DBMS > Amazon Redshift vs. Apache Impala vs. GeoSpock vs. GridGain vs. Oracle

System Properties Comparison Amazon Redshift vs. Apache Impala vs. GeoSpock vs. GridGain vs. Oracle

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

Spatial and temporal data processing engine for extreme data scale

GridGain is an in-memory computing platform, built on Apache Ignite

Widely used RDBMS

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store

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	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Website

aws.amazon.com/redshift

impala.apache.org

geospock.com

www.gridgain.com

www.oracle.com/database

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

www.gridgain.com/docs/index.html

docs.oracle.com/en/database

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

GeoSpock

GridGain Systems, Inc.

Oracle

Initial release

2012

2013

2007

1980

Current release

4.1.0, June 2022

2.0, September 2019

GridGain 8.5.1

23c, September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java, Javascript

Java, C++, .Net

C and C++

Server operating systems

hosted

Linux

hosted

Linux
OS X
Solaris
Windows

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

Data scheme

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

Secondary indexes

restricted

yes

temporal, categorical

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

ANSI SQL for query only (using Presto)

ANSI-99 for query and DML statements, subset of DDL

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

JDBC

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Java
PHP
Python
Ruby
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

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

yes (compute grid and cache interceptors can be used instead)

PL/SQL

also stored procedures in Java possible

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes (replicated cache)

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes (compute grid and hadoop accelerator)

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

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

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

Access rights for users can be defined per table

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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