DBMS > Apache IoTDB vs. Google Cloud Bigtable vs. HyperSQL vs. Microsoft SQL Server

System Properties Comparison Apache IoTDB vs. Google Cloud Bigtable vs. HyperSQL vs. Microsoft SQL Server

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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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Microsofts flagship relational DBMS

Primary database model

Time Series DBMS

Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	3.14
Rank	#85	Overall
	#46	Relational DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Website

iotdb.apache.org

cloud.google.com/bigtable

hsqldb.org

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

Technical documentation

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

cloud.google.com/bigtable/docs

hsqldb.org/web/hsqlDocsFrame.html

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

Developer

Apache Software Foundation

Google

Microsoft

Initial release

2018

2015

2001

1989

Current release

1.1.0, April 2023

2.7.2, June 2023

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

based on BSD license

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux
Windows

Data scheme

yes

schema-free

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

HTTP API

JDBC via HTTP
JDBC
ODBC

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

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

All languages supporting JDBC/ODBC
Java

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

Server-side scripts

Stored procedures

yes

Java, SQL

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

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

Internal replication in Colossus, and regional replication between two clusters in different zones

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 (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

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

User concepts

Access control

yes

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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