DBMS > Adabas vs. Apache Druid vs. OrientDB vs. RocksDB vs. TimescaleDB

System Properties Comparison Adabas vs. Apache Druid vs. OrientDB vs. RocksDB vs. TimescaleDB

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

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Multi-model DBMS (Document, Graph, Key/Value)

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Multivalue DBMS

Relational DBMS
Time Series DBMS

Document store
Graph DBMS
Key-value store

Key-value store

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	3.17
Rank	#94	Overall
	#1	Multivalue DBMS

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

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

Technical documentation

druid.apache.org/docs/latest/design

www.orientdb.com/docs/last/index.html

github.com/facebook/rocksdb/wiki

docs.timescale.com

Developer

Software AG

Apache Software Foundation and contributors

OrientDB LTD; CallidusCloud; SAP

Facebook, Inc.

Timescale

Initial release

1971

2012

2010

2013

2017

Current release

29.0.1, April 2024

3.2.29, March 2024

9.2.1, May 2024

2.15.0, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

Apache version 2

Open Source

BSD

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Linux
OS X
Unix

All OS with a Java JDK (>= JDK 6)

Linux

Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

Secondary indexes

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

SQL for querying

SQL-like query language, no joins

yes

full PostgreSQL SQL syntax

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
RESTful HTTP/JSON API

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

C++ API
Java API

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

Supported programming languages

Natural

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C
C++
Go
Java
Perl
Python
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

in Natural

Java, Javascript

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

Hooks

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

manual/auto, time-based

Sharding

horizontal partitioning

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes, via HDFS, S3 or other storage engines

Multi-source replication

yes

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Transaction concepts

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

ACID

yes

ACID

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

User concepts

Access control

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights for users and roles; record level security configurable

fine grained access rights according to SQL-standard

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