DBMS > Apache IoTDB vs. Hive vs. Microsoft SQL Server vs. RavenDB vs. SAP SQL Anywhere

System Properties Comparison Apache IoTDB vs. Hive vs. Microsoft SQL Server vs. RavenDB 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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Microsofts flagship relational DBMS

Open Source Operational and Transactional Enterprise NoSQL Document Database

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

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Website

iotdb.apache.org

hive.apache.org

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

ravendb.net

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

Technical documentation

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

cwiki.apache.org/confluence/display/Hive/Home

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

ravendb.net/docs

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Apache Software Foundation

initially developed by Facebook

Microsoft

Hibernating Rhinos

SAP

formerly Sybase

Initial release

2018

2012

1989

2010

1992

Current release

1.1.0, April 2023

3.1.3, April 2022

SQL Server 2022, November 2022

5.4, July 2022

17, July 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

AGPL version 3, commercial license available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

All OS with a Java VM

Linux
Windows

Linux
macOS
Raspberry Pi
Windows

AIX
HP-UX
Linux
OS X
Solaris
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

SQL-like DML and DDL statements

yes

SQL-like query language (RQL)

yes

APIs and other access methods

JDBC
Native API

JDBC
ODBC
Thrift

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

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C++
Java
PHP
Python

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

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

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

yes

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

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

Sharding

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

selectable replication factor

yes, but depending on the SQL-Server Edition

Multi-source replication

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

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

fine grained access rights according to SQL-standard

Authorization levels configured per client per database

fine grained access rights according to SQL-standard

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