DBMS > AlaSQL vs. HEAVY.AI vs. PostGIS vs. ReductStore

System Properties Comparison AlaSQL vs. HEAVY.AI vs. PostGIS vs. ReductStore

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Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Spatial extension of PostgreSQL

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Primary database model

Document store
Relational DBMS

Relational DBMS

Spatial DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	0.51
Rank	#256	Overall
	#40	Document stores
	#118	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	21.72
Rank	#29	Overall
	#1	Spatial DBMS

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Website

alasql.org

github.com/heavyai/heavydb
www.heavy.ai

postgis.net

github.com/reductstore
www.reduct.store

Technical documentation

github.com/AlaSQL/alasql

docs.heavy.ai

postgis.net/documentation

www.reduct.store/docs

Developer

Andrey Gershun & Mathias R. Wulff

HEAVY.AI, Inc.

ReductStore LLC

Initial release

2014

2016

2005

2023

Current release

5.10, January 2022

3.4.2, February 2024

1.9, March 2024

License

Commercial or Open Source

Open Source

MIT-License

Open Source

Apache Version 2; enterprise edition available

Open Source

GPL v2.0

Open Source

Business Source License 1.1

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

JavaScript

C++ and CUDA

C++, Rust

Server operating systems

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

Linux

Docker
Linux
macOS
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

yes

APIs and other access methods

JavaScript API

JDBC
ODBC
Thrift
Vega

HTTP API

Supported programming languages

JavaScript

All languages supporting JDBC/ODBC/Thrift
Python

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

yes

based on PostgreSQL

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes

based on PostgreSQL

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

only for local storage and DOM-storage

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

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

fine grained access rights according to SQL-standard

yes

based on PostgreSQL

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More resources

DB-Engines blog posts

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6 April 2021, Matthias Gelbmann

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