DBMS > Heroic vs. RRDtool vs. Sphinx vs. STSdb

System Properties Comparison Heroic vs. RRDtool vs. Sphinx vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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.

Open source search engine for searching in data from different sources, e.g. relational databases

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Time Series DBMS

Search engine

Key-value store

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

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

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

Website

github.com/spotify/heroic

oss.oetiker.ch/rrdtool

sphinxsearch.com

github.com/STSSoft/STSdb4

Technical documentation

spotify.github.io/heroic

oss.oetiker.ch/rrdtool/doc

sphinxsearch.com/docs

Developer

Spotify

Tobias Oetiker

Sphinx Technologies Inc.

STS Soft SC

Initial release

2014

1999

2001

2011

Current release

1.8.0, 2022

3.5.1, February 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GPL V2 and FLOSS

Open Source

GPL version 2, commercial licence available

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

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

C++

Server operating systems

HP-UX
Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Exporting into and restoring from XML files possible

Secondary indexes

yes

via Elasticsearch

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language (SphinxQL)

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

in-process shared library
Pipes

Proprietary protocol

.NET Client API

Supported programming languages

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
Java

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

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

The original contents of fields are not stored in the Sphinx index.

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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The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

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