DBMS > Apache IoTDB vs. SiriDB vs. SQream DB vs. Tkrzw vs. Yaacomo

System Properties Comparison Apache IoTDB vs. SiriDB vs. SQream DB vs. Tkrzw vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

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

Open Source Time Series DBMS

a GPU-based, columnar RDBMS for big data analytics workloads

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

yaacomo.com

Technical documentation

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

docs.siridb.com

docs.sqream.com

Developer

Apache Software Foundation

Cesbit

SQream Technologies

Mikio Hirabayashi

Q2WEB GmbH

Initial release

2018

2017

2020

2009

Current release

1.1.0, April 2023

2022.1.6, December 2022

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

MIT License

commercial

Open Source

Apache Version 2.0

commercial

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++, CUDA, Haskell, Java, Scala

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

Linux
macOS

Android
Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data

yes, ANSI Standard SQL Types

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

APIs and other access methods

JDBC
Native API

HTTP API

.Net
JDBC
ODBC

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

C++
Java
JavaScript (Node.js)
Python

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

horizontal and vertical partitioning

none

horizontal partitioning

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

yes

none

Source-replica 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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

yes

User concepts

Access control

yes

simple rights management via user accounts

fine grained access rights according to SQL-standard

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