SlideShare a Scribd company logo
CACTI
Presented By:
Sweta Dargad
What is Cacti?
 Cacti is a complete frontend to RRDTool, it
stores all of the necessary information to
create graphs and populate them with data
in a MySQL database. The frontend is
completely PHP driven. Along with being able
to maintain Graphs, Data Sources, and Round
Robin Archives in a database, cacti handles
the data gathering. There is also SNMP
support for those used to creating traffic
graphs with MRTG.
MRTG
 The Multi Router Traffic Grapher, or just
simply MRTG, is free software for
monitoring and measuring the traffic load
on network links. It allows the user to see
traffic load on a network over time in
graphical form.
The primary features of Cacti
include:
 unlimited graph items
 auto-padding support for graphs
 graph data manipulation
 flexible data sources
 data gathering on a non-standard timespan
 custom data-gathering scripts
 built-in SNMP support
 graph templates
 data source templates
 host templates
 tree, list, and preview views of graph data
 user-based management and security
Data Sources
 To do data gathering, you can feed cacti the paths to
any external script/command along with any data that
the user will need to "fill in“
 cacti will then gather this data in a cron-job and
populate a MySQL database/the round robin archives.
 Data Sources can also be created, which correspond to
actual data on the graph.
 For instance, if a user would want to graph the ping
times to a host, you could create a data source utilizing
a script that pings a host and returns it's value in
milliseconds. After defining options for RRDTool such as
how to store the data you will be able to define any
additional information that the data input source
requires, such as a host to ping in this case. Once a
data source is created, it is automatically maintained at
5 minute intervals.
Data Sources
 Data sources can be created that utilize
RRDTool's "create" and "update" functions.
Each data source can be used to gather
local or remote data and placed on a graph.
 Supports RRD files with more than one data
source and can use an RRD file stored
anywhere on the local file system.
 Round robin archive (RRA) settings can be
customized giving the user the ability to
gather data on non-standard timespans while
store varying amounts of data.
Graphs

Once one or more data sources are defined, an
RRDTool graph can be created using the data. Cacti
allows you to create almost any imaginable RRDTool
graph using all of the standard RRDTool graph types
and consolidation functions. A color selection area
and automatic text padding function also aid in the
creation of graphs to make the process easier.
 Not only can you create RRDTool based graphs in
cacti, but there are many ways to display them.
Along with a standard "list view" and a "preview
mode", which resembles the RRDTool frontend
14all, there is a "tree view", which allows you to put
graphs onto a hierarchical tree for organizational
purpose
Graphs
 Unlimited number of graph items can be defined for
each graph optionally utilizing CDEFs or data sources
from within cacti.
 Automatic grouping of GPRINT graph items to AREA,
STACK, and LINE[1-3] to allow for quick re-sequencing of
graph items.
 Auto-Padding support to make sure graph legend text
lines up.
 Graph data can be manipulated using the CDEF math
functions built into RRDTool. These CDEF functions can
be defined in cacti and can be used globally on each
graph.
 Support for all of RRDTool's graph item types including
AREA, STACK, LINE[1-3], GPRINT, COMMENT, VRULE, and
HRULE.

graphs
User Management
 Due to the many functions of cacti, a user
based management tool is built in so you
can add users and give them rights to
certain areas of cacti. This would allow
someone to create some users that can
change graph parameters, while others
can only view graphs. Each user also
maintains their own settings when it
comes to viewing graphs.
Data Gathering
 Contains a "data input" mechanism which allows
users to define custom scripts that can be used to
gather data. Each script can contain arguments that
must be entered for each data source created using
the script (such as an IP address).
 Built in SNMP support that can use php-snmp, ucd-
snmp, or net-snmp.
 Ability to retrieve data using SNMP or a script with an
index. An example of this would be populating a list
with IP interfaces or mounted partitions on a server.
Integration with graph templates can be defined to
enable one click graph creation for hosts.
 A PHP-based poller is provided to execute scripts,
retrieve SNMP data, and update your RRD files.
Graph Display
 The tree view allows users to create "graph
hierarchies" and place graphs on the tree. This
is an easy way to manage/organize a large
number of graphs.
 The list view lists the title of each graph in one
large list which links the user to the actual
graph.
 The preview view displays all of the graphs in
one large list format. This is similar to the
default view for the 14all cgi script for
RRDTool/MRTG.
User Management
 User based management allows
administrators to create users and assign
different levels of permissions to the cacti
interface.
 Permissions can be specified per-graph
for each user, making cacti suitable for co
location situations.
 Each user can keep their own graph
settings for varying viewing preferences.
Templating

