DBMS > Adabas vs. Amazon Redshift vs. Postgres-XL vs. ScyllaDB vs. Spark SQL

System Properties Comparison Adabas vs. Amazon Redshift vs. Postgres-XL vs. ScyllaDB vs. Spark SQL

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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Cassandra and DynamoDB compatible wide column store

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Secondary database models

Document store
Spatial DBMS

Key-value store

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

Score	2.79
Rank	#102	Overall
	#2	Multivalue DBMS

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	4.08
Rank	#76	Overall
	#5	Wide column stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

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

aws.amazon.com/redshift

www.postgres-xl.org

www.scylladb.com

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/redshift

www.postgres-xl.org/documentation

docs.scylladb.com

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Software AG

Amazon (based on PostgreSQL)

ScyllaDB

Apache Software Foundation

Initial release

1971

2012

2014

since 2012, originally named StormDB

2015

2014

Current release

10 R1, October 2018

ScyllaDB Open Source 5.4.1, January 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Mozilla public license

Open Source

Open Source (AGPL), commercial license available

Open Source

Apache 2.0

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.

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Implementation language

C++

Scala

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

hosted

Linux
macOS

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

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

XML type, but no XML query functionality

Secondary indexes

yes

restricted

yes

cluster global secondary indices

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

yes

does not fully support an SQL-standard

yes

distributed, parallel query execution

SQL-like DML and DDL statements (CQL)

SQL-like DML and DDL statements

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

JDBC
ODBC

Supported programming languages

Natural

All languages supporting JDBC/ODBC

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

in Natural

user defined functions

in Python

user defined functions

yes, Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

horizontal partitioning

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

selectable replication factor

Representation of geographical distribution of servers is possible

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

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

MVCC

Atomicity and isolation are supported for single operations

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

in-memory tables

User concepts

Access control

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

fine grained access rights according to SQL-standard

Access rights for users can be defined per object

More information provided by the system vendor

Specific characteristics

ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
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Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
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Typical application scenarios

ScyllaDB is ideal for applications that require high throughput and low latency at...
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Key customers

Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
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Market metrics

ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
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Licensing and pricing models

ScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
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