DBMS > EsgynDB vs. Google Cloud Bigtable vs. H2 vs. HyperSQL vs. Machbase Neo

System Properties Comparison EsgynDB vs. Google Cloud Bigtable vs. H2 vs. HyperSQL vs. Machbase Neo

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.

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

TimeSeries DBMS for AIoT and BigData

Primary database model

Relational DBMS

Key-value store
Wide column store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

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

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Website

www.esgyn.cn

cloud.google.com/bigtable

www.h2database.com

hsqldb.org

machbase.com

Technical documentation

cloud.google.com/bigtable/docs

www.h2database.com/html/main.html

hsqldb.org/web/hsqlDocsFrame.html

machbase.com/dbms

Developer

Esgyn

Google

Thomas Mueller

Machbase

Initial release

2015

2005

2001

2013

Current release

2.2.220, July 2023

2.7.2, June 2023

V8.0, August 2023

License

Commercial or Open Source

commercial

Open Source

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

Open Source

based on BSD license

commercial

free test version available

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

hosted

All OS with a Java VM

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

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

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

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

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
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

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

Java Stored Procedures and User-Defined Functions

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

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

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

none

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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)

Immediate Consistency

Foreign keys

Referential integrity

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

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

volatile and lookup table

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

simple password-based access control

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