Lastly, cacti is able to scale to a large number
of data sources and graphs through the use
of templates. This allows the creation of a
single graph or data source template which
defines any graph or data source associated
with it. Host templates enable you to define
the capabilities of a host so cacti can poll it
for information upon the addition of a new
host.
Templates
 Graph templates enable common graphs to be
grouped together by templating. Every field for a
normal graph can be templated or specified on a
per-graph basis.
 Data source templates enable common data source
types to be grouped together by templating. Every
field for a normal data source can be templated or
specified on a per-data source basis.
 Host templates are a group of graph and data
source templates that allow you to define common
host types. Upon the creation of a host, it will
automatically take on the properties of its template.
Download Cacti
 The latest stable version is 0.8.8a, released 04/29/12.
 Cacti requires MySQL, PHP, RRDTool, net-snmp, and a webserver that supports
PHP such as Apache or IIS. Please see the requirements section of the manual
for information on how to fulfill these requirements under certain operating
systems. Please use the install guide for either Unix or Windows for information
about installing Cacti.
 Linux/Unix in tar.gz format
 Windows in ZIP format
 Gentoo Linux users install Cacti using:
emerge cacti
 Debian Linux users install Cacti using:
apt-get install cacti
 Fedora Linux users
yum install cacti
 SUSE Linux users
Available in Yast or SUSE media. Version may not be the latest.
References
 http://www.net-snmp.org/
 http://www.cacti.net/features.php
 http://www.hpl.hp.com/research/cacti/
 http://en.wikipedia.org/wiki/Cacti_%28sof
tware%29

More Related Content

What's hot

Apache Spark™ is here to stay
Apache Spark™ is here to stayApache Spark™ is here to stay
Apache Spark™ is here to stayGiovanna Roda
 
Graph Data Processing With uRIKA Appliance
Graph Data Processing With uRIKA ApplianceGraph Data Processing With uRIKA Appliance
Graph Data Processing With uRIKA ApplianceDavid Prat
 
Working Experience_V5.0
Working Experience_V5.0Working Experience_V5.0
Working Experience_V5.0Danny Lai
 
Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache Apex
Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache ApexApache Big Data EU 2016: Next Gen Big Data Analytics with Apache Apex
Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache ApexApache Apex
 
From Batch to Streaming with Apache Apex Dataworks Summit 2017
From Batch to Streaming with Apache Apex Dataworks Summit 2017From Batch to Streaming with Apache Apex Dataworks Summit 2017
From Batch to Streaming with Apache Apex Dataworks Summit 2017Apache Apex
 
Intro to Apache Apex - Next Gen Platform for Ingest and Transform
Intro to Apache Apex - Next Gen Platform for Ingest and TransformIntro to Apache Apex - Next Gen Platform for Ingest and Transform
Intro to Apache Apex - Next Gen Platform for Ingest and TransformApache Apex
 
Apache Big Data EU 2016: Building Streaming Applications with Apache Apex
Apache Big Data EU 2016: Building Streaming Applications with Apache ApexApache Big Data EU 2016: Building Streaming Applications with Apache Apex
Apache Big Data EU 2016: Building Streaming Applications with Apache ApexApache Apex
 
