DBMS > Apache IoTDB vs. Cachelot.io vs. IBM Db2 Event Store vs. SWC-DB

System Properties Comparison Apache IoTDB vs. Cachelot.io vs. IBM Db2 Event Store vs. SWC-DB

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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

In-memory caching system

Distributed Event Store optimized for Internet of Things use cases

A high performance, scalable Wide Column DBMS

Primary database model

Time Series DBMS

Key-value store

Event Store
Time Series DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.04
Rank	#388	Overall
	#62	Key-value stores

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

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

iotdb.apache.org

cachelot.io

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

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

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

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

Developer

Apache Software Foundation

IBM

Alex Kashirin

Initial release

2018

2015

2017

2020

Current release

1.1.0, April 2023

2.0

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Simplified BSD License

commercial

free developer edition available

Open Source

GPL V3

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++

C and C++

C++

Server operating systems

All OS with a Java VM (>= 1.8)

FreeBSD
Linux
OS X

Linux

Linux, macOS, Windows for the developer addition

Linux

Data scheme

yes

schema-free

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

yes

SQL

Support of SQL

SQL-like query language

yes

through the embedded Spark runtime

SQL-like query language

APIs and other access methods

JDBC
Native API

Memcached protocol

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Proprietary protocol
Thrift

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

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

C++

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)

none

Sharding

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

none

Active-active shard replication

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

none

Eventual Consistency

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

User concepts

Access control

yes

fine grained access rights according to SQL-standard

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