DBMS > Cachelot.io vs. GeoMesa vs. Google Cloud Bigtable vs. TerminusDB

System Properties Comparison Cachelot.io vs. GeoMesa vs. Google Cloud Bigtable vs. TerminusDB

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Editorial information provided by DB-Engines

Name

Description

In-memory caching system

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Key-value store

Spatial DBMS

Key-value store
Wide column store

Graph DBMS

Secondary database models

Document store
RDF store

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

Score	0.04
Rank	#388	Overall
	#62	Key-value stores

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Website

cachelot.io

www.geomesa.org

cloud.google.com/bigtable

terminusdb.com

Technical documentation

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

cloud.google.com/bigtable/docs

terminusdb.github.io/terminusdb/#

Developer

CCRi and others

Google

DataChemist Ltd.

Initial release

2015

2014

2015

2018

Current release

5.0.0, May 2024

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Simplified BSD License

Open Source

Apache License 2.0

commercial

Open Source

GPL V3

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

C++

Scala

Prolog, Rust

Server operating systems

FreeBSD
Linux
OS X

hosted

Linux

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

SQL-like query language (WOQL)

APIs and other access methods

Memcached protocol

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

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

JavaScript
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

depending on storage layer

Sharding

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

depending on storage layer

Internal replication in Colossus, and regional replication between two clusters in different zones

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

depending on storage layer

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory journaling

In-memory capabilities

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

depending on storage layer

User concepts

Access control

yes

depending on the DBMS used for storage

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Role-based access control

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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