DBMS > IBM Db2 Event Store vs. Kinetica vs. NuoDB vs. Oracle vs. Prometheus

System Properties Comparison IBM Db2 Event Store vs. Kinetica vs. NuoDB vs. Oracle vs. Prometheus

Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

Fully vectorized database across both GPUs and CPUs

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Widely used RDBMS

Open-source Time Series DBMS and monitoring system

Primary database model

Event Store
Time Series DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

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

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Website

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

www.kinetica.com

www.3ds.com/nuodb-distributed-sql-database

www.oracle.com/database

prometheus.io

Technical documentation

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

docs.kinetica.com

doc.nuodb.com

docs.oracle.com/en/database

prometheus.io/docs

Developer

IBM

Kinetica

Dassault Systèmes

originally NuoDB, Inc.

Oracle

Initial release

2017

2012

2013

1980

2015

Current release

2.0

7.1, August 2021

23c, September 2023

License

Commercial or Open Source

commercial

free developer edition available

commercial

limited edition free

commercial

restricted free version is available

Open Source

Apache 2.0

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++

C, C++

C++

C and C++

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

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

Linux
Windows

Data scheme

yes

Schemaless in JSON and XML columns

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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 of XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

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

C++
Java
JavaScript (Node.js)
Python

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

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
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

Java, SQL

PL/SQL

also stored procedures in Java possible

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

data is dynamically stored/cached on the nodes where it is read/written

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

Source-replica replication

yes

Managed transparently by NuoDB

Multi-source replication
Source-replica replication

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

tunable commit protocol

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

No - written data is immutable

yes

MVCC

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

GPU vRAM or System RAM

yes

Temporary table

yes

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

Standard SQL roles/ privileges, Administrative Users

fine grained access rights according to SQL-standard

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