Spark Based Distributed Deep Learning Framework For Big Data Applications
Spark Based Distributed Deep Learning Framework For Big Data Applications Spark Based Distributed Deep Learning Framework For Big Data Applications
Spark Based Distributed Deep Learning Framework For Big Data Applications Humoyun Ahmedov
 
Deep Dive into Apache Apex App Development
Deep Dive into Apache Apex App DevelopmentDeep Dive into Apache Apex App Development
Deep Dive into Apache Apex App DevelopmentApache Apex
 
Kafka to Hadoop Ingest with Parsing, Dedup and other Big Data Transformations
Kafka to Hadoop Ingest with Parsing, Dedup and other Big Data TransformationsKafka to Hadoop Ingest with Parsing, Dedup and other Big Data Transformations
Kafka to Hadoop Ingest with Parsing, Dedup and other Big Data TransformationsApache Apex
 
Apache Hadoop Big Data Technology
Apache Hadoop Big Data TechnologyApache Hadoop Big Data Technology
Apache Hadoop Big Data TechnologyJay Nagar
 
Introduction to Real-Time Data Processing
Introduction to Real-Time Data ProcessingIntroduction to Real-Time Data Processing
Introduction to Real-Time Data ProcessingApache Apex
 
Monitoring pg with_graphite_grafana
Monitoring pg with_graphite_grafanaMonitoring pg with_graphite_grafana
Monitoring pg with_graphite_grafanaJan Wieck
 
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...DataWorks Summit
 
Actionable Insights with Apache Apex at Apache Big Data 2017 by Devendra Tagare
Actionable Insights with Apache Apex at Apache Big Data 2017 by Devendra TagareActionable Insights with Apache Apex at Apache Big Data 2017 by Devendra Tagare
Actionable Insights with Apache Apex at Apache Big Data 2017 by Devendra TagareApache Apex
 

What's hot (15)

Apache Spark™ is here to stay
Apache Spark™ is here to stayApache Spark™ is here to stay
Apache Spark™ is here to stay
 
Graph Data Processing With uRIKA Appliance
Graph Data Processing With uRIKA ApplianceGraph Data Processing With uRIKA Appliance
Graph Data Processing With uRIKA Appliance
 
Working Experience_V5.0
Working Experience_V5.0Working Experience_V5.0
Working Experience_V5.0
 
Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache Apex
Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache ApexApache Big Data EU 2016: Next Gen Big Data Analytics with Apache Apex
Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache Apex
 
From Batch to Streaming with Apache Apex Dataworks Summit 2017
From Batch to Streaming with Apache Apex Dataworks Summit 2017From Batch to Streaming with Apache Apex Dataworks Summit 2017
From Batch to Streaming with Apache Apex Dataworks Summit 2017
 
Intro to Apache Apex - Next Gen Platform for Ingest and Transform
Intro to Apache Apex - Next Gen Platform for Ingest and TransformIntro to Apache Apex - Next Gen Platform for Ingest and Transform
Intro to Apache Apex - Next Gen Platform for Ingest and Transform
 
Apache Big Data EU 2016: Building Streaming Applications with Apache Apex
Apache Big Data EU 2016: Building Streaming Applications with Apache ApexApache Big Data EU 2016: Building Streaming Applications with Apache Apex
Apache Big Data EU 2016: Building Streaming Applications with Apache Apex
 
Spark Based Distributed Deep Learning Framework For Big Data Applications
Spark Based Distributed Deep Learning Framework For Big Data Applications Spark Based Distributed Deep Learning Framework For Big Data Applications
Spark Based Distributed Deep Learning Framework For Big Data Applications
 
Deep Dive into Apache Apex App Development
Deep Dive into Apache Apex App DevelopmentDeep Dive into Apache Apex App Development
Deep Dive into Apache Apex App Development
 
