DBMS > Blueflood vs. IBM Db2 Event Store vs. Newts vs. Tkrzw

System Properties Comparison Blueflood vs. IBM Db2 Event Store vs. Newts vs. Tkrzw

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

Distributed Event Store optimized for Internet of Things use cases

Time Series DBMS based on Cassandra

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Time Series DBMS

Event Store
Time Series DBMS

Time Series DBMS

Key-value store

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

blueflood.io

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

opennms.github.io/newts

dbmx.net/tkrzw

Technical documentation

github.com/rax-maas/blueflood/wiki

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

github.com/OpenNMS/newts/wiki

Developer

Rackspace

IBM

OpenNMS Group

Mikio Hirabayashi

Initial release

2013

2017

2014

2020

Current release

2.0

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free developer edition available

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

Java

C and C++

Java

C++

Server operating systems

Linux
OS X

Linux

Linux, macOS, Windows for the developer addition

Linux
OS X
Windows

Linux
macOS

Data scheme

predefined scheme

yes

schema-free

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

SQL

Support of SQL

yes

through the embedded Spark runtime

APIs and other access methods

HTTP REST

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HTTP REST
Java API

Supported programming languages

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

Java

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Active-active shard replication

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

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

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using specific database classes

User concepts

Access control

fine grained access rights according to SQL-standard

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