DBMS > GeoMesa vs. Transwarp ArgoDB vs. Transwarp Hippo

System Properties Comparison GeoMesa vs. Transwarp ArgoDB vs. Transwarp Hippo

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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.

Distributed Analytical Database to replace Hadoop+MPP hybrid architecture

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Spatial DBMS

Relational DBMS

Vector DBMS

Secondary database models

Search engine

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

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	0.07
Rank	#350	Overall
	#153	Relational DBMS

Score	0.00
Rank	#383	Overall
	#13	Vector DBMS

Website

www.geomesa.org

www.transwarp.cn/en/product/argodb

www.transwarp.cn/en/subproduct/hippo

Technical documentation

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

docs.transwarp.cn/#/documents-support/docs?category=ARGODB

Developer

CCRi and others

Transwarp

Initial release

2014

2023

Current release

5.0.0, May 2024

1.0, May 2023

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

C++

Server operating systems

Linux
macOS

Data scheme

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

yes

APIs and other access methods

RESTful HTTP API

Supported programming languages

C++
Java
Python

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

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

User concepts

Access control

yes

depending on the DBMS used for storage

Role based access control and fine grained access rights

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