Kafka to Hadoop Ingest with Parsing, Dedup and other Big Data Transformations
Kafka to Hadoop Ingest with Parsing, Dedup and other Big Data TransformationsKafka to Hadoop Ingest with Parsing, Dedup and other Big Data Transformations
Kafka to Hadoop Ingest with Parsing, Dedup and other Big Data Transformations
 
Apache Hadoop Big Data Technology
Apache Hadoop Big Data TechnologyApache Hadoop Big Data Technology
Apache Hadoop Big Data Technology
 
Introduction to Real-Time Data Processing
Introduction to Real-Time Data ProcessingIntroduction to Real-Time Data Processing
Introduction to Real-Time Data Processing
 
Monitoring pg with_graphite_grafana
Monitoring pg with_graphite_grafanaMonitoring pg with_graphite_grafana
Monitoring pg with_graphite_grafana
 
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
 
Actionable Insights with Apache Apex at Apache Big Data 2017 by Devendra Tagare
Actionable Insights with Apache Apex at Apache Big Data 2017 by Devendra TagareActionable Insights with Apache Apex at Apache Big Data 2017 by Devendra Tagare
Actionable Insights with Apache Apex at Apache Big Data 2017 by Devendra Tagare
 

Similar to Cacti

CS8091_BDA_Unit_IV_Stream_Computing
CS8091_BDA_Unit_IV_Stream_ComputingCS8091_BDA_Unit_IV_Stream_Computing
CS8091_BDA_Unit_IV_Stream_ComputingPalani Kumar
 
Time series data monitoring at 99acres.com
Time series data monitoring at 99acres.comTime series data monitoring at 99acres.com
Time series data monitoring at 99acres.comRavi Raj
 
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCENETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCEcscpconf
 
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCENETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCEcsandit
 
Introduction to GCP Data Flow Presentation
Introduction to GCP Data Flow PresentationIntroduction to GCP Data Flow Presentation
Introduction to GCP Data Flow PresentationKnoldus Inc.
 
Introduction to GCP DataFlow Presentation
Introduction to GCP DataFlow PresentationIntroduction to GCP DataFlow Presentation
Introduction to GCP DataFlow PresentationKnoldus Inc.
 
Hatkit Project - Datafiddler
Hatkit Project - DatafiddlerHatkit Project - Datafiddler
Hatkit Project - Datafiddlerholiman
 
GraphQL & DGraph with Go
GraphQL & DGraph with GoGraphQL & DGraph with Go
GraphQL & DGraph with GoJames Tan
 
Report Hadoop Map Reduce
Report Hadoop Map ReduceReport Hadoop Map Reduce
Report Hadoop Map ReduceUrvashi Kataria
 
6 10-presentation
6 10-presentation6 10-presentation
6 10-presentationRemi Arnaud
 
WS-VLAM workflow
WS-VLAM workflowWS-VLAM workflow
WS-VLAM workflowguest6295d0
 
IRJET- Analysis of Boston’s Crime Data using Apache Pig
IRJET- Analysis of Boston’s Crime Data using Apache PigIRJET- Analysis of Boston’s Crime Data using Apache Pig
IRJET- Analysis of Boston’s Crime Data using Apache PigIRJET Journal
 
Microsoft R - ScaleR Overview
Microsoft R - ScaleR OverviewMicrosoft R - ScaleR Overview
Microsoft R - ScaleR OverviewKhalid Salama
 
Mis presentation
Mis presentationMis presentation
Mis presentationprutha_beta
 
Concepts and Methods of Embedding Statistical Data into Maps
Concepts and Methods of Embedding Statistical Data into MapsConcepts and Methods of Embedding Statistical Data into Maps
Concepts and Methods of Embedding Statistical Data into MapsMohammad Liton Hossain
 
Aucfanlab Datalake - Big Data Management Platform -
Aucfanlab Datalake - Big Data Management Platform -Aucfanlab Datalake - Big Data Management Platform -
Aucfanlab Datalake - Big Data Management Platform -Aucfan
 
