DBMS > Heroic vs. Oracle Berkeley DB vs. Sadas Engine vs. SAP IQ vs. YottaDB

System Properties Comparison Heroic vs. Oracle Berkeley DB vs. Sadas Engine vs. SAP IQ vs. YottaDB

Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Widely used in-process key-value store

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Columnar RDBMS optimized for Big Data analytics

A fast and solid embedded Key-value store

Primary database model

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

column-oriented

Key-value store

Secondary database models

Relational DBMS

using the Octo plugin

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

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	0.20
Rank	#317	Overall
	#47	Key-value stores

Website

github.com/spotify/heroic

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

www.sadasengine.com

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

yottadb.com

Technical documentation

spotify.github.io/heroic

docs.oracle.com/cd/E17076_05/html/index.html

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

help.sap.com/docs/SAP_IQ

yottadb.com/resources/documentation

Developer

Spotify

Oracle

originally developed by Sleepycat, which was acquired by Oracle

SADAS s.r.l.

SAP, formerly Sybase

YottaDB, LLC

Initial release

2014

1994

2006

1994

2001

Current release

18.1.40, May 2020

8.0

16.1 SPS04, April 2019

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

commercial license available

commercial

free trial version available

commercial

Open Source

AGPL 3.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C, Java, C++ (depending on the Berkeley DB edition)

C++

Server operating systems

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

AIX
Linux
Windows

AIX
HP-UX
Linux
Solaris
Windows

Docker
Linux

Data scheme

schema-free

yes

schema-free

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

only with the Berkeley DB XML edition

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

yes

by using the Octo plugin

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC
Proprietary protocol

ADO.NET
JDBC
ODBC
RESTful HTTP API

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

.Net
C
C#
C++
Groovy
Java
PHP
Python

C
C#
C++
Java
Perl
PHP
Python
Ruby

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

yes

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

shared disk or shared nothing architectures with SAP IQ Multiplexer

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

SAP/Sybase Replication Server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

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

managed by 'Learn by Usage'

yes

User concepts

Access control

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

Users and groups based on OS-security mechanisms

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