DBMS > Fujitsu Enterprise Postgres vs. GeoSpock vs. IRONdb vs. JSqlDb vs. Microsoft Azure Cosmos DB

System Properties Comparison Fujitsu Enterprise Postgres vs. GeoSpock vs. IRONdb vs. JSqlDb vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

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

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

JSqlDB seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

JavaScript Query Language Database, stores JavaScript objects and primitives

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS

Time Series DBMS

Document store
Object oriented DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store
Spatial DBMS

Time Series DBMS

Spatial DBMS

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

Score	0.37
Rank	#278	Overall
	#128	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

www.postgresql.fastware.com

geospock.com

www.circonus.com/solutions/time-series-database/

jsqldb.org (offline)

azure.microsoft.com/services/cosmos-db

Technical documentation

www.postgresql.fastware.com/product-manuals

docs.circonus.com/irondb/category/getting-started

learn.microsoft.com/azure/cosmos-db

Developer

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

GeoSpock

Circonus LLC.

Konrad von Backstrom

Microsoft

Initial release

2017

2018

2014

Current release

Fujitsu Enterprise Postgres 14, January 2022

2.0, September 2019

V0.10.20, January 2018

0.8, December 2018

License

Commercial or Open Source

commercial

Open Source

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, Javascript

C and C++

Server operating systems

Linux
Windows

hosted

Linux

Linux
macOS
Windows

hosted

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

yes

ANSI SQL for query only (using Presto)

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like query language

APIs and other access methods

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

JDBC

HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

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

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

JavaScript

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

user defined functions

yes, in Lua

functions in JavaScript

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

partitioning by range, list and by hash

Automatic sharding

Automatic, metric affinity per node

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

configurable replication factor, datacenter aware

none

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

using RocksDB

yes

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users can be defined per table

Access rights can be defined down to the item level

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