DBMS > Apache Impala vs. Heroic vs. Kinetica vs. Oracle vs. Quasardb

System Properties Comparison Apache Impala vs. Heroic vs. Kinetica vs. Oracle vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Fully vectorized database across both GPUs and CPUs

Widely used RDBMS

Distributed, high-performance timeseries database

Primary database model

Secondary database models

Document store

Spatial DBMS
Time Series DBMS

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	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Website

impala.apache.org

github.com/spotify/heroic

www.kinetica.com

www.oracle.com/database

quasar.ai

Technical documentation

impala.apache.org/impala-docs.html

spotify.github.io/heroic

docs.kinetica.com

docs.oracle.com/en/database

doc.quasar.ai/master

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Spotify

Kinetica

Oracle

quasardb

Initial release

2013

2014

2012

1980

2009

Current release

4.1.0, June 2022

7.1, August 2021

23c, September 2023

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

commercial

restricted free version is available

commercial

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

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++

Java

C, C++

C and C++

C++

Server operating systems

Linux

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

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Schemaless in JSON and XML columns

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

via Elasticsearch

yes

with tags

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

SQL-like query language

APIs and other access methods

JDBC
ODBC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
JavaScript (Node.js)
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

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

PL/SQL

also stored procedures in Java possible

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Source-replica replication

Multi-source replication
Source-replica replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

can be realized in PL/SQL

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate 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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

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'

yes

Transient mode

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

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

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