DBMS > AlaSQL vs. Databricks vs. Heroic vs. ReductStore

System Properties Comparison AlaSQL vs. Databricks vs. Heroic vs. ReductStore

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Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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

Time Series DBMS

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

Score	0.46
Rank	#260	Overall
	#40	Document stores
	#121	Relational DBMS

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

alasql.org

www.databricks.com

github.com/spotify/heroic

github.com/reductstore
www.reduct.store

Technical documentation

github.com/AlaSQL/alasql

docs.databricks.com

spotify.github.io/heroic

www.reduct.store/docs

Developer

Andrey Gershun & Mathias R. Wulff

Databricks

Spotify

ReductStore LLC

Initial release

2014

2013

2014

2023

Current release

1.9, March 2024

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

Apache 2.0

Open Source

Business Source License 1.1

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

JavaScript

Java

C++, Rust

Server operating systems

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

hosted

Docker
Linux
macOS
Windows

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, schema free)

schema-free

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

via Elasticsearch

SQL

Support of SQL

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

with Databricks SQL

APIs and other access methods

JavaScript API

JDBC
ODBC
RESTful HTTP API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

HTTP API

Supported programming languages

JavaScript

Python
R
Scala

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

user defined functions and aggregates

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency
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

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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