DBMS > Heroic vs. IBM Db2 Event Store vs. Kinetica vs. Oracle vs. OrigoDB

System Properties Comparison Heroic vs. IBM Db2 Event Store vs. Kinetica vs. Oracle vs. OrigoDB

Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Distributed Event Store optimized for Internet of Things use cases

Fully vectorized database across both GPUs and CPUs

Widely used RDBMS

A fully ACID in-memory object graph database

Primary database model

Time Series DBMS

Event Store
Time Series DBMS

Relational DBMS

Document store
Object oriented 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	0.46
Rank	#265	Overall
	#22	Time Series DBMS

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

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Website

github.com/spotify/heroic

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

www.kinetica.com

www.oracle.com/database

origodb.com

Technical documentation

spotify.github.io/heroic

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

docs.kinetica.com

docs.oracle.com/en/database

origodb.com/docs

Developer

Spotify

IBM

Kinetica

Oracle

Robert Friberg et al

Initial release

2014

2017

2012

1980

2009

under the name LiveDB

Current release

2.0

7.1, August 2021

23c, September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free developer edition available

commercial

restricted free version is available

Open Source

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

Java

C and C++

C, C++

C and C++

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux

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

Linux
Windows

Data scheme

schema-free

yes

Schemaless in JSON and XML columns

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

yes

via Elasticsearch

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

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

.NET Client API
HTTP API
LINQ

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

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

Server-side scripts

Stored procedures

yes

user defined functions

PL/SQL

also stored procedures in Java possible

yes

Triggers

yes

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

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Active-active shard replication

Source-replica replication

Multi-source replication
Source-replica replication

Source-replica replication

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

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

depending on model

Transaction concepts

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

ACID

isolation level can be parameterized

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

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

yes

Write ahead log

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

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

Role based authorization

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