DBMS > Amazon DocumentDB vs. Derby vs. Ehcache vs. Google Cloud Firestore vs. Snowflake

System Properties Comparison Amazon DocumentDB vs. Derby vs. Ehcache vs. Google Cloud Firestore vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

A widely adopted Java cache with tiered storage options

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.

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

Primary database model

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

aws.amazon.com/documentdb

db.apache.org/derby

www.ehcache.org

firebase.google.com/products/firestore

www.snowflake.com

Technical documentation

aws.amazon.com/documentdb/resources

db.apache.org/derby/manuals/index.html

www.ehcache.org/documentation

firebase.google.com/docs/firestore

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Terracotta Inc, owned by Software AG

Google

Snowflake Computing Inc.

Initial release

2019

1997

2009

2017

2014

Current release

10.17.1.0, November 2023

3.10.0, March 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Apache Version 2; commercial licenses available

commercial

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

All OS with a Java VM

hosted

Data scheme

schema-free

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

SQL

Support of SQL

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC

JCache

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

CLI Client
JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

Java

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

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Java Stored Procedures

yes, Firebase Rules & Cloud Functions

user defined functions

Triggers

yes

Cache Event Listeners

yes, with Cloud Functions

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

by using Terracotta Server

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

Source-replica replication

yes

by using Terracotta Server

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

ACID

yes

supports JTA and can work as an XA resource

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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 and roles

fine grained access rights according to SQL-standard

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

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

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