DBMS > HarperDB vs. Newts vs. Quasardb vs. Sequoiadb vs. SQLite

System Properties Comparison HarperDB vs. Newts vs. Quasardb vs. Sequoiadb vs. SQLite

Editorial information provided by DB-Engines

Name

Description

HarperDB takes the "stack" out of "tech stack" by combining an ultra-fast document-style data store, in-memory cache, real-time message broker, and your application components into a single distributed technology.

Time Series DBMS based on Cassandra

Distributed, high-performance timeseries database

NewSQL database with distributed OLTP and SQL

Widely used embeddable, in-process RDBMS

Primary database model

Document store

Time Series DBMS

Document store
Relational DBMS

Relational DBMS

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

Score	0.55
Rank	#243	Overall
	#38	Document stores

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	0.13
Rank	#332	Overall
	#28	Time Series DBMS

Score	0.42
Rank	#263	Overall
	#41	Document stores
	#121	Relational DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Website

www.harperdb.io

opennms.github.io/newts

quasar.ai

www.sequoiadb.com

www.sqlite.org

Technical documentation

docs.harperdb.io/docs

github.com/OpenNMS/newts/wiki

doc.quasar.ai/master

www.sequoiadb.com/en/index.php?m=Files&a=index

www.sqlite.org/docs.html

Developer

HarperDB

OpenNMS Group

quasardb

Sequoiadb Ltd.

Dwayne Richard Hipp

Initial release

2017

2014

2009

2013

2000

Current release

3.1, August 2021

3.14.1, January 2024

3.46.1 (13 August 2024), August 2024

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

commercial

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

Open Source

Server: AGPL; Client: Apache V2

Open Source

Public Domain

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

C++

Server operating systems

Linux
OS X

Linux
OS X
Windows

BSD
Linux
OS X
Windows

Linux

server-less

Data scheme

dynamic schema

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

JSON data types

yes

integer and binary

yes

oid, date, timestamp, binary, regex

yes

not rigid because of 'dynamic typing' concept.

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

with tags

yes

SQL

Support of SQL

SQL-like data manipulation statements

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HTTP REST
Java API

HTTP API

proprietary protocol using JSON

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

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

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

.Net
C++
Java
PHP
Python

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

JavaScript

Triggers

yes

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

consistent hashing

Sharding

none

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

Source-replica replication with selectable replication factor

Source-replica replication

none

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

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Document is locked during a transaction

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes, using LMDB

yes

by using LevelDB

yes

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

simple password-based access control

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