DBMS > Blueflood vs. Manticore Search vs. RRDtool

System Properties Comparison Blueflood vs. Manticore Search vs. RRDtool

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

Multi-storage database for search, including full-text search.

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.

Primary database model

Time Series DBMS

Search engine

Time Series DBMS

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	0.09
Rank	#353	Overall
	#33	Time Series DBMS

Score	0.23
Rank	#317	Overall
	#21	Search engines

Score	2.14
Rank	#124	Overall
	#10	Time Series DBMS

Website

blueflood.io

manticoresearch.com

oss.oetiker.ch/rrdtool

Technical documentation

github.com/rax-maas/blueflood/wiki

manual.manticoresearch.com

oss.oetiker.ch/rrdtool/doc

Developer

Rackspace

Manticore Software

Tobias Oetiker

Initial release

2013

2017

1999

Current release

6.0, February 2023

1.8.0, 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GPL version 2

Open Source

GPL V2 and FLOSS

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++

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

Server operating systems

Linux
OS X

FreeBSD
Linux
macOS
Windows

HP-UX
Linux

Data scheme

predefined scheme

Fixed schema

yes

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

Numeric data only

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.

Can index from XML

Exporting into and restoring from XML files possible

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

APIs and other access methods

HTTP REST

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

in-process shared library
Pipes

Supported programming languages

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

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

Server-side scripts

Stored procedures

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Synchronous replication based on Galera library

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

none

Foreign keys

Referential integrity

Transaction concepts

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

yes

isolated transactions for atomic changes and binary logging for safe writes

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore 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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