DBMS > GridGain vs. Microsoft SQL Server vs. Postgres-XL vs. Riak TS

System Properties Comparison GridGain vs. Microsoft SQL Server vs. Postgres-XL vs. Riak TS

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Editorial information provided by DB-Engines

Name

Description

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

Microsofts flagship relational DBMS

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

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

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Website

www.gridgain.com

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

www.postgres-xl.org

Technical documentation

www.gridgain.com/docs/index.html

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

www.postgres-xl.org/documentation

www.tiot.jp/riak-docs/riak/ts/latest

Developer

GridGain Systems, Inc.

Microsoft

Open Source, formerly Basho Technologies

Initial release

2007

1989

2014

since 2012, originally named StormDB

2015

Current release

GridGain 8.5.1

SQL Server 2022, November 2022

10 R1, October 2018

3.0.0, September 2022

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Mozilla public license

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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Implementation language

Java, C++, .Net

C++

Erlang

Server operating systems

Linux
OS X
Solaris
Windows

Linux
Windows

Linux
macOS

Linux
OS X

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

XML type, but no XML query functionality

Secondary indexes

yes

restricted

SQL

Support of SQL

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

yes

distributed, parallel query execution

yes, limited

APIs and other access methods

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

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

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

HTTP API
Native Erlang Interface

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

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

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

user defined functions

Erlang

Triggers

yes (cache interceptors and events)

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes, but depending on the SQL-Server Edition

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

Transaction concepts

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

ACID

MVCC

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

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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