DBMS > Apache IoTDB vs. Blueflood vs. Tkrzw

System Properties Comparison Apache IoTDB vs. Blueflood vs. Tkrzw

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Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Scalable TimeSeries 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

Key-value store

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

Score	1.19
Rank	#176	Overall
	#15	Time Series DBMS

Score	0.09
Rank	#353	Overall
	#33	Time Series DBMS

Score	0.09
Rank	#354	Overall
	#51	Key-value stores

Website

iotdb.apache.org

blueflood.io

dbmx.net/tkrzw

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

github.com/rax-maas/blueflood/wiki

Developer

Apache Software Foundation

Rackspace

Mikio Hirabayashi

Initial release

2018

2013

2020

Current release

1.1.0, April 2023

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

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

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Implementation language

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X

Linux
macOS

Data scheme

yes

predefined scheme

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

yes

SQL

Support of SQL

SQL-like query language

APIs and other access methods

JDBC
Native API

HTTP REST

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

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

Durability

Support for making data persistent

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

yes

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