DBMS > Derby vs. Google Cloud Bigtable vs. Hyprcubd vs. SWC-DB

System Properties Comparison Derby vs. Google Cloud Bigtable vs. Hyprcubd vs. SWC-DB

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Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

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

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

Serverless Time Series DBMS

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Key-value store
Wide column store

Time Series DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

db.apache.org/derby

cloud.google.com/bigtable

hyprcubd.com (offline)

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

db.apache.org/derby/manuals/index.html

cloud.google.com/bigtable/docs

Developer

Apache Software Foundation

Google

Hyprcubd, Inc.

Alex Kashirin

Initial release

1997

2015

2020

Current release

10.17.1.0, November 2023

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

GPL V3

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

Java

C++

Server operating systems

All OS with a Java VM

hosted

Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

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

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

JDBC

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

gRPC (https)

Proprietary protocol
Thrift

Supported programming languages

Java

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

C++

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

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

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)

Eventual Consistency

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

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)

token access

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