DBMS > Apache Impala vs. Fujitsu Enterprise Postgres vs. GeoSpock vs. MongoDB vs. RDF4J

System Properties Comparison Apache Impala vs. Fujitsu Enterprise Postgres vs. GeoSpock vs. MongoDB vs. RDF4J

Editorial information provided by DB-Engines

Name

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

Description

Analytic DBMS for Hadoop

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

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Relational DBMS

Document store

RDF store

Secondary database models

Document store

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	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.31
Rank	#285	Overall
	#129	Relational DBMS

Score	421.65
Rank	#5	Overall
	#1	Document stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

impala.apache.org

www.postgresql.fastware.com

geospock.com

www.mongodb.com

rdf4j.org

Technical documentation

impala.apache.org/impala-docs.html

www.postgresql.fastware.com/product-manuals

www.mongodb.com/docs/manual

rdf4j.org/documentation

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

GeoSpock

MongoDB, Inc

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2013

2009

2004

Current release

4.1.0, June 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

Open Source

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

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

yes

MongoDB available as DBaaS (MongoDB Atlas)

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Implementation language

C++

Java, Javascript

C++

Java

Server operating systems

Linux

Linux
Windows

hosted

Linux
OS X
Solaris
Windows

Linux
OS X
Unix
Windows

Data scheme

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.

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

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

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

temporal, categorical

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

ANSI SQL for query only (using Presto)

Read-only SQL queries via the MongoDB Atlas SQL Interface

APIs and other access methods

JDBC
ODBC

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

JDBC

GraphQL
HTTP REST
Prisma
proprietary protocol using JSON

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

All languages supporting JDBC/ODBC

.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

Java
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

JavaScript

yes

Triggers

yes

in MongoDB Atlas only

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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.

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

ACID

Multi-document ACID Transactions with snapshot isolation

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

optional, enabled by default

yes

in-memory storage is supported as well

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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...
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Built around the flexible document data model and unified API, MongoDB is a developer...
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Typical application scenarios

Transactional payments applications, reporting and mixed workloads.
» more

AI-enriched intelligent apps (Continental, Telefonica, Iron Mountain) Internet of...
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Key customers

ADP, Adobe, Amadeus, AstraZeneca, Auto Trader, Barclays, BBVA, Bosch, Cisco, CERN,...
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Market metrics

Over 30 years experience in database technology. Over 20 years in Postgres development...
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Hundreds of millions downloads, over 150,000+ Atlas clusters provisioned every month...
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Licensing and pricing models

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

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