Python and trending_data_ops
Python and trending_data_opsPython and trending_data_ops
Python and trending_data_opschase pettet
 
Osgis 2010 notes
Osgis 2010 notesOsgis 2010 notes
Osgis 2010 notesJoanne Cook
 
ANG-GridWay-Poster-Final-Colorful-Bright-Final0
ANG-GridWay-Poster-Final-Colorful-Bright-Final0ANG-GridWay-Poster-Final-Colorful-Bright-Final0
ANG-GridWay-Poster-Final-Colorful-Bright-Final0Jingjing Sun
 
ANG-GridWay-Poster-Final-Colorful-Bright-Final0
ANG-GridWay-Poster-Final-Colorful-Bright-Final0ANG-GridWay-Poster-Final-Colorful-Bright-Final0
ANG-GridWay-Poster-Final-Colorful-Bright-Final0Jingjing Sun
 

Similar to Cacti (20)

CS8091_BDA_Unit_IV_Stream_Computing
CS8091_BDA_Unit_IV_Stream_ComputingCS8091_BDA_Unit_IV_Stream_Computing
CS8091_BDA_Unit_IV_Stream_Computing
 
Time series data monitoring at 99acres.com
Time series data monitoring at 99acres.comTime series data monitoring at 99acres.com
Time series data monitoring at 99acres.com
 
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCENETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
 
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCENETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCE
 
Introduction to GCP Data Flow Presentation
Introduction to GCP Data Flow PresentationIntroduction to GCP Data Flow Presentation
Introduction to GCP Data Flow Presentation
 
Introduction to GCP DataFlow Presentation
Introduction to GCP DataFlow PresentationIntroduction to GCP DataFlow Presentation
Introduction to GCP DataFlow Presentation
 
Hatkit Project - Datafiddler
Hatkit Project - DatafiddlerHatkit Project - Datafiddler
Hatkit Project - Datafiddler
 
GraphQL & DGraph with Go
GraphQL & DGraph with GoGraphQL & DGraph with Go
GraphQL & DGraph with Go
 
Report Hadoop Map Reduce
Report Hadoop Map ReduceReport Hadoop Map Reduce
Report Hadoop Map Reduce
 
6 10-presentation
6 10-presentation6 10-presentation
6 10-presentation
 
WS-VLAM workflow
WS-VLAM workflowWS-VLAM workflow
WS-VLAM workflow
 
IRJET- Analysis of Boston’s Crime Data using Apache Pig
IRJET- Analysis of Boston’s Crime Data using Apache PigIRJET- Analysis of Boston’s Crime Data using Apache Pig
IRJET- Analysis of Boston’s Crime Data using Apache Pig
 
Microsoft R - ScaleR Overview
Microsoft R - ScaleR OverviewMicrosoft R - ScaleR Overview
Microsoft R - ScaleR Overview
 
Mis presentation
Mis presentationMis presentation
Mis presentation
 
Concepts and Methods of Embedding Statistical Data into Maps
Concepts and Methods of Embedding Statistical Data into MapsConcepts and Methods of Embedding Statistical Data into Maps
Concepts and Methods of Embedding Statistical Data into Maps
 
Aucfanlab Datalake - Big Data Management Platform -
Aucfanlab Datalake - Big Data Management Platform -Aucfanlab Datalake - Big Data Management Platform -
Aucfanlab Datalake - Big Data Management Platform -
 
Python and trending_data_ops
Python and trending_data_opsPython and trending_data_ops
Python and trending_data_ops
 
Osgis 2010 notes
Osgis 2010 notesOsgis 2010 notes
Osgis 2010 notes
 
ANG-GridWay-Poster-Final-Colorful-Bright-Final0
ANG-GridWay-Poster-Final-Colorful-Bright-Final0ANG-GridWay-Poster-Final-Colorful-Bright-Final0
ANG-GridWay-Poster-Final-Colorful-Bright-Final0
 
