DBMS > Apache Pinot vs. GeoMesa vs. Spark SQL vs. STSdb vs. Transbase

System Properties Comparison Apache Pinot vs. GeoMesa vs. Spark SQL vs. STSdb vs. Transbase

Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

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

Primary database model

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

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

pinot.apache.org

www.geomesa.org

spark.apache.org/sql

github.com/STSSoft/STSdb4

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

Technical documentation

docs.pinot.apache.org

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

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

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

Developer

Apache Software Foundation and contributors

CCRi and others

Apache Software Foundation

STS Soft SC

Transaction Software GmbH

Initial release

2015

2014

2011

1987

Current release

1.0.0, September 2023

5.0.0, May 2024

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache License 2.0

Open Source

Apache 2.0

Open Source

GPLv2, commercial license 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

Java

Scala

C and C++

Server operating systems

All OS with a Java JDK11 or higher

Linux
OS X
Windows

Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC

JDBC
ODBC

.NET Client API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Go
Java
Python

Java
Python
R
Scala

C#
Java

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

horizontal partitioning

depending on storage layer

yes, utilizing Spark Core

none

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

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

Spatial database management systems
6 April 2021, Matthias Gelbmann

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