DBMS > HarperDB vs. Sadas Engine vs. Spark SQL vs. SQL.JS

System Properties Comparison HarperDB vs. Sadas Engine vs. Spark SQL vs. SQL.JS

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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.

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Spark SQL is a component on top of 'Spark Core' for structured data processing

Port of SQLite to JavaScript

Primary database model

Document store

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	#373	Overall
	#157	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.63
Rank	#241	Overall
	#112	Relational DBMS

Website

Technical documentation

docs.harperdb.io/docs

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

spark.apache.org/docs/latest/sql-programming-guide.html

sql.js.org/documentation/index.html

Developer

HarperDB

SADAS s.r.l.

Apache Software Foundation

Alon Zakai

enhancements implemented by others

Initial release

2017

2006

2014

2012

Current release

3.1, August 2021

8.0

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

free community edition available

commercial

free trial version available

Open Source

Apache 2.0

Open Source

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++

Scala

JavaScript

Server operating systems

Linux
OS X

AIX
Linux
Windows

Linux
OS X
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

dynamic schema

yes

Typing

predefined data types such as float or date

yes

JSON data types

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

yes

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JDBC
ODBC
Proprietary protocol

JDBC
ODBC

JavaScript 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++
Groovy
Java
PHP
Python

Java
Python
R
Scala

JavaScript

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

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

horizontal partitioning

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes, using LMDB

yes

except by serializing a db to a file

In-memory capabilities

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

yes

managed by 'Learn by Usage'

yes

User concepts

Access control

Access rights for users and roles

Access rights for users, groups and roles according to SQL-standard

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More resources

DB-Engines blog posts

New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

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