DBMS > Google Cloud Datastore vs. Infobright vs. Oracle vs. SAP IQ vs. Solr

System Properties Comparison Google Cloud Datastore vs. Infobright vs. Oracle vs. SAP IQ vs. Solr

Editorial information provided by DB-Engines

Name

Description

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

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Widely used RDBMS

Columnar RDBMS optimized for Big Data analytics

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Document store

Relational DBMS

column-oriented

Search engine

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

Spatial DBMS

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

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	1.02
Rank	#192	Overall
	#90	Relational DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	2.64
Rank	#108	Overall
	#52	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

cloud.google.com/datastore

ignitetech.com/softwarelibrary/infobrightdb

www.oracle.com/database

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

solr.apache.org

Technical documentation

cloud.google.com/datastore/docs

docs.oracle.com/en/database

help.sap.com/docs/SAP_IQ

solr.apache.org/resources.html

Developer

Google

Ignite Technologies Inc.; formerly InfoBright Inc.

Oracle

SAP, formerly Sybase

Apache Software Foundation

Initial release

2008

2005

1980

1994

2006

Current release

23c, September 2023

16.1 SPS04, April 2019

9.6.1, May 2024

License

Commercial or Open Source

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

commercial

restricted free version is available

commercial

Open Source

Apache Version 2

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 and C++

Java

Server operating systems

hosted

Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

AIX
HP-UX
Linux
Solaris
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

Schemaless in JSON and XML columns

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes, details here

yes

supports customizable data types and automatic typing

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

Knowledge Grid Technology used instead

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language (GQL)

yes

with proprietary extensions

yes

Solr Parallel SQL Interface

APIs and other access methods

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

ADO.NET
JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

ADO.NET
JDBC
ODBC
RESTful HTTP API

Java API
RESTful HTTP/JSON API

Supported programming languages

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

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

C
C#
C++
Java
Perl
PHP
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

using Google App Engine

PL/SQL

also stored procedures in Java possible

yes

Java plugins

Triggers

Callbacks using the Google Apps Engine

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding, horizontal partitioning

shared disk or shared nothing architectures with SAP IQ Multiplexer

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Source-replica replication

Multi-source replication
Source-replica replication

SAP/Sybase Replication Server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

can be realized in PL/SQL

Hadoop integration

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

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

Eventual 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

ACID

isolation level can be parameterized

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

User concepts

Access control

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

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

fine grained access rights according to SQL-standard

yes

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