DBMS > AlaSQL vs. Drizzle vs. OpenTenBase vs. Spark SQL

System Properties Comparison AlaSQL vs. Drizzle vs. OpenTenBase vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

JavaScript DBMS library

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

An enterprise-level distributed HTAP open source database based on PostgreSQL

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

Primary database model

Document store
Relational 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	0.08
Rank	#370	Overall
	#156	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

alasql.org

github.com/OpenTenBase/OpenTenBase
www.opentenbase.org

spark.apache.org/sql

Technical documentation

github.com/AlaSQL/alasql

docs.opentenbase.org
docs.opentenbase.org/en

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

Developer

Andrey Gershun & Mathias R. Wulff

Drizzle project, originally started by Brian Aker

OpenAtom Foundation, previously Tencent

Apache Software Foundation

Initial release

2014

2008

2014

Current release

7.2.4, September 2012

2.5, January 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

MIT-License

Open Source

GNU GPL

Open Source

BSD-3

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

C++

Scala

Server operating systems

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

FreeBSD
Linux
OS X

Linux

Linux
OS X
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.

Secondary indexes

yes

SQL

Support of SQL

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

yes

with proprietary extensions

yes

SQL-like DML and DDL statements

APIs and other access methods

JavaScript API

JDBC

JDBC
ODBC

Supported programming languages

JavaScript

C
C++
Java
PHP

C
Go
Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

yes

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

yes

none

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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

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More resources

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