DBMS > BoltDB vs. DataFS vs. Google BigQuery vs. JanusGraph vs. Oracle Coherence

System Properties Comparison BoltDB vs. DataFS vs. Google BigQuery vs. JanusGraph vs. Oracle Coherence

Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

Oracles in-memory data grid solution

Primary database model

Secondary database models

Graph DBMS

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

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Website

github.com/boltdb/bolt

newdatabase.com

cloud.google.com/bigquery

janusgraph.org

www.oracle.com/java/coherence

Technical documentation

dev.mobiland.com/Overview.xsp

cloud.google.com/bigquery/docs

docs.janusgraph.org

docs.oracle.com/en/middleware/standalone/coherence

Developer

Mobiland AG

Google

Linux Foundation; originally developed as Titan by Aurelius

Oracle

Initial release

2013

2018

2010

2017

2007

Current release

1.1.263, October 2022

0.6.3, February 2023

14.1, August 2023

License

Commercial or Open Source

Open Source

MIT License

commercial

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

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Windows

hosted

Linux
OS X
Unix
Windows

All OS with a Java VM

Data scheme

schema-free

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

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

SQL

Support of SQL

yes

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

RESTful HTTP/JSON API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

JCache
JPA
RESTful HTTP API

Supported programming languages

.Net
C
C#
C++
VB.Net

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Clojure
Java
Python

.Net
C++
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

no, except callback-events from server when changes happened

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

none

Proprietary Sharding system

none

yes

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

yes, with selectable consistency level

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

none

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

yes

ACID

Since BigQuery is designed for querying data

ACID

configurable

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

optionally

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Windows-Profile

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

authentification to access the cache via certificates or http basic authentication

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.