DBMS > IBM Db2 Event Store vs. Oracle vs. Oracle Berkeley DB vs. RisingWave

System Properties Comparison IBM Db2 Event Store vs. Oracle vs. Oracle Berkeley DB vs. RisingWave

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Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

Widely used RDBMS

Widely used in-process key-value store

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

Primary database model

Event Store
Time Series DBMS

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

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

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	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	0.64
Rank	#238	Overall
	#110	Relational DBMS

Website

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

www.oracle.com/database

www.oracle.com/database/technologies/related/berkeleydb.html

www.risingwave.com/database

Technical documentation

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

docs.oracle.com/en/database

docs.oracle.com/cd/E17076_05/html/index.html

docs.risingwave.com/docs/current/intro

Developer

IBM

Oracle

originally developed by Sleepycat, which was acquired by Oracle

RisingWave Labs

Initial release

2017

1980

1994

2022

Current release

2.0

23c, September 2023

18.1.40, May 2020

1.2, September 2023

License

Commercial or Open Source

commercial

free developer edition available

commercial

restricted free version is available

Open Source

commercial license available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

C, Java, C++ (depending on the Berkeley DB edition)

Rust

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

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

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Docker
Linux
macOS

Data scheme

yes

Schemaless in JSON and XML columns

schema-free

yes

Typing

predefined data types such as float or date

yes

Standard SQL-types and JSON

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

with proprietary extensions

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

JDBC
PostgreSQL wire protocol

Supported programming languages

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

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

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

PL/SQL

also stored procedures in Java possible

UDFs in Python or Java

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard 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

Foreign keys

Referential integrity

yes

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

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

Users and Roles

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