DBMS > HarperDB vs. Quasardb vs. RRDtool vs. VoltDB

System Properties Comparison HarperDB vs. Quasardb vs. RRDtool 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.

Distributed, high-performance timeseries database

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

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

Relational DBMS

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

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	1.90
Rank	#132	Overall
	#11	Time Series DBMS

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

www.harperdb.io

quasar.ai

oss.oetiker.ch/rrdtool

www.voltdb.com

Technical documentation

docs.harperdb.io/docs

doc.quasar.ai/master

oss.oetiker.ch/rrdtool/doc

docs.voltdb.com

Developer

HarperDB

quasardb

Tobias Oetiker

VoltDB Inc.

Initial release

2017

2009

1999

2010

Current release

3.1, August 2021

3.14.1, January 2024

1.8.0, 2022

11.3, April 2022

License

Commercial or Open Source

commercial

free community edition available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

GPL V2 and FLOSS

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

C++

Implementations in Java (e.g. RRD4J) and C# available

Java, C++

Server operating systems

Linux
OS X

BSD
Linux
OS X
Windows

HP-UX
Linux

Linux
OS X

for development

Data scheme

dynamic schema

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON data types

yes

integer and binary

Numeric data only

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.

Exporting into and restoring from XML files possible

Secondary indexes

yes

with tags

yes

SQL

Support of SQL

SQL-like data manipulation statements

SQL-like query language

yes

only a subset of SQL 99

APIs and other access methods

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HTTP API

in-process shared library
Pipes

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

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

consistent hashing

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

Source-replica replication with selectable replication factor

none

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

none

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes, using LMDB

yes

by using LevelDB

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

Transient mode

yes

User concepts

Access control

Access rights for users and roles

Cryptographically strong user authentication and audit trail

Users and roles with access to stored procedures

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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