DBMS > GeoSpock vs. Greenplum vs. IRONdb vs. JSqlDb vs. Microsoft Azure Cosmos DB

System Properties Comparison GeoSpock vs. Greenplum 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

Spatial and temporal data processing engine for extreme data scale

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

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

Time Series DBMS

Document store
Spatial DBMS

Spatial DBMS

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

Score	8.08
Rank	#48	Overall
	#31	Relational DBMS

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

Website

geospock.com

greenplum.org

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

jsqldb.org (offline)

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.greenplum.org

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

learn.microsoft.com/azure/cosmos-db

Developer

GeoSpock

Pivotal Software Inc.

Circonus LLC.

Konrad von Backstrom

Microsoft

Initial release

2005

2017

2018

2014

Current release

2.0, September 2019

7.0.0, September 2023

V0.10.20, January 2018

0.8, December 2018

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

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.

yes

since Version 4.2

Secondary indexes

temporal, categorical

yes

All properties auto-indexed by default

SQL

Support of SQL

ANSI SQL for query only (using Presto)

yes

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

SQL-like query language

APIs and other access methods

JDBC

JDBC
ODBC

HTTP API

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

Supported programming languages

C
Java
Perl
Python
R

.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

yes

yes, in Lua

functions in JavaScript

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

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

yes

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

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

Access rights for users can be defined per table

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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