DBMS > Apache IoTDB vs. Ignite vs. Microsoft SQL Server vs. Quasardb vs. TerminusDB

System Properties Comparison Apache IoTDB vs. Ignite vs. Microsoft SQL Server vs. Quasardb vs. TerminusDB

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

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Microsofts flagship relational DBMS

Distributed, high-performance timeseries database

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Graph DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
RDF store

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	3.11
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Website

iotdb.apache.org

ignite.apache.org

www.microsoft.com/en-us/sql-server

quasar.ai

terminusdb.com

Technical documentation

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

apacheignite.readme.io/docs

learn.microsoft.com/en-US/sql/sql-server

doc.quasar.ai/master

terminusdb.github.io/terminusdb/#

Developer

Apache Software Foundation

Microsoft

quasardb

DataChemist Ltd.

Initial release

2018

2015

1989

2009

2018

Current release

1.1.0, April 2023

Apache Ignite 2.6

SQL Server 2022, November 2022

3.14.1, January 2024

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

restricted free version is available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++, Java, .Net

C++

Prolog, Rust

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X
Solaris
Windows

Linux
Windows

BSD
Linux
OS X
Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

SQL

Support of SQL

SQL-like query language

ANSI-99 for query and DML statements, subset of DDL

yes

SQL-like query language

SQL-like query language (WOQL)

APIs and other access methods

JDBC
Native API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

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

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

JavaScript
Python

Server-side scripts

Stored procedures

yes

yes (compute grid and cache interceptors can be used instead)

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

consistent hashing

Graph 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 (replicated cache)

yes, but depending on the SQL-Server Edition

Source-replica replication with selectable replication factor

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes (compute grid and hadoop accelerator)

with Hadoop integration

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

by using LevelDB

yes

in-memory journaling

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

yes

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

Role-based access control

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