DBMS > Amazon Redshift vs. GridGain vs. Sphinx vs. Yanza

System Properties Comparison Amazon Redshift vs. GridGain vs. Sphinx vs. Yanza

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Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Large scale data warehouse service for use with business intelligence tools

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

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

Time Series DBMS for IoT Applications

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Search engine

Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

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

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

aws.amazon.com/redshift

www.gridgain.com

sphinxsearch.com

yanza.com

Technical documentation

docs.aws.amazon.com/redshift

www.gridgain.com/docs/index.html

sphinxsearch.com/docs

Developer

Amazon (based on PostgreSQL)

GridGain Systems, Inc.

Sphinx Technologies Inc.

Yanza

Initial release

2012

2007

2001

2015

Current release

GridGain 8.5.1

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

GPL version 2, commercial licence available

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

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
OS X
Solaris
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Windows

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

Secondary indexes

restricted

yes

full-text index on all search fields

SQL

Support of SQL

yes

does not fully support an SQL-standard

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

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

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

Proprietary protocol

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Java
PHP
Python
Ruby
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

any language that supports HTTP calls

Server-side scripts

Stored procedures

user defined functions

in Python

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

Triggers

yes (cache interceptors and events)

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

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)

none

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

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

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

fine grained access rights according to SQL-standard

Security Hooks for custom implementations

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