DBMS > AlaSQL vs. Apache Pinot vs. Citus vs. JaguarDB

System Properties Comparison AlaSQL vs. Apache Pinot vs. Citus vs. JaguarDB

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Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Performant, highly scalable DBMS for AI and IoT applications

Primary database model

Document store
Relational DBMS

Relational DBMS

Key-value store
Vector DBMS

Secondary database models

Document store

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	0.40
Rank	#270	Overall
	#125	Relational DBMS

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Website

Technical documentation

github.com/AlaSQL/alasql

docs.pinot.apache.org

docs.citusdata.com

www.jaguardb.com/support.html

Developer

Andrey Gershun & Mathias R. Wulff

Apache Software Foundation and contributors

DataJaguar, Inc.

Initial release

2014

2015

2010

2015

Current release

1.0.0, September 2023

8.1, December 2018

3.3 July 2023

License

Commercial or Open Source

Open Source

MIT-License

Open Source

Apache Version 2.0

Open Source

AGPL, commercial license also available

Open Source

GPL V3.0

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

Java

C++

the server part. Clients available in other languages

Server operating systems

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

All OS with a Java JDK11 or higher

Linux

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

specific XML type available, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like query language

yes

standard, with numerous extensions

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

JavaScript API

JDBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC

Supported programming languages

JavaScript

Go
Java
Python

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

other methods possible by using 3rd party extensions

Multi-source replication

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

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

rights management via user accounts

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