DBMS > PostGIS vs. RavenDB vs. Spark SQL vs. Transbase

System Properties Comparison PostGIS vs. RavenDB vs. Spark SQL vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

Spatial extension of PostgreSQL

Open Source Operational and Transactional Enterprise NoSQL Document Database

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

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

Primary database model

Spatial DBMS

Document store

Relational DBMS

Secondary database models

Relational DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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Trend Chart

Score	23.90
Rank	#29	Overall
	#1	Spatial DBMS

Score	3.01
Rank	#101	Overall
	#17	Document stores

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.15
Rank	#337	Overall
	#147	Relational DBMS

Website

postgis.net

ravendb.net

spark.apache.org/sql

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

Technical documentation

postgis.net/documentation

ravendb.net/docs

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

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

Developer

Hibernating Rhinos

Apache Software Foundation

Transaction Software GmbH

Initial release

2005

2010

2014

1987

Current release

3.4.2, February 2024

5.4, July 2022

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

GPL v2.0

Open Source

AGPL version 3, commercial license available

Open Source

Apache 2.0

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 and C++

Server operating systems

Linux
macOS
Raspberry Pi
Windows

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language (RQL)

SQL-like DML and DDL statements

yes

APIs and other access methods

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
Python
R
Scala

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

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

based on PostgreSQL

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on PostgreSQL

Multi-source replication

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

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

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.

User concepts

Access control

yes

based on PostgreSQL

Authorization levels configured per client per database

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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