DBMS > GeoSpock vs. Google Cloud Firestore vs. Heroic vs. Hypertable

System Properties Comparison GeoSpock vs. Google Cloud Firestore vs. Heroic vs. Hypertable

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Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Spatial and temporal data processing engine for extreme data scale

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

An open source BigTable implementation based on distributed file systems such as Hadoop

Primary database model

Secondary database models

Time Series DBMS

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

Score	6.63
Rank	#54	Overall
	#9	Document stores

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Website

geospock.com

firebase.google.com/products/firestore

github.com/spotify/heroic

Technical documentation

firebase.google.com/docs/firestore

spotify.github.io/heroic

Developer

GeoSpock

Google

Spotify

Hypertable Inc.

Initial release

2017

2014

2009

Current release

2.0, September 2019

0.9.8.11, March 2016

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GNU version 3. Commercial license available

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, Javascript

Java

C++

Server operating systems

hosted

Linux
OS X
Windows

an inofficial Windows port is available

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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

temporal, categorical

yes

via Elasticsearch

restricted

only exact value or prefix value scans

SQL

Support of SQL

ANSI SQL for query only (using Presto)

APIs and other access methods

JDBC

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

C++ API
Thrift

Supported programming languages

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

Triggers

yes, with Cloud Functions

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

selectable replication factor on file system level

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

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.

User concepts

Access control

Access rights for users can be defined per table

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

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More resources

DB-Engines blog posts

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