DBMS > Apache IoTDB vs. MarkLogic vs. Microsoft Access vs. Microsoft SQL Server vs. RisingWave

System Properties Comparison Apache IoTDB vs. MarkLogic vs. Microsoft Access vs. Microsoft SQL Server vs. RisingWave

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

Operational and transactional Enterprise NoSQL database

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Microsofts flagship relational DBMS

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

Primary database model

Time Series DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

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	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.64
Rank	#238	Overall
	#110	Relational DBMS

Website

iotdb.apache.org

www.marklogic.com

www.microsoft.com/en-us/microsoft-365/access

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

www.risingwave.com/database

Technical documentation

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

docs.marklogic.com

developer.microsoft.com/en-us/access

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

docs.risingwave.com/docs/current/intro

Developer

Apache Software Foundation

MarkLogic Corp.

Microsoft

RisingWave Labs

Initial release

2018

2001

1992

1989

2022

Current release

1.1.0, April 2023

11.0, December 2022

1902 (16.0.11328.20222), March 2019

SQL Server 2022, November 2022

1.2, September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

restricted free version is available

commercial

Bundled with Microsoft Office

commercial

restricted free version is available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Rust

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X
Windows

Windows

Not a real database server, but making use of DLLs

Linux
Windows

Docker
Linux
macOS

Data scheme

yes

schema-free

Schema can be enforced

yes

Typing

predefined data types such as float or date

yes

Standard SQL-types and JSON

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

SQL92

yes

but not compliant to any SQL standard

yes

APIs and other access methods

JDBC
Native API

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

ADO.NET
DAO
ODBC
OLE DB

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

JDBC
PostgreSQL wire protocol

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

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

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

yes

since Access 2010 using the ACE-engine

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

UDFs in Python or Java

Triggers

yes

since Access 2010 using the ACE-engine

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

yes

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

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

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

can act as a resource manager in an XA/JTA transaction

ACID

but no files for transaction logging

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

In-memory capabilities

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

yes

yes, with Range Indexes

yes

User concepts

Access control

yes

Role-based access control at the document and subdocument levels

a simple user-level security was built in till version Access 2003

fine grained access rights according to SQL-standard

Users and Roles

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