DBMS > Cachelot.io vs. GeoMesa vs. Spark SQL vs. SurrealDB vs. Transbase

System Properties Comparison Cachelot.io vs. GeoMesa vs. Spark SQL vs. SurrealDB vs. Transbase

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.

Spark SQL is a component on top of 'Spark Core' for structured data processing

A fully ACID transactional, developer-friendly, multi-model DBMS

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Key-value store

Spatial DBMS

Relational DBMS

Document store
Graph DBMS

Relational DBMS

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	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	1.02
Rank	#190	Overall
	#33	Document stores
	#18	Graph DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

www.transaction.de/en/products/transbase.html

Technical documentation

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

spark.apache.org/docs/latest/sql-programming-guide.html

surrealdb.com/docs

www.transaction.de/en/products/transbase/features.html

Developer

CCRi and others

Apache Software Foundation

SurrealDB Ltd

Transaction Software GmbH

Initial release

2015

2014

2022

1987

Current release

5.0.0, May 2024

3.5.0 ( 2.13), September 2023

v1.5.0, May 2024

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Simplified BSD License

Open Source

Apache License 2.0

Open Source

Apache 2.0

Open Source

commercial

free development license

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

C++

Scala

Rust

C and C++

Server operating systems

FreeBSD
Linux
OS X

Linux
OS X
Windows

Linux
macOS
Windows

FreeBSD
Linux
macOS
Solaris
Windows

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 DML and DDL statements

SQL-like query language

yes

APIs and other access methods

Memcached protocol

JDBC
ODBC

GraphQL
RESTful HTTP API
WebSocket

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

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

Java
Python
R
Scala

Deno
Go
JavaScript (Node.js)
Rust

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

depending on storage layer

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

depending on storage layer

none

Source-replica replication

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

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

User concepts

Access control

yes

depending on the DBMS used for storage

yes, based on authentication and database rules

fine grained access rights according to SQL-standard

More information provided by the system vendor

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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