DBMS > AlaSQL vs. Apache Drill vs. Faircom EDGE vs. H2 vs. Spark SQL

System Properties Comparison AlaSQL vs. Apache Drill vs. Faircom EDGE vs. H2 vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

Primary database model

Document store
Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Spatial 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	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

alasql.org

drill.apache.org

www.faircom.com/products/faircom-edge

www.h2database.com

spark.apache.org/sql

Technical documentation

github.com/AlaSQL/alasql

drill.apache.org/docs

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

www.h2database.com/html/main.html

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

Developer

Andrey Gershun & Mathias R. Wulff

Apache Software Foundation

FairCom Corporation

Thomas Mueller

Apache Software Foundation

Initial release

2014

2012

1979

2005

2014

Current release

1.20.3, January 2023

V3, October 2020

2.2.220, July 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

MIT-License

Open Source

Apache Version 2

commercial

Restricted, free version available

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

Apache 2.0

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

ANSI C, C++

Java

Scala

Server operating systems

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

Linux
OS X
Windows

Android
Linux

ARM, x86
Raspbian
Windows

All OS with a Java VM

Linux
OS X
Windows

Data scheme

schema-free

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

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL 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.

yes

Secondary indexes

yes

SQL

Support of SQL

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

SQL SELECT statement is SQL:2003 compliant

yes

ANSI SQL queries

yes

SQL-like DML and DDL statements

APIs and other access methods

JavaScript API

JDBC
ODBC
RESTful HTTP API

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

JDBC
ODBC

Supported programming languages

JavaScript

C++

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

Java

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

yes

.Net, JavaScript, C/C++

Java Stored Procedures and User-Defined Functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

File partitioning

Customizable business rules for partitioning

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Synchronous and asynchronous realtime replication based on transaction logs

With clustering: 2 database servers on different computers operate on identical copies of a database

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

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

across SQL and NoSQL

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

Depending on the underlying data source

yes

In-memory capabilities

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

yes

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

fine grained access rights according to SQL-standard

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