DBMS > Amazon Redshift vs. Apache Impala vs. Manticore Search vs. Oracle vs. Riak KV

System Properties Comparison Amazon Redshift vs. Apache Impala vs. Manticore Search vs. Oracle vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

Multi-storage database for search, including full-text search.

Widely used RDBMS

Distributed, fault tolerant key-value store

Primary database model

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store

Time Series DBMS

using the Manticore Columnar Library

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

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Trend Chart

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

aws.amazon.com/redshift

impala.apache.org

manticoresearch.com

www.oracle.com/database

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

manual.manticoresearch.com

docs.oracle.com/en/database

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Manticore Software

Oracle

OpenSource, formerly Basho Technologies

Initial release

2012

2013

2017

1980

2009

Current release

4.1.0, June 2022

6.0, February 2023

23c, September 2023

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

GPL version 2

commercial

restricted free version is available

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C and C++

Erlang

Server operating systems

hosted

Linux

FreeBSD
Linux
macOS
Windows

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

Linux
OS X

Data scheme

yes

Fixed schema

yes

Schemaless in JSON and XML columns

schema-free

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

Secondary indexes

restricted

yes

full-text index on all search fields

yes

restricted

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

user defined functions

PL/SQL

also stored procedures in Java possible

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding, horizontal partitioning

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Synchronous replication based on Galera library

Multi-source replication
Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

can be realized in PL/SQL

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

links between data sets can be stored

Transaction concepts

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

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

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'

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

yes, using Riak Security

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