DBMS > Blueflood vs. JSqlDb vs. Spark SQL

System Properties Comparison Blueflood vs. JSqlDb 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 TimeSeries DBMS based on Cassandra

JavaScript Query Language Database, stores JavaScript objects and primitives

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

Primary database model

Time Series DBMS

Document store
Object oriented DBMS

Relational DBMS

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

Score	0.09
Rank	#353	Overall
	#33	Time Series DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

blueflood.io

jsqldb.org (offline)

spark.apache.org/sql

Technical documentation

github.com/rax-maas/blueflood/wiki

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

Developer

Rackspace

Konrad von Backstrom

Apache Software Foundation

Initial release

2013

2018

2014

Current release

0.8, December 2018

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

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

Scala

Server operating systems

Linux
OS X

Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

predefined 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

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

HTTP REST

JDBC
ODBC

Supported programming languages

JavaScript

Java
Python
R
Scala

Server-side scripts

Stored procedures

functions in JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

Transaction concepts

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

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

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