DBMS > Oracle Berkeley DB vs. Spark SQL vs. TerarkDB vs. Transbase vs. Warp 10

System Properties Comparison Oracle Berkeley DB vs. Spark SQL vs. TerarkDB vs. Transbase vs. Warp 10

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

A resource-optimized, high-performance, universally applicable RDBMS

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

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

Score	2.52
Rank	#114	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.04
Rank	#377	Overall
	#58	Key-value stores

Score	0.15
Rank	#337	Overall
	#147	Relational DBMS

Score	0.08
Rank	#358	Overall
	#35	Time Series DBMS

Website

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

spark.apache.org/sql

github.com/bytedance/terarkdb

www.transaction.de/en/products/transbase.html

www.warp10.io

Technical documentation

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

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

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

www.transaction.de/en/products/transbase/features.html

www.warp10.io/content/02_Getting_started

Developer

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Apache Software Foundation

ByteDance, originally Terark

Transaction Software GmbH

SenX

Initial release

1994

2014

2016

1987

2015

Current release

18.1.40, May 2020

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

commercial license available

Open Source

Apache 2.0

commercial

restricted open source version available

commercial

free development license

Open Source

Apache License 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

C++

C and C++

Java

Server operating systems

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

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

yes

APIs and other access methods

JDBC
ODBC

C++ API
Java API

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API
Jupyter
WebSocket

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

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

WarpScript

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

More information provided by the system vendor

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