DBMS > Adabas vs. Amazon DynamoDB vs. Axibase vs. Google Cloud Datastore vs. PlanetScale

System Properties Comparison Adabas vs. Amazon DynamoDB vs. Axibase vs. Google Cloud Datastore vs. PlanetScale

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

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

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

Primary database model

Multivalue DBMS

Document store
Key-value store

Time Series DBMS

Document store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	3.17
Rank	#94	Overall
	#1	Multivalue DBMS

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	1.59
Rank	#151	Overall
	#70	Relational DBMS

Website

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

aws.amazon.com/dynamodb

axibase.com/docs/atsd/finance

cloud.google.com/datastore

planetscale.com

Technical documentation

docs.aws.amazon.com/dynamodb

cloud.google.com/datastore/docs

planetscale.com/docs

Developer

Software AG

Amazon

Axibase Corporation

Google

PlanetScale

Initial release

1971

2012

2013

2008

2020

Current release

15585

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

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

Java

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

hosted

Linux

hosted

Docker
Linux
macOS

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes, details here

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

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

SQL-like query language

SQL-like query language (GQL)

yes

with proprietary extensions

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 API

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

Natural

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Go
Java
PHP
Python
R
Ruby

.Net
Go
Java
JavaScript (Node.js)
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

Server-side scripts

Stored procedures

in Natural

yes

using Google App Engine

yes

proprietary syntax

Triggers

yes

by integration with AWS Lambda

yes

Callbacks using the Google Apps Engine

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

Source-replica replication

Multi-source replication using Paxos

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

User concepts

Access control

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Users with fine-grained authorization concept

no user groups or roles

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