DBMS > Apache Phoenix vs. GeoSpock vs. Google Cloud Firestore vs. Hive vs. Snowflake

System Properties Comparison Apache Phoenix vs. GeoSpock vs. Google Cloud Firestore vs. Hive vs. Snowflake

Editorial information provided by DB-Engines

Name

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

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

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.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS

Document store

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

phoenix.apache.org

geospock.com

firebase.google.com/products/firestore

hive.apache.org

www.snowflake.com

Technical documentation

phoenix.apache.org

firebase.google.com/docs/firestore

cwiki.apache.org/confluence/display/Hive/Home

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

GeoSpock

Google

Apache Software Foundation

initially developed by Facebook

Snowflake Computing Inc.

Initial release

2014

2017

2012

2014

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

2.0, September 2019

3.1.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, Javascript

Java

Server operating systems

Linux
Unix
Windows

hosted

All OS with a Java VM

hosted

Data scheme

yes

late-bound, schema-on-read capabilities

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

Secondary indexes

yes

temporal, categorical

yes

SQL

Support of SQL

yes

ANSI SQL for query only (using Presto)

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC

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

JDBC
ODBC
Thrift

CLI Client
JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

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

C++
Java
PHP
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

yes, Firebase Rules & Cloud Functions

yes

user defined functions and integration of map-reduce

user defined functions

Triggers

yes, with Cloud Functions

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Using Cloud Dataflow

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

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.

yes

User concepts

Access control

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

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.

Access rights for users, groups and roles

Users with fine-grained authorization concept, user roles and pluggable authentication

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