DBMS > Apache IoTDB vs. Microsoft Access vs. Microsoft SQL Server vs. Sphinx vs. SurrealDB

System Properties Comparison Apache IoTDB vs. Microsoft Access vs. Microsoft SQL Server vs. Sphinx vs. SurrealDB

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

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

Open source search engine for searching in data from different sources, e.g. relational databases

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Time Series DBMS

Relational DBMS

Search engine

Document store
Graph 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	101.16
Rank	#11	Overall
	#8	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	1.02
Rank	#190	Overall
	#33	Document stores
	#18	Graph DBMS

Website

iotdb.apache.org

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

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

sphinxsearch.com

surrealdb.com

Technical documentation

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

developer.microsoft.com/en-us/access

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

sphinxsearch.com/docs

surrealdb.com/docs

Developer

Apache Software Foundation

Microsoft

Sphinx Technologies Inc.

SurrealDB Ltd

Initial release

2018

1992

1989

2001

2022

Current release

1.1.0, April 2023

1902 (16.0.11328.20222), March 2019

SQL Server 2022, November 2022

3.5.1, February 2023

v1.5.0, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Bundled with Microsoft Office

commercial

restricted free version is available

Open Source

GPL version 2, commercial licence available

Open Source

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)

Windows

Not a real database server, but making use of DLLs

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
macOS
Windows

Data scheme

yes

schema-free

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

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

yes

but not compliant to any SQL standard

yes

SQL-like query language (SphinxQL)

SQL-like query language

APIs and other access methods

JDBC
Native API

ADO.NET
DAO
ODBC
OLE DB

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

Proprietary protocol

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

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

none

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

Sharding

Partitioning is done manually, search queries against distributed index is supported

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

none

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

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

The original contents of fields are not stored in the Sphinx index.

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

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

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

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