DBMS > Altibase vs. Apache IoTDB vs. GeoMesa vs. Yanza

System Properties Comparison Altibase vs. Apache IoTDB vs. GeoMesa vs. Yanza

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Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

An enterprise grade, high-performance RDBMS

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

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Time Series DBMS for IoT Applications

Primary database model

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

Score	1.02
Rank	#189	Overall
	#88	Relational DBMS

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Website

altibase.com

iotdb.apache.org

www.geomesa.org

yanza.com

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

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

www.geomesa.org/documentation/stable/user/index.html

Developer

Altibase

Apache Software Foundation

CCRi and others

Yanza

Initial release

1999

2018

2014

2015

Current release

v7.3, 2023, August 2023

1.1.0, April 2023

5.0.0, May 2024

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

Open Source

Apache Version 2.0

Open Source

Apache License 2.0

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

Scala

Server operating systems

AIX
HP-UX
Linux

All OS with a Java VM (>= 1.8)

Windows

Data 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

yes

SQL

Support of SQL

ANSI SQL-92

SQL-like query language

APIs and other access methods

JDBC
ODBC
Proprietary protocol

JDBC
Native API

HTTP API

Supported programming languages

C
C++
Java

C
C#
C++
Go
Java
Python
Scala

any language that supports HTTP calls

Server-side scripts

Stored procedures

stored procedures and stored functions

yes

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

depending on storage layer

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

depending on storage layer

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

depending on storage layer

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

depending on storage layer

User concepts

Access control

fine grained access rights according to SQL-standard

yes

depending on the DBMS used for storage

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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