DBMS > EsgynDB vs. GridGain vs. NSDb vs. SWC-DB vs. TinkerGraph

System Properties Comparison EsgynDB vs. GridGain vs. NSDb vs. SWC-DB vs. TinkerGraph

Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

A high performance, scalable Wide Column DBMS

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

Wide column store

Graph DBMS

Secondary database models

Time Series DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Score	0.08
Rank	#348	Overall
	#35	Graph DBMS

Website

www.esgyn.cn

www.gridgain.com

nsdb.io

github.com/kashirin-alex/swc-db
www.swcdb.org

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

www.gridgain.com/docs/index.html

nsdb.io/Architecture

Developer

Esgyn

GridGain Systems, Inc.

Alex Kashirin

Initial release

2015

2007

2017

2020

2009

Current release

GridGain 8.5.1

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GPL V3

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++, Java

Java, C++, .Net

Java, Scala

C++

Java

Server operating systems

Linux

Linux
OS X
Solaris
Windows

Linux
macOS

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

SQL

Support of SQL

yes

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

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
ODBC

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

gRPC
HTTP REST
WebSocket

Proprietary protocol
Thrift

TinkerPop 3

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C#
C++
Java
PHP
Python
Ruby
Scala

Java
Scala

C++

Groovy
Java

Server-side scripts

Stored procedures

Java Stored Procedures

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

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes (replicated cache)

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

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

Using Apache Lucene

yes

optional

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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