DBMS > Ehcache vs. Fauna vs. Fujitsu Enterprise Postgres vs. GeoSpock vs. MongoDB

System Properties Comparison Ehcache vs. Fauna vs. Fujitsu Enterprise Postgres vs. GeoSpock vs. MongoDB

Editorial information provided by DB-Engines

Name

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

Description

A widely adopted Java cache with tiered storage options

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

Spatial and temporal data processing engine for extreme data scale

One of the most popular document stores available both as a fully managed cloud service and for deployment on self-managed infrastructure

Primary database model

Key-value store

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Relational DBMS

Document store

Secondary database models

Document store
Spatial DBMS

Time Series DBMS

Spatial DBMS
Search engine

integrated Lucene index, currently in MongoDB Atlas only.
Time Series DBMS

Time Series Collections introduced in Release 5.0
Vector DBMS

currently available in the MongoDB Atlas cloud service only

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

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

Score	1.52
Rank	#153	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	0.31
Rank	#285	Overall
	#129	Relational DBMS

Score	421.65
Rank	#5	Overall
	#1	Document stores

Website

www.ehcache.org

fauna.com

www.postgresql.fastware.com

geospock.com

www.mongodb.com

Technical documentation

www.ehcache.org/documentation

docs.fauna.com

www.postgresql.fastware.com/product-manuals

www.mongodb.com/docs/manual

Developer

Terracotta Inc, owned by Software AG

Fauna, Inc.

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

GeoSpock

MongoDB, Inc

Initial release

2009

2014

2009

Current release

3.10.0, March 2022

Fujitsu Enterprise Postgres 14, January 2022

2.0, September 2019

6.0.7, June 2023

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

MongoDB Inc.'s Server Side Public License v1. Prior versions were published under GNU AGPL v3.0. Commercial licenses are also available.

Cloud-based only

Only available as a cloud service

yes

MongoDB available as DBaaS (MongoDB Atlas)

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

MongoDB Atlas: Global multi-cloud database with unmatched data distribution and mobility across AWS, Azure, and Google Cloud, built-in automation for resource and workload optimization, and so much more.

Implementation language

Java

Scala

Java, Javascript

C++

Server operating systems

All OS with a Java VM

hosted

Linux
Windows

hosted

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

Although schema-free, documents of the same collection often follow the same structure. Optionally impose all or part of a schema by defining a JSON schema.

Typing

predefined data types such as float or date

yes

string, integer, double, decimal, boolean, date, object_id, geospatial

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

temporal, categorical

yes

SQL

Support of SQL

yes

ANSI SQL for query only (using Presto)

Read-only SQL queries via the MongoDB Atlas SQL Interface

APIs and other access methods

JCache

RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC

GraphQL
HTTP REST
Prisma
proprietary protocol using JSON

Supported programming languages

Java

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Actionscript

unofficial driver
C
C#
C++
Clojure

unofficial driver
ColdFusion

unofficial driver
D

unofficial driver
Dart

unofficial driver
Delphi

unofficial driver
Erlang
Go
Groovy

unofficial driver
Haskell
Java
JavaScript
Kotlin
Lisp

unofficial driver
Lua

unofficial driver
MatLab

unofficial driver
Perl
PHP
PowerShell

unofficial driver
Prolog

unofficial driver
Python
R

unofficial driver
Ruby
Rust
Scala
Smalltalk

unofficial driver
Swift

Server-side scripts

Stored procedures

user defined functions

JavaScript

Triggers

yes

Cache Event Listeners

yes

in MongoDB Atlas only

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

horizontal partitioning

consistent hashing

partitioning by range, list and by hash

Automatic sharding

Sharding

Partitioned by hashed, ranged, or zoned sharding keys. Live resharding allows users to change their shard keys as an online operation with zero downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

Multi-source replication

Source-replica replication

Multi-Source deployments with MongoDB Atlas Global Clusters
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Eventual Consistency

can be individually decided for each read operation
Immediate Consistency

default behaviour

Foreign keys

Referential integrity

yes

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

yes

supports JTA and can work as an XA resource

ACID

Multi-document ACID Transactions with snapshot isolation

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

optional, enabled by default

In-memory capabilities

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

yes

In-memory storage engine introduced with MongoDB version 3.2

User concepts

Access control

Identity management, authentication, and access control

fine grained access rights according to SQL-standard

Access rights for users can be defined per table

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

100% compatible with community PostgreSQL
» more

MongoDB provides an integrated suite of cloud database and data services to accelerate...
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Competitive advantages

Built-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
» more

Built around the flexible document data model and unified API, MongoDB is a developer...
» more

Typical application scenarios

Transactional payments applications, reporting and mixed workloads.
» more

AI-enriched intelligent apps (Continental, Telefonica, Iron Mountain) Internet of...
» more

Key customers

ADP, Adobe, Amadeus, AstraZeneca, Auto Trader, Barclays, BBVA, Bosch, Cisco, CERN,...
» more

Market metrics

Over 30 years experience in database technology. Over 20 years in Postgres development...
» more

Hundreds of millions downloads, over 150,000+ Atlas clusters provisioned every month...
» more

Licensing and pricing models

Core based licensing
» more

MongoDB database server: Server-Side Public License (SSPL) . Commercial licenses...
» more

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