DBMS > IBM Db2 Event Store vs. SwayDB vs. Trafodion vs. VictoriaMetrics

System Properties Comparison IBM Db2 Event Store vs. SwayDB vs. Trafodion vs. VictoriaMetrics

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Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Distributed Event Store optimized for Internet of Things use cases

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Transactional SQL-on-Hadoop DBMS

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

Primary database model

Event Store
Time Series DBMS

Key-value store

Relational DBMS

Time Series DBMS

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	0.00
Rank	#382	Overall
	#59	Key-value stores

Score	1.32
Rank	#162	Overall
	#14	Time Series DBMS

Website

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

swaydb.simer.au

trafodion.apache.org

victoriametrics.com

Technical documentation

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

trafodion.apache.org/documentation.html

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

Developer

IBM

Simer Plaha

Apache Software Foundation, originally developed by HP

VictoriaMetrics

Initial release

2017

2018

2014

2018

Current release

2.0

2.3.0, February 2019

v1.91, May 2023

License

Commercial or Open Source

commercial

free developer edition available

Open Source

GNU Affero GPL V3.0

Open Source

Apache 2.0

Open Source

Apache Version 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++

Scala

C++, Java

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux

FreeBSD
Linux
macOS
OpenBSD

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

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.

Secondary indexes

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

Supported programming languages

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

Java
Kotlin
Scala

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

Java Stored Procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

none

yes, via HBase

Synchronous replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

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

Atomic execution of operations

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

User concepts

Access control

fine grained access rights according to SQL-standard

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