DBMS > Citus vs. JSqlDb vs. Sequoiadb vs. Spark SQL

System Properties Comparison Citus vs. JSqlDb vs. Sequoiadb vs. Spark SQL

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Editorial information provided by DB-Engines

Name

JSqlDB seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

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

JavaScript Query Language Database, stores JavaScript objects and primitives

NewSQL database with distributed OLTP and SQL

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

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Document store
Relational DBMS

Relational DBMS

Secondary database models

Document store

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

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.citusdata.com

jsqldb.org (offline)

www.sequoiadb.com

spark.apache.org/sql

Technical documentation

docs.citusdata.com

www.sequoiadb.com/en/index.php?m=Files&a=index

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

Developer

Konrad von Backstrom

Sequoiadb Ltd.

Apache Software Foundation

Initial release

2010

2018

2013

2014

Current release

8.1, December 2018

0.8, December 2018

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

Open Source

Server: AGPL; Client: Apache V2

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

C++

Scala

Server operating systems

Linux

Linux
macOS
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

yes

standard, with numerous extensions

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

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

proprietary protocol using JSON

JDBC
ODBC

Supported programming languages

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

JavaScript

.Net
C++
Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

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

functions in JavaScript

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

none

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using RocksDB

yes

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

simple password-based access control

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