DBMS > EsgynDB vs. Google BigQuery vs. GridGain vs. Hive vs. HugeGraph

System Properties Comparison EsgynDB vs. Google BigQuery vs. GridGain vs. Hive vs. HugeGraph

Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service with append-only tables

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

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

A fast-speed and highly-scalable Graph DBMS

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Graph DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

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

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Website

www.esgyn.cn

cloud.google.com/bigquery

www.gridgain.com

hive.apache.org

github.com/hugegraph
hugegraph.apache.org

Technical documentation

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

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

hugegraph.apache.org/docs

Developer

Esgyn

Google

GridGain Systems, Inc.

Apache Software Foundation

initially developed by Facebook

Baidu

Initial release

2015

2010

2007

2012

2018

Current release

GridGain 8.5.1

3.1.3, April 2022

0.9

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

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

Server operating systems

Linux

hosted

Linux
OS X
Solaris
Windows

All OS with a Java VM

Linux
macOS
Unix

Data scheme

yes

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

yes

also supports composite index and range index

SQL

Support of SQL

yes

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

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP/JSON API

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

JDBC
ODBC
Thrift

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

C++
Java
PHP
Python

Groovy
Java
Python

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

in JavaScript

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

yes

user defined functions and integration of map-reduce

asynchronous Gremlin script jobs

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

yes

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

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes (replicated cache)

selectable replication factor

yes

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

yes

query execution via MapReduce

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Security Hooks for custom implementations

Access rights for users, groups and roles

Users, roles and permissions

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