DBMS > GeoMesa vs. Oracle NoSQL vs. SQream DB

System Properties Comparison GeoMesa vs. Oracle NoSQL vs. SQream DB

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Editorial information provided by DB-Engines

Name

Description

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Spatial DBMS

Document store
Key-value store
Relational DBMS

Relational DBMS

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

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

www.geomesa.org

www.oracle.com/database/nosql/technologies/nosql

sqream.com

Technical documentation

www.geomesa.org/documentation/stable/user/index.html

docs.oracle.com/en/database/other-databases/nosql-database/index.html

docs.sqream.com

Developer

CCRi and others

Oracle

SQream Technologies

Initial release

2014

2011

2017

Current release

4.0.5, February 2024

23.3, December 2023

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Scala

Java

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux
Solaris SPARC/x86

Linux

Data scheme

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

Typing

predefined data types such as float or date

yes

optional

yes, ANSI Standard SQL 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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP API

.Net
JDBC
ODBC

Supported programming languages

C
C#
Go
Java
JavaScript (Node.js)
Python

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

Sharding

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

configurable

ACID within a storage node (=shard)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

depending on storage layer

yes

off heap cache

User concepts

Access control

yes

depending on the DBMS used for storage

Access rights for users and roles

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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