DBMS > Google Cloud Datastore vs. H2GIS vs. Kyligence Enterprise vs. Realm

System Properties Comparison Google Cloud Datastore vs. H2GIS vs. Kyligence Enterprise vs. Realm

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Editorial information provided by DB-Engines

Name

Description

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

Spatial extension of H2

A distributed analytics engine for big data, built on top of Apache Kylin

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Primary database model

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
	#7	Spatial DBMS

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Website

cloud.google.com/datastore

www.h2gis.org

kyligence.io/kyligence-enterprise

realm.io

Technical documentation

cloud.google.com/datastore/docs

www.h2gis.org/docs/home

realm.io/docs

Developer

Google

CNRS

Kyligence, Inc.

Realm, acquired by MongoDB in May 2019

Initial release

2008

2013

2016

2014

License

Commercial or Open Source

commercial

Open Source

LGPL 3.0

commercial

Open Source

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

hosted

Linux

Android
Backend: server-less
iOS
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

ANSI SQL for queries (using Apache Calcite)

APIs and other access methods

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

JDBC
ODBC
RESTful HTTP API

Supported programming languages

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

Java

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

using Google App Engine

yes

based on H2

runs within the applications so server-side scripts are unnecessary

Triggers

Callbacks using the Google Apps Engine

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

based on H2

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

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.

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

In-Memory realm

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

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More resources

DB-Engines blog posts

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2 March 2016, Paul Andlinger

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