DBMS > HBase vs. NSDb vs. Oracle Berkeley DB vs. Postgres-XL

System Properties Comparison HBase vs. NSDb vs. Oracle Berkeley DB vs. Postgres-XL

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

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

Widely used in-process key-value store

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Wide column store

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

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

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

hbase.apache.org

nsdb.io

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

www.postgres-xl.org

Technical documentation

hbase.apache.org/book.html

nsdb.io/Architecture

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

www.postgres-xl.org/documentation

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2008

2017

1994

2014

since 2012, originally named StormDB

Current release

2.3.4, January 2021

18.1.40, May 2020

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache Version 2.0

Open Source

commercial license available

Open Source

Mozilla public license

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

Java, Scala

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

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux
macOS

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
macOS

Data scheme

schema-free, schema definition possible

schema-free

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

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

yes

XML type, but no XML query functionality

Secondary indexes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

SQL interfaced based on SQLite is available

yes

distributed, parallel query execution

APIs and other access methods

Java API
RESTful HTTP API
Thrift

gRPC
HTTP REST
WebSocket

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

yes

Coprocessors in Java

user defined functions

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Single row ACID (across millions of columns)

ACID

MVCC

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

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