DBMS > EsgynDB vs. HugeGraph vs. NSDb vs. Solr vs. WakandaDB

System Properties Comparison EsgynDB vs. HugeGraph vs. NSDb vs. Solr vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

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

A fast-speed and highly-scalable Graph DBMS

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

A widely used distributed, scalable search engine based on Apache Lucene

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Secondary database models

Spatial DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

www.esgyn.cn

github.com/hugegraph
hugegraph.apache.org

nsdb.io

solr.apache.org

wakanda.github.io

Technical documentation

hugegraph.apache.org/docs

nsdb.io/Architecture

solr.apache.org/resources.html

wakanda.github.io/doc

Developer

Esgyn

Baidu

Apache Software Foundation

Wakanda SAS

Initial release

2015

2018

2017

2006

2012

Current release

0.9

9.6.0, April 2024

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

Apache Version 2

Open Source

AGPLv3, extended commercial license available

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

Java, Scala

Java

C++, JavaScript

Server operating systems

Linux

Linux
macOS
Unix

Linux
macOS

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux
OS X
Windows

Data scheme

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

yes

supports customizable data types and automatic typing

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

also supports composite index and range index

all fields are automatically indexed

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

Solr Parallel SQL Interface

APIs and other access methods

ADO.NET
JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

gRPC
HTTP REST
WebSocket

Java API
RESTful HTTP/JSON API

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

Groovy
Java
Python

Java
Scala

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

JavaScript

Server-side scripts

Stored procedures

Java Stored Procedures

asynchronous Gremlin script jobs

Java plugins

yes

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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

Users, roles and permissions

yes

More information provided by the system vendor

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