DBMS > AgensGraph vs. Google BigQuery vs. Graph Engine vs. jBASE vs. PouchDB

System Properties Comparison AgensGraph vs. Google BigQuery vs. Graph Engine vs. jBASE vs. PouchDB

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

Large scale data warehouse service with append-only tables

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

A robust multi-value DBMS comprising development tools and middleware

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Graph DBMS
Relational DBMS

Relational DBMS

Graph DBMS
Key-value store

Multivalue DBMS

Document store

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	1.41
Rank	#159	Overall
	#3	Multivalue DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Website

bitnine.net/agensgraph

cloud.google.com/bigquery

www.graphengine.io

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

pouchdb.com

Technical documentation

bitnine.net/documentation

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

pouchdb.com/guides

Developer

Bitnine Global Inc.

Google

Microsoft

Rocket Software (formerly Zumasys)

Apache Software Foundation

Initial release

2016

2010

1991

2012

Current release

2.1, December 2018

5.7

7.1.1, June 2019

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

MIT License

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

.NET and C

JavaScript

Server operating systems

Linux
OS X
Windows

hosted

.NET

AIX
Linux
Windows

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

Data scheme

depending on used data model

yes

schema-free

Typing

predefined data types such as float or date

yes

optional

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 views

SQL

Support of SQL

yes

Embedded SQL for jBASE in BASIC

APIs and other access methods

Cypher Query Language
JDBC

RESTful HTTP/JSON API

RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

C
Java
JavaScript
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

.Net
Basic
Jabbascript
Java

JavaScript

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

yes

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

none

horizontal partitioning

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

fine grained access rights according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

Access rights can be defined down to the item level

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