DBMS > Blueflood vs. Google Cloud Bigtable vs. H2GIS vs. Oracle NoSQL

System Properties Comparison Blueflood vs. Google Cloud Bigtable vs. H2GIS vs. Oracle NoSQL

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

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

Spatial extension of H2

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Primary database model

Time Series DBMS

Key-value store
Wide column store

Spatial DBMS

Document store
Key-value store
Relational DBMS

Secondary database models

Relational DBMS

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

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

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

Score	0.08
Rank	#368	Overall
	#7	Spatial DBMS

Score	3.05
Rank	#97	Overall
	#17	Document stores
	#16	Key-value stores
	#50	Relational DBMS

Website

blueflood.io

cloud.google.com/bigtable

www.h2gis.org

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

github.com/rax-maas/blueflood/wiki

cloud.google.com/bigtable/docs

www.h2gis.org/docs/home

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Rackspace

Google

CNRS

Oracle

Initial release

2013

2015

2013

2011

Current release

23.3, December 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

LGPL 3.0

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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

Server operating systems

Linux
OS X

hosted

Linux
Solaris SPARC/x86

Data scheme

predefined scheme

schema-free

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

Typing

predefined data types such as float or date

yes

optional

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 DML and DDL statements

APIs and other access methods

HTTP REST

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

RESTful HTTP API

Supported programming languages

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

Java

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

Server-side scripts

Stored procedures

yes

based on H2

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

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

yes

based on H2

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

ACID

configurable

ACID within a storage node (=shard)

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

off heap cache

User concepts

Access control

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

yes

based on H2

Access rights for users and roles

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