DBMS > Oracle Berkeley DB vs. Spark SQL vs. TerarkDB vs. Yanza

System Properties Comparison Oracle Berkeley DB vs. Spark SQL vs. TerarkDB vs. Yanza

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Editorial information provided by DB-Engines

Name

Description

Widely used in-process key-value store

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

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Time Series DBMS for IoT Applications

Primary database model

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Key-value store

Time Series DBMS

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

Score	2.77
Rank	#109	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	22.86
Rank	#35	Overall
	#21	Relational DBMS

Score	0.00
Rank	#354	Overall
	#54	Key-value stores

Score	0.00
Rank	#354	Overall
	#33	Time Series DBMS

Website

www.oracle.com/technetwork/database/database-technologies/berkeleydb/overview/index.html

spark.apache.org/sql

github.com/Terark/terarkdb

yanza.com

Technical documentation

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

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

github.com/Terark/terarkdb/wiki

Developer

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Apache Software Foundation

Terark

Yanza

Initial release

1994

2014

2016

2015

Current release

18.1.40, May 2020

3.2.0, October 2021

License

Commercial or Open Source

Open Source

commercial license available

Open Source

Apache 2.0

commercial

restricted open source version available

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

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

C++

Server operating systems

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X
Windows

Windows

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

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

C++ API
Java API

HTTP API

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

Java
Python
R
Scala

C++
Java

any language that supports HTTP calls

Server-side scripts

Stored procedures

Triggers

yes

only for the SQL API

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

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

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