bigquery/docs

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

docs.janusgraph.org

openmldb.ai/docs/zh/main

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Google

Circonus LLC.

Linux Foundation; originally developed as Titan by Aurelius

4 Paradigm Inc.

Initial release

2005

2010

2017

2020

Current release

3.3.3, December 2023

V0.10.20, January 2018

0.6.3, February 2023

2024-2 February 2024

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

Apache 2.0

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

Erlang

C and C++

Java

C++, Java, Scala

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

hosted

Linux

Linux
OS X
Unix
Windows

Linux

Data scheme

schema-free

yes

schema-free

yes

Fixed schema

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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.

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

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

yes

APIs and other access methods

RESTful HTTP/JSON API

HTTP API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

JDBC
SQLAlchemy

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Clojure
Java
Python

C++
Go
Java
Python
Scala

Server-side scripts

Stored procedures

View functions in JavaScript

user defined functions

in JavaScript

yes, in Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

none

Automatic, metric affinity per node

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

configurable replication factor, datacenter aware

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

atomic operations within a single document possible

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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 privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.