ANG-GridWay-Poster-Final-Colorful-Bright-Final0
ANG-GridWay-Poster-Final-Colorful-Bright-Final0ANG-GridWay-Poster-Final-Colorful-Bright-Final0
ANG-GridWay-Poster-Final-Colorful-Bright-Final0
 

More from sweta dargad

RRD Tool and Network Monitoring
RRD Tool and Network MonitoringRRD Tool and Network Monitoring
RRD Tool and Network Monitoringsweta dargad
 
Architecture for SNMP based Network Monitoring System
Architecture for SNMP based Network Monitoring SystemArchitecture for SNMP based Network Monitoring System
Architecture for SNMP based Network Monitoring Systemsweta dargad
 
Snmp based network monitoring system
Snmp based network monitoring systemSnmp based network monitoring system
Snmp based network monitoring systemsweta dargad
 
Applications of RFID technology
Applications of RFID technologyApplications of RFID technology
Applications of RFID technologysweta dargad
 
Cyber security tutorial2
Cyber security tutorial2Cyber security tutorial2
Cyber security tutorial2sweta dargad
 
Cyber security tutorial1
Cyber security tutorial1Cyber security tutorial1
Cyber security tutorial1sweta dargad
 
Classifying Cybercrimes
Classifying CybercrimesClassifying Cybercrimes
Classifying Cybercrimessweta dargad
 
Open source nms’s
Open source nms’sOpen source nms’s
Open source nms’ssweta dargad
 

More from sweta dargad (11)

Sock Puppet.pptx
Sock Puppet.pptxSock Puppet.pptx
Sock Puppet.pptx
 
Stacks
StacksStacks
Stacks
 
RRD Tool and Network Monitoring
RRD Tool and Network MonitoringRRD Tool and Network Monitoring
RRD Tool and Network Monitoring
 
Architecture for SNMP based Network Monitoring System
Architecture for SNMP based Network Monitoring SystemArchitecture for SNMP based Network Monitoring System
Architecture for SNMP based Network Monitoring System
 
Snmp based network monitoring system
Snmp based network monitoring systemSnmp based network monitoring system
Snmp based network monitoring system
 
Applications of RFID technology
Applications of RFID technologyApplications of RFID technology
Applications of RFID technology
 
Cyber security tutorial2
Cyber security tutorial2Cyber security tutorial2
Cyber security tutorial2
 
Cyber security tutorial1
Cyber security tutorial1Cyber security tutorial1
Cyber security tutorial1
 
Classifying Cybercrimes
Classifying CybercrimesClassifying Cybercrimes
Classifying Cybercrimes
 
All about snmp
All about snmpAll about snmp
All about snmp
 
Open source nms’s
Open source nms’sOpen source nms’s
Open source nms’s
 

Recently uploaded

Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)
Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)
Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)Julian Hyde
 
UiPath Test Automation using UiPath Test Suite series, part 2
UiPath Test Automation using UiPath Test Suite series, part 2UiPath Test Automation using UiPath Test Suite series, part 2
UiPath Test Automation using UiPath Test Suite series, part 2DianaGray10
 
IESVE for Early Stage Design and Planning
IESVE for Early Stage Design and PlanningIESVE for Early Stage Design and Planning
IESVE for Early Stage Design and PlanningIES VE
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Patrick Viafore
 
Intro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераIntro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераMark Opanasiuk
 
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdfLinux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdfFIDO Alliance
 
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya HalderCustom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya HalderCzechDreamin
 
Optimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through ObservabilityOptimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through ObservabilityScyllaDB
 
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...FIDO Alliance
 
Demystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyDemystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyJohn Staveley
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?Mark Billinghurst
 
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...FIDO Alliance
 
Syngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdfSyngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdfSyngulon
 
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...CzechDreamin
 
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...FIDO Alliance
 
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptxIOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptxAbida Shariff
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfFIDO Alliance
 
