DBMS > Blueflood vs. Google Cloud Datastore vs. H2GIS vs. PostGIS

System Properties Comparison Blueflood vs. Google Cloud Datastore vs. H2GIS vs. PostGIS

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Spatial extension of H2

Spatial extension of PostgreSQL

Primary database model

Time Series DBMS

Document store

Spatial 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	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.08
Rank	#368	Overall
	#7	Spatial DBMS

Score	21.72
Rank	#29	Overall
	#1	Spatial DBMS

Website

blueflood.io

cloud.google.com/datastore

www.h2gis.org

postgis.net

Technical documentation

github.com/rax-maas/blueflood/wiki

cloud.google.com/datastore/docs

www.h2gis.org/docs/home

postgis.net/documentation

Developer

Rackspace

Google

CNRS

Initial release

2013

2008

2013

2005

Current release

3.4.2, February 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

LGPL 3.0

Open Source

GPL v2.0

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

Java

Server operating systems

Linux
OS X

hosted

Data scheme

predefined scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

APIs and other access methods

HTTP REST

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java

Server-side scripts

Stored procedures

using Google App Engine

yes

based on H2

user defined functions

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

none

yes

based on PostgreSQL

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Multi-source replication using Paxos

yes

based on H2

yes

based on PostgreSQL

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

User concepts

Access control

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

yes

based on H2

yes

based on PostgreSQL

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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