DBMS > Adabas vs. Google BigQuery vs. PlanetScale vs. Quasardb vs. TimesTen

System Properties Comparison Adabas vs. Google BigQuery vs. PlanetScale vs. Quasardb vs. TimesTen

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Name

Description

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

used typically together with the Natural programming platform

Large scale data warehouse service with append-only tables

Scalable, distributed, serverless MySQL database platform built on top of Vitess

Distributed, high-performance timeseries database

In-Memory RDBMS compatible to Oracle

Primary database model

Secondary database models

Document store
Spatial DBMS

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Score	3.17
Rank	#94	Overall
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Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.59
Rank	#151	Overall
	#70	Relational DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

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

cloud.google.com/bigquery

planetscale.com

quasar.ai

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

cloud.google.com/bigquery/docs

planetscale.com/docs

doc.quasar.ai/master

docs.oracle.com/database/timesten-18.1

Developer

Software AG

Google

PlanetScale

quasardb

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

1971

2010

2020

2009

1998

Current release

3.14.1, January 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

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

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++

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

hosted

Docker
Linux
macOS

BSD
Linux
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

yes

with tags

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

yes

with proprietary extensions

SQL-like query language

yes

APIs and other access methods

HTTP API

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

with add-on software Adabas SOA Gateway

RESTful HTTP/JSON API

ADO.NET
JDBC
MySQL protocol
ODBC

HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Natural

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

in Natural

user defined functions

in JavaScript

yes

proprietary syntax

PL/SQL

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

none

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

Multi-source replication
Source-replica replication

Source-replica replication with selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID at shard level

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Users with fine-grained authorization concept

no user groups or roles

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

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