DBMS > Oracle Berkeley DB vs. SAP IQ vs. Spark SQL

System Properties Comparison Oracle Berkeley DB vs. SAP IQ vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Widely used in-process key-value store

Columnar RDBMS optimized for Big Data analytics

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

Primary database model

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

column-oriented

Relational DBMS

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

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

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

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

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

spark.apache.org/sql

Technical documentation

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

help.sap.com/docs/SAP_IQ

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

Developer

Oracle

originally developed by Sleepycat, which was acquired by Oracle

SAP, formerly Sybase

Apache Software Foundation

Initial release

1994

2014

Current release

18.1.40, May 2020

16.1 SPS04, April 2019

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

commercial license available

commercial

Open Source

Apache 2.0

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

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

Scala

Server operating systems

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

Data scheme

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

yes

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

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

C
C#
C++
Java
Perl
PHP
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

none

shared disk or shared nothing architectures with SAP IQ Multiplexer

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

SAP/Sybase Replication Server

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

fine grained access rights according to SQL-standard

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