Connecting the Dots in Product Design at KAYAK
Connecting the Dots in Product Design at KAYAKConnecting the Dots in Product Design at KAYAK
Connecting the Dots in Product Design at KAYAKUXDXConf
 
Structuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessStructuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessUXDXConf
 
WSO2CONMay2024OpenSourceConferenceDebrief.pptx
WSO2CONMay2024OpenSourceConferenceDebrief.pptxWSO2CONMay2024OpenSourceConferenceDebrief.pptx
WSO2CONMay2024OpenSourceConferenceDebrief.pptxJennifer Lim
 

Recently uploaded (20)

Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)
Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)
Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)
 
UiPath Test Automation using UiPath Test Suite series, part 2
UiPath Test Automation using UiPath Test Suite series, part 2UiPath Test Automation using UiPath Test Suite series, part 2
UiPath Test Automation using UiPath Test Suite series, part 2
 
IESVE for Early Stage Design and Planning
IESVE for Early Stage Design and PlanningIESVE for Early Stage Design and Planning
IESVE for Early Stage Design and Planning
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024
 
Intro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераIntro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджера
 
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdfLinux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf
 
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya HalderCustom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
 
Optimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through ObservabilityOptimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through Observability
 
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
 
Demystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyDemystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John Staveley
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?
 
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
 
Syngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdfSyngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdf
 
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
 
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...
 
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptxIOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
 
Connecting the Dots in Product Design at KAYAK
Connecting the Dots in Product Design at KAYAKConnecting the Dots in Product Design at KAYAK
Connecting the Dots in Product Design at KAYAK
 
Structuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessStructuring Teams and Portfolios for Success
Structuring Teams and Portfolios for Success
 
WSO2CONMay2024OpenSourceConferenceDebrief.pptx
WSO2CONMay2024OpenSourceConferenceDebrief.pptxWSO2CONMay2024OpenSourceConferenceDebrief.pptx
WSO2CONMay2024OpenSourceConferenceDebrief.pptx
 

