DBMS > Apache IoTDB vs. Axibase vs. MySQL vs. searchxml vs. Spark SQL

System Properties Comparison Apache IoTDB vs. Axibase vs. MySQL vs. searchxml vs. Spark SQL

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 HBase with integrated rule engine and visualization

Widely used open source RDBMS

DBMS for structured and unstructured content wrapped with an application server

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Native XML DBMS
Search engine

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

iotdb.apache.org

axibase.com/docs/atsd/finance

www.mysql.com

www.searchxml.net/category/products

spark.apache.org/sql

Technical documentation

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

dev.mysql.com/doc

www.searchxml.net/support/handouts

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Apache Software Foundation

Axibase Corporation

Oracle

since 2010, originally MySQL AB, then Sun

informationpartners gmbh

Apache Software Foundation

Initial release

2018

2013

1995

2015

2014

Current release

1.1.0, April 2023

15585

8.4.0, April 2024

1.0

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C and C++

C++

Scala

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

JDBC
Native API

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

RESTful HTTP API
WebDAV
XQuery
XSLT

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Go
Java
PHP
Python
R
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C++

most other programming languages supported via APIs

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

proprietary syntax

yes

on the application server

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

yes, utilizing Spark Core

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

Source-replica replication

Multi-source replication
Source-replica replication

yes

sychronisation to multiple collections

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

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

User concepts

Access control

yes

Users with fine-grained authorization concept

no user groups or roles

Domain, group and role-based access control at the document level and for application services

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