DBMS > LeanXcale vs. NSDb vs. Oracle Berkeley DB vs. SpaceTime

System Properties Comparison LeanXcale vs. NSDb vs. Oracle Berkeley DB vs. SpaceTime

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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

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Widely used in-process key-value store

SpaceTime is a spatio-temporal DBMS with a focus on performance.

Primary database model

Key-value store
Relational DBMS

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Spatial DBMS

Secondary database models

Relational DBMS

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	0.08
Rank	#369	Overall
	#40	Time Series DBMS

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

Score	0.03
Rank	#392	Overall
	#8	Spatial DBMS

Website

www.leanxcale.com

nsdb.io

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

www.mireo.com/spacetime

Technical documentation

nsdb.io/Architecture

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

Developer

LeanXcale

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Mireo

Initial release

2015

2017

1994

2020

Current release

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

commercial license available

commercial

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, Scala

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

C++

Server operating systems

Linux
macOS

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

yes

through Apache Derby

SQL-like query language

yes

SQL interfaced based on SQLite is available

A subset of ANSI SQL is implemented

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

gRPC
HTTP REST
WebSocket

RESTful HTTP API

Supported programming languages

C
Java
Scala

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

C#
C++
Python

Server-side scripts

Stored procedures

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Fixed-grid hypercubes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Real-time block device replication (DRBD)

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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

Using Apache Lucene

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

yes

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