DBMS > Apache IoTDB vs. DuckDB vs. Lovefield vs. Microsoft SQL Server vs. SAP SQL Anywhere

System Properties Comparison Apache IoTDB vs. DuckDB vs. Lovefield vs. Microsoft SQL Server vs. SAP SQL Anywhere

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

An embeddable, in-process, column-oriented SQL OLAP RDBMS

Embeddable relational database for web apps written in pure JavaScript

Microsofts flagship relational DBMS

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Time Series DBMS

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	#164	Overall
	#14	Time Series DBMS

Score	4.63
Rank	#69	Overall
	#37	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Website

iotdb.apache.org

duckdb.org

google.github.io/lovefield

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

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

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

duckdb.org/docs

github.com/google/lovefield/blob/master/docs/spec_index.md

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

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Apache Software Foundation

Google

Microsoft

SAP

formerly Sybase

Initial release

2018

2014

1989

1992

Current release

1.1.0, April 2023

1.0.0, June 2024

2.1.12, February 2017

SQL Server 2022, November 2022

17, July 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

MIT License

Open Source

Apache 2.0

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

JavaScript

C++

Server operating systems

All OS with a Java VM (>= 1.8)

server-less

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

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

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

JDBC
Native API

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

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

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

JavaScript

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

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

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

yes, in C/C++, Java, .Net or Perl

Triggers

yes

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

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

none

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

none

yes, but depending on the SQL-Server Edition

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

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

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

yes

fine grained access rights according to SQL-standard

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