DBMS > Apache Impala vs. Kinetica vs. NCache vs. Oracle vs. Tibero

System Properties Comparison Apache Impala vs. Kinetica vs. NCache vs. Oracle vs. Tibero

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Fully vectorized database across both GPUs and CPUs

Open-Source and Enterprise in-memory Key-Value Store

Widely used RDBMS

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store

Spatial DBMS
Time Series DBMS

Document store
Search engine

Using distributed Lucene

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	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.94
Rank	#195	Overall
	#29	Key-value stores

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Website

impala.apache.org

www.kinetica.com

www.alachisoft.com/ncache

www.oracle.com/database

us.tmaxsoft.com/products/tibero

Technical documentation

impala.apache.org/impala-docs.html

docs.kinetica.com

www.alachisoft.com/resources/docs

docs.oracle.com/en/database

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Kinetica

Alachisoft

Oracle

TmaxSoft

Initial release

2013

2012

2005

1980

2003

Current release

4.1.0, June 2022

7.1, August 2021

5.3.3, April 2024

23c, September 2023

6, April 2015

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Enterprise Edition available

commercial

restricted free version is available

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

C++

C, C++

C#, .NET, .NET Core, Java

C and C++

C and Assembler

Server operating systems

Linux

Linux
Windows

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

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-free

yes

Schemaless in JSON and XML columns

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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

SQL-like DML and DDL statements

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

yes

with proprietary extensions

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

IDistributedCache
JCache
LINQ
Proprietary native API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
JavaScript (Node.js)
Python

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

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

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

support for stored procedures with SQL-Server CLR

PL/SQL

also stored procedures in Java possible

Persistent Stored Procedure (PSM)

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Notifications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding, horizontal partitioning

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

yes, with selectable consistency level

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

optimistic locking and pessimistic locking

ACID

isolation level can be parameterized

ACID

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

GPU vRAM or System RAM

yes

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

planned for next version

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights for users and roles on table level

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

Market Leader in .NET Distributed Caching since 2005.
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Licensing and pricing models

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