DBMS > Amazon DynamoDB vs. GigaSpaces vs. Sphinx vs. Teradata

System Properties Comparison Amazon DynamoDB vs. GigaSpaces vs. Sphinx vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

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

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Document store
Key-value store

Document store
Object oriented DBMS

Values are user defined objects

Search engine

Relational DBMS

Secondary database models

Graph DBMS
Search engine

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

aws.amazon.com/dynamodb

www.gigaspaces.com

sphinxsearch.com

www.teradata.com

Technical documentation

docs.aws.amazon.com/dynamodb

docs.gigaspaces.com/latest/landing.html

sphinxsearch.com/docs

docs.teradata.com

Developer

Amazon

Gigaspaces Technologies

Sphinx Technologies Inc.

Teradata

Initial release

2012

2000

2001

1984

Current release

15.5, September 2020

3.5.1, February 2023

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache Version 2; Commercial licenses available

Open Source

GPL version 2, commercial licence available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java, C++, .Net

C++

Server operating systems

hosted

Linux
macOS
Solaris
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

hosted
Linux

Data scheme

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.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

full-text index on all search fields

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL-99 for query and DML statements

SQL-like query language (SphinxQL)

yes

SQL 2016 + extensions

APIs and other access methods

RESTful HTTP API

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

Proprietary protocol

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
C++
Java
Python
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

by integration with AWS Lambda

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

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

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)

Role-based access control

fine grained access rights according to SQL-standard

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