DBMS > Amazon Redshift vs. Apache Impala vs. CouchDB vs. KeyDB vs. SAP Adaptive Server

System Properties Comparison Amazon Redshift vs. Apache Impala vs. CouchDB vs. KeyDB vs. SAP Adaptive Server

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

Primary database model

Secondary database models

Document store

Spatial DBMS

using the Geocouch extension

Spatial DBMS

with Boeing's Spatial Query Server

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	9.30
Rank	#45	Overall
	#7	Document stores

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	36.31
Rank	#25	Overall
	#17	Relational DBMS

Website

aws.amazon.com/redshift

impala.apache.org

couchdb.apache.org

github.com/Snapchat/KeyDB
keydb.dev

www.sap.com/products/technology-platform/sybase-ase.html

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

docs.couchdb.org/en/stable

docs.keydb.dev

help.sap.com/docs/SAP_ASE

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

EQ Alpha Technology Ltd.

SAP, Sybase

Initial release

2012

2013

2005

2019

1987

Current release

4.1.0, June 2022

3.3.3, December 2023

16.0

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache version 2

Open Source

BSD-3

commercial

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++

Erlang

C++

C and C++

Server operating systems

hosted

Linux

Android
BSD
Linux
OS X
Solaris
Windows

Linux

AIX
HP-UX
Linux
Solaris
Unix
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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.

Secondary indexes

restricted

yes

via views

yes

by using the Redis Search module

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

Proprietary protocol

RESP - REdis Serialization Protoco

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

C
C++
Cobol
Java
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

View functions in JavaScript

Lua

Java and Transact-SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

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

atomic operations within a single document possible

Optimistic locking, atomic execution of commands blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

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 database

simple password-based access control and ACL

fine grained access rights according to SQL-standard

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