DBMS > ReductStore vs. RRDtool vs. Spark SQL vs. Transwarp ArgoDB

System Properties Comparison ReductStore vs. RRDtool vs. Spark SQL vs. Transwarp ArgoDB

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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.

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

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

Distributed Analytical Database to replace Hadoop+MPP hybrid architecture

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Search engine

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

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Score	1.90
Rank	#132	Overall
	#11	Time Series DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.12
Rank	#348	Overall
	#151	Relational DBMS

Website

github.com/reductstore
www.reduct.store

oss.oetiker.ch/rrdtool

spark.apache.org/sql

www.transwarp.cn/en/product/argodb

Technical documentation

www.reduct.store/docs

oss.oetiker.ch/rrdtool/doc

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

docs.transwarp.cn/#/documents-support/docs?category=ARGODB

Developer

ReductStore LLC

Tobias Oetiker

Apache Software Foundation

Transwarp

Initial release

2023

1999

2014

Current release

1.9, March 2024

1.8.0, 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Business Source License 1.1

Open Source

GPL V2 and FLOSS

Open Source

Apache 2.0

commercial

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++, Rust

Implementations in Java (e.g. RRD4J) and C# available

Scala

Server operating systems

Docker
Linux
macOS
Windows

HP-UX
Linux

Linux
OS X
Windows

Data scheme

yes

Typing

predefined data types such as float or date

Numeric data only

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.

Exporting into and restoring from XML files possible

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

HTTP API

in-process shared library
Pipes

JDBC
ODBC

Supported programming languages

C++
JavaScript (Node.js)
Python
Rust

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

by using the rrdcached daemon

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

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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