DBMS > Adabas vs. Amazon Redshift vs. Databend vs. Graph Engine vs. Manticore Search

System Properties Comparison Adabas vs. Amazon Redshift vs. Databend vs. Graph Engine vs. Manticore Search

Editorial information provided by DB-Engines

Name

Description

OLTP - DBMS for mainframes and Linux/Unix/Windows environments

used typically together with the Natural programming platform

Large scale data warehouse service for use with business intelligence tools

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

Primary database model

Multivalue DBMS

Relational DBMS

Graph DBMS
Key-value store

Search engine

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	3.17
Rank	#94	Overall
	#1	Multivalue DBMS

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	0.22
Rank	#312	Overall
	#21	Search engines

Website

www.softwareag.com/en_corporate/platform/adabas-natural.html

aws.amazon.com/redshift

github.com/datafuselabs/databend
www.databend.com

www.graphengine.io

manticoresearch.com

Technical documentation

docs.aws.amazon.com/redshift

docs.databend.com

www.graphengine.io/docs/manual

manual.manticoresearch.com

Developer

Software AG

Amazon (based on PostgreSQL)

Databend Labs

Microsoft

Manticore Software

Initial release

1971

2012

2021

2010

2017

Current release

1.0.59, April 2023

6.0, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

MIT License

Open Source

GPL version 2

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

Rust

.NET and C

C++

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

hosted

hosted
Linux
macOS

.NET

FreeBSD
Linux
macOS
Windows

Data scheme

yes

Fixed schema

Typing

predefined data types such as float or date

yes

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

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

Secondary indexes

yes

restricted

yes

full-text index on all search fields

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

yes

does not fully support an SQL-standard

yes

SQL-like query language

APIs and other access methods

HTTP API

with add-on software Adabas SOA Gateway
SOAP-based API

with add-on software Adabas SOA Gateway

JDBC
ODBC

CLI Client
JDBC
RESTful HTTP API

RESTful HTTP API

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

Supported programming languages

Natural

All languages supporting JDBC/ODBC

Go
Java
JavaScript (Node.js)
Python
Rust

C#
C++
F#
Visual Basic

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

Server-side scripts

Stored procedures

in Natural

user defined functions

in Python

yes

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

yes, with additonal products like Adabas Cluster Services, Adabas Parallel Services, Adabas Vista

Sharding

none

horizontal partitioning

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

none

Synchronous replication based on Galera library

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

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

In-memory capabilities

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

yes

User concepts

Access control

only with OS-specific tools (e.g. IBM RACF, CA Top Secret)

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles

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