DBMS > GeoMesa vs. Google Cloud Bigtable vs. Google Cloud Spanner vs. Sadas Engine

System Properties Comparison GeoMesa vs. Google Cloud Bigtable vs. Google Cloud Spanner vs. Sadas Engine

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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.

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

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Spatial DBMS

Key-value store
Wide column store

Relational DBMS

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

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

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

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

www.geomesa.org

cloud.google.com/bigtable

cloud.google.com/spanner

www.sadasengine.com

Technical documentation

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

cloud.google.com/bigtable/docs

cloud.google.com/spanner/docs

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

CCRi and others

Google

SADAS s.r.l.

Initial release

2014

2015

2017

2006

Current release

5.0.0, May 2024

8.0

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

free trial version available

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

Scala

C++

Server operating systems

hosted

AIX
Linux
Windows

Data scheme

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

yes

Query statements complying to ANSI 2011

yes

APIs and other access methods

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

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

JDBC
ODBC
Proprietary protocol

Supported programming languages

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

Go
Java
JavaScript (Node.js)
Python

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

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

Multi-source replication with 3 replicas for regional instances.

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

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

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

Atomic single-row operations

ACID

Strict serializable isolation

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

managed by 'Learn by Usage'

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)

Access rights for users, groups and roles according to SQL-standard

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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