DBMS > Hive vs. Microsoft SQL Server vs. RDF4J vs. TimescaleDB vs. YottaDB

System Properties Comparison Hive vs. Microsoft SQL Server vs. RDF4J vs. TimescaleDB vs. YottaDB

Editorial information provided by DB-Engines

Name

Description

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

Microsofts flagship relational DBMS

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

A fast and solid embedded Key-value store

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

using the Octo plugin

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

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Score	0.20
Rank	#317	Overall
	#47	Key-value stores

Website

hive.apache.org

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

rdf4j.org

www.timescale.com

yottadb.com

Technical documentation

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

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

rdf4j.org/documentation

docs.timescale.com

yottadb.com/resources/documentation

Developer

Apache Software Foundation

initially developed by Facebook

Microsoft

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Timescale

YottaDB, LLC

Initial release

2012

1989

2004

2017

2001

Current release

3.1.3, April 2022

SQL Server 2022, November 2022

2.15.0, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

Open Source

AGPL 3.0

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Java

Server operating systems

All OS with a Java VM

Linux
Windows

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Docker
Linux

Data scheme

yes

RDF Schemas

yes

schema-free

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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 DML and DDL statements

yes

full PostgreSQL SQL syntax

by using the Octo plugin

APIs and other access methods

JDBC
ODBC
Thrift

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

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

C++
Java
PHP
Python

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

Java
PHP
Python

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

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

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

none

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes, but depending on the SQL-Server Edition

none

Source-replica replication with hot standby and reads on replicas

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

Users and groups based on OS-security mechanisms

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