DBMS > ReductStore vs. RocksDB vs. SiteWhere vs. Spark SQL

System Properties Comparison ReductStore vs. RocksDB vs. SiteWhere vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

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.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

M2M integration platform for persisting/querying time series data

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

Primary database model

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

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/reductstore
www.reduct.store

rocksdb.org

github.com/sitewhere/sitewhere

spark.apache.org/sql

Technical documentation

www.reduct.store/docs

github.com/facebook/rocksdb/wiki

sitewhere1.sitewhere.io/index.html

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

Developer

ReductStore LLC

Facebook, Inc.

SiteWhere

Apache Software Foundation

Initial release

2023

2013

2010

2014

Current release

1.9, March 2024

9.2.1, May 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Business Source License 1.1

Open Source

BSD

Open Source

Common Public Attribution License Version 1.0

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

C++, Rust

C++

Java

Scala

Server operating systems

Docker
Linux
macOS
Windows

Linux

Linux
OS X
Windows

Data scheme

schema-free

predefined scheme

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 API

C++ API
Java API

HTTP REST

JDBC
ODBC

Supported programming languages

C++
JavaScript (Node.js)
Python
Rust

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

based on HBase

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

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

Users with fine-grained authorization concept

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