DBMS > GridGain vs. Hive vs. Microsoft SQL Server vs. RethinkDB

System Properties Comparison GridGain vs. Hive vs. Microsoft SQL Server vs. RethinkDB

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Editorial information provided by DB-Engines

Name

Description

GridGain is an in-memory computing platform, built on Apache Ignite

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

Microsofts flagship relational DBMS

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Document store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

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

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Website

www.gridgain.com

hive.apache.org

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

rethinkdb.com

Technical documentation

www.gridgain.com/docs/index.html

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

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

rethinkdb.com/docs

Developer

GridGain Systems, Inc.

Apache Software Foundation

initially developed by Facebook

Microsoft

The Linux Foundation

since July 2017

Initial release

2007

2012

1989

2009

Current release

GridGain 8.5.1

3.1.3, April 2022

SQL Server 2022, November 2022

2.4.1, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

Java

C++

Server operating systems

Linux
OS X
Solaris
Windows

All OS with a Java VM

Linux
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

ANSI-99 for query and DML statements, subset of DDL

SQL-like DML and DDL statements

yes

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC
Thrift

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

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C++
Java
PHP
Python

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

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

yes

user defined functions and integration of map-reduce

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

Triggers

yes (cache interceptors and events)

yes

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

range based

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

selectable replication factor

yes, but depending on the SQL-Server Edition

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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

Security Hooks for custom implementations

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

yes

users and table-level permissions

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