DBMS > Apache Impala vs. Citus vs. MarkLogic vs. Oracle

System Properties Comparison Apache Impala vs. Citus vs. MarkLogic vs. Oracle

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Operational and transactional Enterprise NoSQL database

Widely used RDBMS

Primary database model

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

Secondary database models

Document store

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

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Website

impala.apache.org

www.citusdata.com

www.marklogic.com

www.oracle.com/database

Technical documentation

impala.apache.org/impala-docs.html

docs.citusdata.com

docs.marklogic.com

docs.oracle.com/en/database

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

MarkLogic Corp.

Oracle

Initial release

2013

2010

2001

1980

Current release

4.1.0, June 2022

8.1, December 2018

11.0, December 2022

23c, September 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

AGPL, commercial license also available

commercial

restricted free version is available

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C and C++

Server operating systems

Linux

Linux
OS X
Windows

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

Data scheme

yes

schema-free

Schema can be enforced

yes

Schemaless in JSON and XML columns

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

specific XML type available, but no XML query functionality

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

standard, with numerous extensions

yes

SQL92

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

via XQuery or JavaScript

PL/SQL

also stored procedures in Java possible

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

other methods possible by using 3rd party extensions

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

can act as a resource manager in an XA/JTA transaction

ACID

isolation level can be parameterized

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.

yes, with Range Indexes

yes

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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

Role-based access control at the document and subdocument levels

fine grained access rights according to SQL-standard

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