DBMS > EsgynDB vs. Google Cloud Bigtable vs. Hive vs. HugeGraph vs. ObjectBox

System Properties Comparison EsgynDB vs. Google Cloud Bigtable vs. Hive vs. HugeGraph vs. ObjectBox

Editorial information provided by DB-Engines

Name

Description

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

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

A fast-speed and highly-scalable Graph DBMS

Extremely fast embedded database for small devices, IoT and Mobile

Primary database model

Relational DBMS

Key-value store
Wide column store

Relational DBMS

Graph DBMS

Object oriented DBMS

Secondary database models

Time Series DBMS

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

Score	0.23
Rank	#319	Overall
	#141	Relational DBMS

Score	3.58
Rank	#92	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	62.59
Rank	#18	Overall
	#12	Relational DBMS

Score	0.15
Rank	#339	Overall
	#33	Graph DBMS

Score	1.22
Rank	#172	Overall
	#5	Object oriented DBMS

Website

www.esgyn.cn

cloud.google.com/bigtable

hive.apache.org

github.com/hugegraph
hugegraph.apache.org

objectbox.io

Technical documentation

cloud.google.com/bigtable/docs

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

hugegraph.apache.org/docs

docs.objectbox.io

Developer

Esgyn

Google

Apache Software Foundation

initially developed by Facebook

Baidu

ObjectBox Limited

Initial release

2015

2012

2018

2017

Current release

3.1.3, April 2022

0.9

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

Apache License 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 and C++

Server operating systems

Linux

hosted

All OS with a Java VM

Linux
macOS
Unix

Android
iOS
Linux
macOS
Windows

Data scheme

yes

schema-free

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.

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
ODBC
Thrift

Java API
RESTful HTTP API
TinkerPop Gremlin

Proprietary native API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

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

C++
Java
PHP
Python

Groovy
Java
Python

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Internal replication in Colossus, and regional replication between two clusters in different zones

selectable replication factor

yes

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

online/offline synchronization between client and server

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

Atomic single-row operations

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 rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights for users, groups and roles

Users, roles and permissions

yes

More information provided by the system vendor

News

Edge AI: The era of on-device AI
23 April 2024

In-Memory Database Use Cases
15 February 2024

Data Viewer for Objects – announcing ObjectBox Admin
14 November 2023

Vector Databases for Edge AI
9 August 2023

Vector Database Release for Flutter / Dart + Python
13 June 2023

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

DB-Engines blog posts

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