DBMS > EsgynDB vs. Hive vs. HugeGraph vs. Quasardb

System Properties Comparison EsgynDB vs. Hive vs. HugeGraph vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A fast-speed and highly-scalable Graph DBMS

Distributed, high-performance timeseries database

Primary database model

Relational DBMS

Graph DBMS

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	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

www.esgyn.cn

hive.apache.org

github.com/hugegraph
hugegraph.apache.org

quasar.ai

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

hugegraph.apache.org/docs

doc.quasar.ai/master

Developer

Esgyn

Apache Software Foundation

initially developed by Facebook

Baidu

quasardb

Initial release

2015

2012

2018

2009

Current release

3.1.3, April 2022

0.9

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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++

Server operating systems

Linux

All OS with a Java VM

Linux
macOS
Unix

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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.

Secondary indexes

yes

also supports composite index and range index

yes

with tags

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC
Thrift

Java API
RESTful HTTP API
TinkerPop Gremlin

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C++
Java
PHP
Python

Groovy
Java
Python

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

Java Stored Procedures

yes

user defined functions and integration of map-reduce

asynchronous Gremlin script jobs

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

selectable replication factor

yes

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

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

via hugegraph-spark

with Hadoop integration

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

Users, roles and permissions

Cryptographically strong user authentication and audit trail

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