DBMS > GridGain vs. Sphinx vs. STSdb vs. TerminusDB

System Properties Comparison GridGain vs. Sphinx vs. STSdb vs. TerminusDB

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Name

Description

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Key-value store
Relational DBMS

Search engine

Key-value store

Graph DBMS

Secondary database models

Document store
RDF store

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

Score	1.92
Rank	#126	Overall
	#25	Key-value stores
	#62	Relational DBMS

Score	7.23
Rank	#61	Overall
	#8	Search engines

Score	0.01
Rank	#359	Overall
	#56	Key-value stores

Score	0.12
Rank	#309	Overall
	#28	Graph DBMS

Website

www.gridgain.com

sphinxsearch.com

github.com/STSSoft/STSdb4

terminusdb.com

Technical documentation

www.gridgain.com/docs/latest

sphinxsearch.com/docs

terminusdb.github.io/terminusdb/#

Developer

GridGain Systems, Inc.

Sphinx Technologies Inc.

STS Soft SC

DataChemist Ltd.

Initial release

2007

2001

2011

2018

Current release

GridGain 8.5.1

3.4.1, July 2021

4.0.8, September 2015

10.0.21, March 2022

License

Commercial or Open Source

commercial

Open Source

GPL version 2, commercial licence available

Open Source

GPLv2, commercial license available

Open Source

GPL V3

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

C++

Prolog, Rust

Server operating systems

Linux
OS X
Solaris
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Windows

Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

yes

full-text index on all search fields

SQL

Support of SQL

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

SQL-like query language (SphinxQL)

SQL-like query language (WOQL)

APIs and other access methods

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

Proprietary protocol

.NET Client API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
Java

JavaScript
Python

Server-side scripts

Stored procedures

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

yes

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

none

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

none

Journaling Streams

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

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 journaling

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

Role-based access control

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