DBMS > GeoMesa vs. Spark SQL vs. Sphinx vs. Transbase

System Properties Comparison GeoMesa vs. Spark SQL vs. Sphinx vs. Transbase

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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.

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

Open source search engine for searching in data from different sources, e.g. relational databases

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

Primary database model

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

Score	0.81
Rank	#214	Overall
	#4	Spatial DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	6.03
Rank	#60	Overall
	#6	Search engines

Score	0.15
Rank	#337	Overall
	#147	Relational DBMS

Website

www.geomesa.org

spark.apache.org/sql

sphinxsearch.com

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

sphinxsearch.com/docs

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

Developer

CCRi and others

Apache Software Foundation

Sphinx Technologies Inc.

Transaction Software GmbH

Initial release

2014

2001

1987

Current release

4.0.5, February 2024

3.5.0 ( 2.13), September 2023

3.5.1, February 2023

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence available

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

Scala

C++

C and C++

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

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

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (SphinxQL)

yes

APIs and other access methods

JDBC
ODBC

Proprietary protocol

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Java
Python
R
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

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

depending on storage layer

yes, utilizing Spark Core

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

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

depending on storage layer

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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