DBMS > Google Cloud Datastore vs. Manticore Search vs. OpenQM vs. RDF4J vs. Tibero

System Properties Comparison Google Cloud Datastore vs. Manticore Search vs. OpenQM vs. RDF4J vs. Tibero

Editorial information provided by DB-Engines

Name

Description

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Multi-storage database for search, including full-text search.

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

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	0.34
Rank	#284	Overall
	#10	Multivalue DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	1.59
Rank	#148	Overall
	#68	Relational DBMS

Website

cloud.google.com/datastore

manticoresearch.com

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

rdf4j.org

us.tmaxsoft.com/products/tibero

Technical documentation

cloud.google.com/datastore/docs

manual.manticoresearch.com

rdf4j.org/documentation

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Google

Manticore Software

Rocket Software, originally Martin Phillips

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

TmaxSoft

Initial release

2008

2017

1993

2004

2003

Current release

6.0, February 2023

3.4-12

6, April 2015

License

Commercial or Open Source

commercial

Open Source

GPL version 2

Open Source

GPLv2, extended commercial license available

Open Source

Eclipse Distribution License (EDL), v1.0.

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

C++

Java

C and Assembler

Server operating systems

hosted

FreeBSD
Linux
macOS
Windows

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux
OS X
Unix
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

Fixed schema

yes

with some exceptions

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes, details here

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like query language

yes

APIs and other access methods

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

.Net
Basic
C
Java
Objective C
PHP
Python

Java
PHP
Python

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

using Google App Engine

user defined functions

yes

Persistent Stored Procedure (PSM)

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

yes

none

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Synchronous replication based on Galera library

yes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

yes

in-memory storage is supported as well

yes

In-memory capabilities

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

planned for next version

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

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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