DBMS > BigObject vs. EsgynDB vs. Google Cloud Bigtable vs. H2 vs. HyperSQL

System Properties Comparison BigObject vs. EsgynDB vs. Google Cloud Bigtable vs. H2 vs. HyperSQL

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

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.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Website

bigobject.io

www.esgyn.cn

cloud.google.com/bigtable

www.h2database.com

hsqldb.org

Technical documentation

docs.bigobject.io

cloud.google.com/bigtable/docs

www.h2database.com/html/main.html

hsqldb.org/web/hsqlDocsFrame.html

Developer

BigObject, Inc.

Esgyn

Google

Thomas Mueller

Initial release

2015

2005

2001

Current release

2.2.220, July 2023

2.7.2, June 2023

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

based on BSD license

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

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux

hosted

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

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

JDBC
ODBC

HTTP API

JDBC via HTTP
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

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

Java

All languages supporting JDBC/ODBC
Java

Server-side scripts

Stored procedures

Lua

Java Stored Procedures

Java Stored Procedures and User-Defined Functions

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication between multi datacenters

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

With clustering: 2 database servers on different computers operate on identical copies of a database

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

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

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

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

Read/write lock on objects (tables, trees)

yes

yes, multi-version concurrency control (MVCC)

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)

fine grained access rights according to SQL-standard

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