DBMS > Apache IoTDB vs. HBase vs. JSqlDb vs. Microsoft SQL Server vs. SWC-DB

System Properties Comparison Apache IoTDB vs. HBase vs. JSqlDb vs. Microsoft SQL Server vs. SWC-DB

Editorial information provided by DB-Engines

Name

JSqlDB seems to be discontinued. Therefore it is excluded 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

Wide-column store based on Apache Hadoop and on concepts of BigTable

JavaScript Query Language Database, stores JavaScript objects and primitives

Microsofts flagship relational DBMS

A high performance, scalable Wide Column DBMS

Primary database model

Time Series DBMS

Wide column store

Document store
Object oriented DBMS

Relational DBMS

Wide column store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Time Series DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

iotdb.apache.org

hbase.apache.org

jsqldb.org (offline)

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

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

Technical documentation

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

hbase.apache.org/book.html

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Konrad von Backstrom

Microsoft

Alex Kashirin

Initial release

2018

2008

2018

1989

2020

Current release

1.1.0, April 2023

2.3.4, January 2021

0.8, December 2018

SQL Server 2022, November 2022

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache version 2

Open Source

commercial

restricted free version is available

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Unix
Windows

using Cygwin

Linux
macOS
Windows

Linux
Windows

Linux

Data scheme

yes

schema-free, schema definition possible

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

SQL-like query language

APIs and other access methods

JDBC
Native API

Java API
RESTful HTTP API
Thrift

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

Proprietary protocol
Thrift

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

JavaScript

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

C++

Server-side scripts

Stored procedures

yes

Coprocessors in Java

functions in JavaScript

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

none

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

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

Multi-source replication
Source-replica replication

none

yes, but depending on the SQL-Server Edition

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 or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Single row ACID (across millions of columns)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using RocksDB

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

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