DBMS > Amazon SimpleDB vs. GridGain vs. Microsoft SQL Server vs. Sphinx vs. XTDB

System Properties Comparison Amazon SimpleDB vs. GridGain vs. Microsoft SQL Server vs. Sphinx vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

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

Microsofts flagship relational DBMS

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Key-value store

Key-value store
Relational DBMS

Relational DBMS

Search engine

Document store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.88
Rank	#133	Overall
	#23	Key-value stores

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

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

aws.amazon.com/simpledb

www.gridgain.com

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

sphinxsearch.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.aws.amazon.com/simpledb

www.gridgain.com/docs/index.html

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

sphinxsearch.com/docs

www.xtdb.com/docs

Developer

Amazon

GridGain Systems, Inc.

Microsoft

Sphinx Technologies Inc.

Juxt Ltd.

Initial release

2007

1989

2001

2019

Current release

GridGain 8.5.1

SQL Server 2022, November 2022

3.5.1, February 2023

1.19, September 2021

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

GPL version 2, commercial licence available

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java, C++, .Net

C++

Clojure

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

All columns are indexed automatically

yes

full-text index on all search fields

yes

SQL

Support of SQL

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

yes

SQL-like query language (SphinxQL)

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP API

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

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

Proprietary protocol

HTTP REST
JDBC

Supported programming languages

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

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

Clojure
Java

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

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Sharding

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

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes (replicated cache)

yes, but depending on the SQL-Server Edition

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Concurrent data updates can be detected by the application

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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 and roles can be defined via the AWS Identity and Access Management (IAM)

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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