DBMS > Hive vs. jBASE vs. NSDb vs. SwayDB vs. Transbase

System Properties Comparison Hive vs. jBASE vs. NSDb vs. SwayDB vs. Transbase

Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A robust multi-value DBMS comprising development tools and middleware

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

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

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

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	1.49
Rank	#156	Overall
	#3	Multivalue DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

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

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

hive.apache.org

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

nsdb.io

swaydb.simer.au

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

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

nsdb.io/Architecture

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

Developer

Apache Software Foundation

initially developed by Facebook

Rocket Software (formerly Zumasys)

Simer Plaha

Transaction Software GmbH

Initial release

2012

1991

2017

2018

1987

Current release

3.1.3, April 2022

5.7

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache Version 2.0

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

All OS with a Java VM

AIX
Linux
Windows

Linux
macOS

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

optional

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

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Embedded SQL for jBASE in BASIC

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC
Thrift

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

gRPC
HTTP REST
WebSocket

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C++
Java
PHP
Python

.Net
Basic
Jabbascript
Java

Java
Scala

Java
Kotlin
Scala

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

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

selectable replication factor

yes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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 rights for users, groups and roles

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

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

show all

Recent citations in the news

Apache Software Foundation Announces Apache Hive 4.0
30 April 2024, Datanami

Design a data mesh pattern for Amazon EMR-based data lakes using AWS Lake Formation with Hive metastore ...
10 June 2024, AWS Blog

18 Top Big Data Tools and Technologies to Know About in 2024
24 January 2024, TechTarget

ASF Unveils the Next Evolution of Big Data Processing With the Launch of Hive 4.0
2 May 2024, Datanami

Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS | Amazon Web Services
18 October 2023, AWS Blog

provided by Google News

Temenos signs first customer in India
24 August 2009, Finextra

provided by Google News

Share this page