DBMS > Apache Pinot vs. jBASE vs. Sequoiadb vs. Spark SQL

System Properties Comparison Apache Pinot vs. jBASE vs. Sequoiadb vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

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

A robust multi-value DBMS comprising development tools and middleware

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

Multivalue DBMS

Document store
Relational DBMS

Relational DBMS

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

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	1.49
Rank	#156	Overall
	#3	Multivalue DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

pinot.apache.org

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

www.sequoiadb.com

spark.apache.org/sql

Technical documentation

docs.pinot.apache.org

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

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

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

Developer

Apache Software Foundation and contributors

Rocket Software (formerly Zumasys)

Sequoiadb Ltd.

Apache Software Foundation

Initial release

2015

1991

2013

2014

Current release

1.0.0, September 2023

5.7

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

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

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Implementation language

Java

C++

Scala

Server operating systems

All OS with a Java JDK11 or higher

AIX
Linux
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

optional

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

Embedded SQL for jBASE in BASIC

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

proprietary protocol using JSON

JDBC
ODBC

Supported programming languages

Go
Java
Python

.Net
Basic
Jabbascript
Java

.Net
C++
Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Foreign keys

Referential integrity

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

In-memory capabilities

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

yes

User concepts

Access control

Access rights can be defined down to the item level

simple password-based access control

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