DBMS > IBM Db2 Event Store vs. Infobright vs. Oracle vs. Solr

System Properties Comparison IBM Db2 Event Store vs. Infobright vs. Oracle vs. Solr

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Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

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

Widely used RDBMS

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

Primary database model

Event Store
Time Series DBMS

Relational DBMS

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	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	1.02
Rank	#192	Overall
	#90	Relational DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

www.ibm.com/products/db2-event-store

ignitetech.com/softwarelibrary/infobrightdb

www.oracle.com/database

solr.apache.org

Technical documentation

www.ibm.com/docs/en/db2-event-store

docs.oracle.com/en/database

solr.apache.org/resources.html

Developer

IBM

Ignite Technologies Inc.; formerly InfoBright Inc.

Oracle

Apache Software Foundation

Initial release

2017

2005

1980

2006

Current release

2.0

23c, September 2023

9.6.1, May 2024

License

Commercial or Open Source

commercial

free developer edition available

commercial

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

commercial

restricted free version is available

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

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

Linux

Linux, macOS, Windows for the developer addition

Linux
Windows

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

All OS with a Java VM

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

Data scheme

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

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

Knowledge Grid Technology used instead

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

with proprietary extensions

Solr Parallel SQL Interface

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Java API
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

.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

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

Server-side scripts

Stored procedures

yes

PL/SQL

also stored procedures in Java possible

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

Source-replica replication

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

isolation level can be parameterized

optimistic locking

Concurrency

Support for concurrent manipulation of data

No - written data is immutable

yes

Durability

Support for making data persistent

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

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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