DBMS > Blueflood vs. Realm vs. ReductStore vs. Spark SQL vs. STSdb

System Properties Comparison Blueflood vs. Realm vs. ReductStore vs. Spark SQL vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

blueflood.io

realm.io

github.com/reductstore
www.reduct.store

spark.apache.org/sql

github.com/STSSoft/STSdb4

Technical documentation

github.com/rax-maas/blueflood/wiki

realm.io/docs

www.reduct.store/docs

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

Developer

Rackspace

Realm, acquired by MongoDB in May 2019

ReductStore LLC

Apache Software Foundation

STS Soft SC

Initial release

2013

2014

2023

2014

2011

Current release

1.9, March 2024

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Business Source License 1.1

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

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

Java

C++, Rust

Scala

Server operating systems

Linux
OS X

Android
Backend: server-less
iOS
Windows

Docker
Linux
macOS
Windows

Linux
OS X
Windows

Windows

Data scheme

predefined scheme

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

SQL-like DML and DDL statements

APIs and other access methods

HTTP REST

HTTP API

JDBC
ODBC

.NET Client API

Supported programming languages

.Net
Java

with Android only
Objective-C
React Native
Swift

C++
JavaScript (Node.js)
Python
Rust

Java
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

runs within the applications so server-side scripts are unnecessary

Triggers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

yes, utilizing Spark Core

none

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

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

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

In-Memory realm

User concepts

Access control

yes

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