DBMS > Ehcache vs. Google Cloud Bigtable vs. OpenQM vs. Stardog vs. SwayDB

System Properties Comparison Ehcache vs. Google Cloud Bigtable vs. OpenQM vs. Stardog vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

QpenQM is a high-performance, self-tuning, multi-value DBMS

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Key-value store

Key-value store
Wide column store

Multivalue DBMS

Graph DBMS
RDF store

Key-value store

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

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

www.ehcache.org

cloud.google.com/bigtable

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

www.stardog.com

swaydb.simer.au

Technical documentation

www.ehcache.org/documentation

cloud.google.com/bigtable/docs

docs.stardog.com

Developer

Terracotta Inc, owned by Software AG

Google

Rocket Software, originally Martin Phillips

Stardog-Union

Simer Plaha

Initial release

2009

2015

1993

2010

2018

Current release

3.10.0, March 2022

3.4-12

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

GPLv2, extended commercial license available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

GNU Affero GPL V3.0

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

Scala

Server operating systems

All OS with a Java VM

hosted

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux
macOS
Windows

Data scheme

schema-free

yes

with some exceptions

schema-free and OWL/RDFS-schema support

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.

yes

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

JCache

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

Java

C#
C++
Go
Java
JavaScript (Node.js)
Python

.Net
Basic
C
Java
Objective C
PHP
Python

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

Cache Event Listeners

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

Multi-source replication in HA-Cluster

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Immediate Consistency in HA-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

yes

supports JTA and can work as an XA resource

Atomic single-row operations

ACID

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights can be defined down to the item level

Access rights for users and roles

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