DBMS > PostGIS vs. RavenDB vs. Solr vs. Teradata vs. TimesTen

System Properties Comparison PostGIS vs. RavenDB vs. Solr vs. Teradata vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Spatial extension of PostgreSQL

Open Source Operational and Transactional Enterprise NoSQL Document Database

A widely used distributed, scalable search engine based on Apache Lucene

A hybrid cloud data analytics software platform (Teradata Vantage)

In-Memory RDBMS compatible to Oracle

Primary database model

Secondary database models

Relational DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

postgis.net/documentation

ravendb.net/docs

solr.apache.org/resources.html

docs.teradata.com

docs.oracle.com/database/timesten-18.1

Developer

Hibernating Rhinos

Apache Software Foundation

Teradata

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2005

2010

2006

1984

1998

Current release

3.4.2, February 2024

5.4, July 2022

9.6.0, April 2024

Teradata Vantage 1.0 MU2, January 2019

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

GPL v2.0

Open Source

AGPL version 3, commercial license available

Open Source

Apache Version 2

commercial

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

Server operating systems

Linux
macOS
Raspberry Pi
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

hosted
Linux

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

yes

SQL

Support of SQL

yes

SQL-like query language (RQL)

Solr Parallel SQL Interface

yes

SQL 2016 + extensions

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

Java API
RESTful HTTP/JSON API

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

user defined functions

yes

Java plugins

yes

UDFs, stored procedures, table functions in parallel

PL/SQL

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

yes

based on PostgreSQL

Sharding

Hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on PostgreSQL

Multi-source replication

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

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.

Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

yes

based on PostgreSQL

Authorization levels configured per client per database

yes

fine grained access rights according to SQL-standard

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More resources

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