Cacti

  • 2. What is Cacti?  Cacti is a complete frontend to RRDTool, it stores all of the necessary information to create graphs and populate them with data in a MySQL database. The frontend is completely PHP driven. Along with being able to maintain Graphs, Data Sources, and Round Robin Archives in a database, cacti handles the data gathering. There is also SNMP support for those used to creating traffic graphs with MRTG.
  • 3. MRTG  The Multi Router Traffic Grapher, or just simply MRTG, is free software for monitoring and measuring the traffic load on network links. It allows the user to see traffic load on a network over time in graphical form.
  • 4. The primary features of Cacti include:  unlimited graph items  auto-padding support for graphs  graph data manipulation  flexible data sources  data gathering on a non-standard timespan  custom data-gathering scripts  built-in SNMP support  graph templates  data source templates  host templates  tree, list, and preview views of graph data  user-based management and security
  • 5. Data Sources  To do data gathering, you can feed cacti the paths to any external script/command along with any data that the user will need to "fill in“  cacti will then gather this data in a cron-job and populate a MySQL database/the round robin archives.  Data Sources can also be created, which correspond to actual data on the graph.  For instance, if a user would want to graph the ping times to a host, you could create a data source utilizing a script that pings a host and returns it's value in milliseconds. After defining options for RRDTool such as how to store the data you will be able to define any additional information that the data input source requires, such as a host to ping in this case. Once a data source is created, it is automatically maintained at 5 minute intervals.
  • 6. Data Sources  Data sources can be created that utilize RRDTool's "create" and "update" functions. Each data source can be used to gather local or remote data and placed on a graph.  Supports RRD files with more than one data source and can use an RRD file stored anywhere on the local file system.  Round robin archive (RRA) settings can be customized giving the user the ability to gather data on non-standard timespans while store varying amounts of data.
  • 7.
  • 8. Graphs  Once one or more data sources are defined, an RRDTool graph can be created using the data. Cacti allows you to create almost any imaginable RRDTool graph using all of the standard RRDTool graph types and consolidation functions. A color selection area and automatic text padding function also aid in the creation of graphs to make the process easier.  Not only can you create RRDTool based graphs in cacti, but there are many ways to display them. Along with a standard "list view" and a "preview mode", which resembles the RRDTool frontend 14all, there is a "tree view", which allows you to put graphs onto a hierarchical tree for organizational purpose
  • 9. Graphs  Unlimited number of graph items can be defined for each graph optionally utilizing CDEFs or data sources from within cacti.  Automatic grouping of GPRINT graph items to AREA, STACK, and LINE[1-3] to allow for quick re-sequencing of graph items.  Auto-Padding support to make sure graph legend text lines up.  Graph data can be manipulated using the CDEF math functions built into RRDTool. These CDEF functions can be defined in cacti and can be used globally on each graph.  Support for all of RRDTool's graph item types including AREA, STACK, LINE[1-3], GPRINT, COMMENT, VRULE, and HRULE. 
  • 11. User Management  Due to the many functions of cacti, a user based management tool is built in so you can add users and give them rights to certain areas of cacti. This would allow someone to create some users that can change graph parameters, while others can only view graphs. Each user also maintains their own settings when it comes to viewing graphs.
  • 12. Data Gathering  Contains a "data input" mechanism which allows users to define custom scripts that can be used to gather data. Each script can contain arguments that must be entered for each data source created using the script (such as an IP address).  Built in SNMP support that can use php-snmp, ucd- snmp, or net-snmp.  Ability to retrieve data using SNMP or a script with an index. An example of this would be populating a list with IP interfaces or mounted partitions on a server. Integration with graph templates can be defined to enable one click graph creation for hosts.  A PHP-based poller is provided to execute scripts, retrieve SNMP data, and update your RRD files.
  • 13. Graph Display  The tree view allows users to create "graph hierarchies" and place graphs on the tree. This is an easy way to manage/organize a large number of graphs.  The list view lists the title of each graph in one large list which links the user to the actual graph.  The preview view displays all of the graphs in one large list format. This is similar to the default view for the 14all cgi script for RRDTool/MRTG.
  • 14. User Management  User based management allows administrators to create users and assign different levels of permissions to the cacti interface.  Permissions can be specified per-graph for each user, making cacti suitable for co location situations.  Each user can keep their own graph settings for varying viewing preferences.
  • 15. Templating  Lastly, cacti is able to scale to a large number of data sources and graphs through the use of templates. This allows the creation of a single graph or data source template which defines any graph or data source associated with it. Host templates enable you to define the capabilities of a host so cacti can poll it for information upon the addition of a new host.
  • 16. Templates  Graph templates enable common graphs to be grouped together by templating. Every field for a normal graph can be templated or specified on a per-graph basis.  Data source templates enable common data source types to be grouped together by templating. Every field for a normal data source can be templated or specified on a per-data source basis.  Host templates are a group of graph and data source templates that allow you to define common host types. Upon the creation of a host, it will automatically take on the properties of its template.
  • 17. Download Cacti  The latest stable version is 0.8.8a, released 04/29/12.  Cacti requires MySQL, PHP, RRDTool, net-snmp, and a webserver that supports PHP such as Apache or IIS. Please see the requirements section of the manual for information on how to fulfill these requirements under certain operating systems. Please use the install guide for either Unix or Windows for information about installing Cacti.  Linux/Unix in tar.gz format  Windows in ZIP format  Gentoo Linux users install Cacti using: emerge cacti  Debian Linux users install Cacti using: apt-get install cacti  Fedora Linux users yum install cacti  SUSE Linux users Available in Yast or SUSE media. Version may not be the latest.
  • 18. References  http://www.net-snmp.org/  http://www.cacti.net/features.php  http://www.hpl.hp.com/research/cacti/  http://en.wikipedia.org/wiki/Cacti_%28sof tware%29