DBMS > Google BigQuery vs. JanusGraph vs. Percona Server for MongoDB vs. PouchDB

System Properties Comparison Google BigQuery vs. JanusGraph vs. Percona Server for MongoDB vs. PouchDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A Graph DBMS optimized for distributed clusters

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

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Relational DBMS

Graph DBMS

Document store

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	1.85
Rank	#134	Overall
	#12	Graph DBMS

Score	0.50
Rank	#250	Overall
	#39	Document stores

Score	2.18
Rank	#114	Overall
	#21	Document stores

Website

cloud.google.com/bigquery

janusgraph.org

www.percona.com/mongodb/software/percona-server-for-mongodb

pouchdb.com

Technical documentation

cloud.google.com/bigquery/docs

docs.janusgraph.org

docs.percona.com/percona-distribution-for-mongodb

pouchdb.com/guides

Developer

Google

Linux Foundation; originally developed as Titan by Aurelius

Percona

Apache Software Foundation

Initial release

2010

2017

2015

2012

Current release

1.0.0, October 2023

3.4.10-2.10, November 2017

7.1.1, June 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPL Version 2

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

Java

C++

JavaScript

Server operating systems

hosted

Linux
OS X
Unix
Windows

Linux

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

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

proprietary protocol using JSON

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Clojure
Java
Python

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

JavaScript

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

JavaScript

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

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

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

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

via In-Memory Engine

yes

User concepts

Access control

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

Access rights for users and roles

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