DBMS > LeanXcale vs. Oracle Berkeley DB vs. Spark SQL vs. YTsaurus

System Properties Comparison LeanXcale vs. Oracle Berkeley DB vs. Spark SQL vs. YTsaurus

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Editorial information provided by DB-Engines

Name

Description

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Widely used in-process key-value store

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

YTsaurus is an open source platform for distributed storage and processing.

Primary database model

Key-value store
Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Document store
Key-value store

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

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.21
Rank	#324	Overall
	#45	Document stores
	#48	Key-value stores

Website

www.leanxcale.com

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

spark.apache.org/sql

ytsaurus.tech

Technical documentation

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

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

ytsaurus.tech/docs/en

Developer

LeanXcale

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Apache Software Foundation

Yandex

Initial release

2015

1994

2014

2023

Current release

18.1.40, May 2020

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

commercial license available

Open Source

Apache 2.0

Open Source

Apache License, Version 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++

Server operating systems

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X
Windows

Ubuntu

Data scheme

yes

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

through Apache Derby

yes

SQL interfaced based on SQLite is available

SQL-like DML and DDL statements

YQL, an SQL-based language, is supported

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

JDBC
ODBC

Supported programming languages

C
Java
Scala

.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++
Go
Java
JavaScript
Python

Server-side scripts

Stored procedures

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Access Control Lists

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