DBMS > HarperDB vs. Newts vs. Oracle NoSQL vs. VoltDB

System Properties Comparison HarperDB vs. Newts vs. Oracle NoSQL vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Time Series DBMS based on Cassandra

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Document store

Time Series DBMS

Document store
Key-value store
Relational DBMS

Relational DBMS

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

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	3.05
Rank	#97	Overall
	#17	Document stores
	#16	Key-value stores
	#50	Relational DBMS

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

www.harperdb.io

opennms.github.io/newts

www.oracle.com/database/nosql/technologies/nosql

www.voltdb.com

Technical documentation

docs.harperdb.io/docs

github.com/OpenNMS/newts/wiki

docs.oracle.com/en/database/other-databases/nosql-database/index.html

docs.voltdb.com

Developer

HarperDB

OpenNMS Group

Oracle

VoltDB Inc.

Initial release

2017

2014

2011

2010

Current release

3.1, August 2021

24.1, May 2024

11.3, April 2022

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Node.js

Java

Java, C++

Server operating systems

Linux
OS X

Linux
OS X
Windows

Linux
Solaris SPARC/x86

Linux
OS X

for development

Data scheme

dynamic schema

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

Typing

predefined data types such as float or date

yes

JSON data types

yes

optional

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

yes

SQL

Support of SQL

SQL-like data manipulation statements

SQL-like DML and DDL statements

yes

only a subset of SQL 99

APIs and other access methods

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HTTP REST
Java API

RESTful HTTP API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

Java

C
C#
Go
Java
JavaScript (Node.js)
Python

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

Java

Triggers

Partitioning methods

Methods for storing different data on different nodes

A table resides as a whole on one (or more) nodes in a cluster

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

selectable replication factor

based on Cassandra

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

Atomic execution of specific operations

configurable

ACID within a storage node (=shard)

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes, using LMDB

yes

Snapshots and command logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

off heap cache

User concepts

Access control

Access rights for users and roles

Users and roles with access to stored procedures

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More resources

Recent citations in the news

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