DBMS > Oracle vs. Quasardb vs. Solr vs. Spark SQL

System Properties Comparison Oracle vs. Quasardb vs. Solr vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Widely used RDBMS

Distributed, high-performance timeseries database

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

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

Primary database model

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

Spatial DBMS

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

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.oracle.com/database

quasar.ai

solr.apache.org

spark.apache.org/sql

Technical documentation

docs.oracle.com/en/database

doc.quasar.ai/master

solr.apache.org/resources.html

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

Developer

Oracle

quasardb

Apache Software Foundation

Initial release

1980

2009

2006

2014

Current release

23c, September 2023

3.14.1, January 2024

9.6.1, May 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

restricted free version is available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Apache Version 2

Open Source

Apache 2.0

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 and C++

C++

Java

Scala

Server operating systems

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

BSD
Linux
OS X
Windows

All OS with a Java VM

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

Linux
OS X
Windows

Data scheme

yes

Schemaless in JSON and XML columns

schema-free

yes

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

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language

Solr Parallel SQL Interface

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP API

Java API
RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

PL/SQL

also stored procedures in Java possible

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

consistent hashing

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication with selectable replication factor

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

with Hadoop integration

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

isolation level can be parameterized

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

Transient mode

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

yes

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