DBMS > Google Cloud Datastore vs. Newts vs. OrigoDB vs. PostGIS vs. Quasardb

System Properties Comparison Google Cloud Datastore vs. Newts vs. OrigoDB vs. PostGIS vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

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

Time Series DBMS based on Cassandra

A fully ACID in-memory object graph database

Spatial extension of PostgreSQL

Distributed, high-performance timeseries database

Primary database model

Document store

Time Series DBMS

Document store
Object oriented DBMS

Spatial DBMS

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

cloud.google.com/datastore

opennms.github.io/newts

origodb.com

postgis.net

quasar.ai

Technical documentation

cloud.google.com/datastore/docs

github.com/OpenNMS/newts/wiki

origodb.com/docs

postgis.net/documentation

doc.quasar.ai/master

Developer

Google

OpenNMS Group

Robert Friberg et al

quasardb

Initial release

2008

2014

2009

under the name LiveDB

2005

2009

Current release

3.4.2, February 2024

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPL v2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

hosted

Linux
OS X
Windows

Linux
Windows

BSD
Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes, details here

yes

User defined using .NET types and collections

yes

integer and binary

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.

can be achieved using .NET

yes

Secondary indexes

yes

with tags

SQL

Support of SQL

SQL-like query language (GQL)

yes

SQL-like query language

APIs and other access methods

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

HTTP REST
Java API

.NET Client API
HTTP API
LINQ

HTTP API

Supported programming languages

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

Java

.Net

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

Server-side scripts

Stored procedures

using Google App Engine

yes

user defined functions

Triggers

Callbacks using the Google Apps Engine

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

horizontal partitioning

client side managed; servers are not synchronized

yes

based on PostgreSQL

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

Source-replica replication

yes

based on PostgreSQL

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

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.

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

depending on model

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

Durability

Support for making data persistent

yes

Write ahead log

yes

by using LevelDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

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

Role based authorization

yes

based on PostgreSQL

Cryptographically strong user authentication and audit trail

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More resources

DB-Engines blog posts

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