DBMS > HBase vs. NSDb vs. RavenDB vs. SwayDB vs. Transbase

System Properties Comparison HBase vs. NSDb vs. RavenDB vs. SwayDB vs. Transbase

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

Open Source Operational and Transactional Enterprise NoSQL Document Database

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

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

Primary database model

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

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

Technical documentation

hbase.apache.org/book.html

nsdb.io/Architecture

ravendb.net/docs

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Hibernating Rhinos

Simer Plaha

Transaction Software GmbH

Initial release

2008

2017

2010

2018

1987

Current release

2.3.4, January 2021

5.4, July 2022

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache Version 2.0

Open Source

AGPL version 3, commercial license available

Open Source

GNU Affero GPL V3.0

commercial

free development 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

Scala

C and C++

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux
macOS

Linux
macOS
Raspberry Pi
Windows

FreeBSD
Linux
macOS
Solaris
Windows

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.

Secondary indexes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (RQL)

yes

APIs and other access methods

Java API
RESTful HTTP API
Thrift

gRPC
HTTP REST
WebSocket

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

Java
Scala

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
Kotlin
Scala

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

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

none

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

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

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, Cluster-wide transaction available

Atomic execution of operations

yes

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

Authorization levels configured per client per database

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

Cloudera's HBase PaaS offering now supports Complex Transactions
11 August 2021, Krishna Maheshwari (sponsor)

Why is Hadoop not listed in the DB-Engines Ranking?
13 May 2013, Paul Andlinger

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