Category: Telegraf example

GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Skip to content. Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Branch: master. Find file Copy path. Raw Blame History. Telegraf is entirely plugin driven. All metrics are gathered from the. Plugins must be declared in here to be active.

To deactivate a plugin, comment out the name and any variables. Use 'telegraf -config telegraf. Environment variables can be used anywhere in this config file, simply surround. Environment variables can be used as tags, and throughout the config file. Default data collection interval for all inputs. Rounds collection interval to 'interval'. Telegraf will send metrics to outputs in batches of at most.

This controls the size of writes that Telegraf sends to output plugins. Maximum number of unwritten metrics per output. Increasing this value. Collection jitter is used to jitter the collection by a random amount. Each plugin will sleep for a random time within jitter before collecting.

This can be used to avoid many plugins querying things like sysfs at the. Default flushing interval for all outputs.InfluxDB and Grafana have also improved a lot.

Read on for details about to monitor network interface statistics using Telegraf, InfluxDB and Grafana. It is designed for exactly this use-case, where metrics are collected over time. It can then send those metrics to a variety of datastores, e. So it has very good support for writing data to InfluxDB. The advantage of this model is that is much more flexible - I can easily add collectors to get data from different sources, or I can query different data stores from my visualization layer.

Maybe I can even mash up multiple data sources in one graph or dashboard. Chronografa user interface written by InfluxData. My base system is a minimal install of Ubuntu No need for nightly or custom builds anymore! Follow the standard installation instructions to add the Influx Data repo, then install and start InfluxDB v1.

telegraf example

These are distributed from the same Influx Data repos we added in the last step. This includes the right packaging bits such that Telegraf is now properly configured as a service.

Much simpler than having to build Influxsnmp. Now we just need to configure it to poll our switch. Note that last step there - I commented out the mibs: line in snmp.

telegraf example

Now we need to configure Telegraf to poll our switch. The default telegraf. We can leave all that at default - it will work for our purposes.

We just need to add some configuration to tell it to use SNMP to poll our switch.

Monitoring with Telegraf, InfluxDB and Grafana

By default, Telegraf polls metrics every 10s. You can use the --test option to get Telegraf to grab one cycle of metrics, and publish them to stdout. This tells you that your configuration is sane, and capable of collecting data:. Follow the standard installation instructions to add the Grafana repo and signing key, then install the package:. Note the comments there - Grafana does not auto-start. Grafana is now listening on port A dashboard is a set of panels, grouped however you like - e.

It is super easy to create new dashboards, add panels, and play around with different ways of presenting your data. You can easily combine different data sources, creating all kinds of useful visualizations. Click on New dashboard :. Click on Graph, then click on Panel Title on the new panel.

Click Edit. Now we can add metrics, and set up our graph the way we want. On the Legend tab, select the With only zeros option. I only have a few interfaces enabled on this switch, so this will suppress display of all the disconnected interfaces.

It will get more interesting as you collect more data.

telegraf example

Add a Row to the Dashboard, and choose Singlestat. We want to know if our switch was rebooted recently.Learn about Grafana the monitoring solution for every database. Open Source is at the heart of what we do at Grafana Labs. So far i tested it on machine with 46 disks, 8 interfaces and it loaded correctly but pretty slow, poor browser barely handled all that data.

Docker "veth" interfaces are blacklisted via template regexp. Using per-partition IOPS produces way too much graphs, but if you really want it, you can fix it by editing regexp in "disk" template variable. Also, i'm not sure about drbd and other "virtual" block devices. Grafana Cloud. Downloads: Reviews: 5. Overview Revisions Reviews. Empty string writes to the default rp. If not provided, will default to 5s.

Setting mountpoints will restrict the stats to the specified mountpoints. Setting devices will restrict the stats to the specified devices. Get this dashboard: Copy ID to Clipboard.

Dependencies: Grafana 6. Data Sources: InfluxDB. Collector: Telegraf. Categories: Host Metrics. Trademark Policy. Grafana Features Contribute Dashboards Plugins. Events Contact.I've been using Munin for the past years as my monitoring tool. It works well, it's light, and super easy to set up.

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However Munin is old it's written in Perl Anyway, Munin is great, I will still use it, but it may be time to look at what kind of monitoring software we have in Instead of having one software that does everything nowadays we like to separate the roles this way:.

Just look at Grafana's possible sources. ELK is overkill for us the "E" Prometheus is a nice option, but as you read in the title, we're going to see how to setup TIG in this post. I was afraid at first because I thought all these hype softwares were a pain to install, but as you'll see, they're actually super simple to setup. They're both open source and written in go.

InfluxData provide the complete stack with Chronograf for displaying the data and Kapacitor for the alerting. This makes the TICK stack. As Grafana is a very high quality software that can also do alerting, I chose to use it. It's also more advanced than Chronograf.

As you can see, we really have a lot of possibilities! FYI we won't use Docker at all in the post, but you can run the components in containers if you want. You can launch its shell with the influx command. You can setup a retention policy if you wish. By default, the hostname will be the server hostname makes senseand the metrics will be collected every 10 seconds. I usually take a look at the inputs folder in the github repo because each inputs has a README that helps to set it up.

So now, we want to monitor other servers and send the data to InfluxDB. This will mess up your data and database. First we want to have certificates. You can get them the same way you did for Grafana, with acme.Use Kapacitor to import stream or batch time series data, and then transform, analyze, and act on the data. To get started using Kapacitor, use Telegraf to collect system metrics on your local machine and store them in InfluxDB.

Then, use Kapacitor to process your system data. Kapacitor tasks define work to do on a set of data using TICKscript syntax. Kapacitor tasks include:. Triggering an alert is a common Kapacitor use case. The database and retention policy to alert on must be defined.

Returns a graphviz dot formatted tree that shows the data processing pipeline defined by the TICKscript and key-value associative array entries with statistics about each node and links along an edge to the next node also including associative array statistical information. In the example above, the stream0 node aka the stream var from the TICKscript has sent 12 points to the from1 node. The from1 node has also sent 12 points on to the alert2 node.

If a connection error appears, for example: getsockopt: connection refused Linux or connectex: No connection could be made Windowsverify the Kapacitor service is running see Installing and Starting Kapacitor. If Kapacitor is running, check the firewall settings of the host machine and ensure that port is accessible. If the size is more than a few bytes, data has been captured. Telegraf logs errors if it cannot communicate to InfluxDB.

InfluxDB logs an error about connection refused if it cannot send data to Kapacitor. Use the flag -real-clock to set the replay time by deltas between the timestamps. Time is measured on each node by the data points it receives. Each JSON line represents one alert, and includes the alert level and data that triggered the alert.

Optional Modify the task to be really sensitive to ensure the alerts are working. In the TICKscript, change the lamda function. Once the alerts. This is probably not what was intended. Double quotes denote data fields, single quotes string values. To match the valuethe tick script above should look like this:.

The TICKscript below will compute the running mean and compare current values to it. It will then trigger an alert if the values are more than 3 standard deviations away from the mean. Just like that, a dynamic threshold can be created, and, if cpu usage drops in the day or spikes at night, an alert will be issued.

Try it out. Use define to update the task TICKscript.

telegraf example

Note: If a task is already enabled, redefining the task with the define command automatically reloads reload the task. To define a task without reloading it, use -no-reload. An alert trigger should be written to the log shortly, once enough artificial load has been created.

Leave the loop running for a few minutes. After canceling the loop, another alert should be issued indicating that cpu usage has again changed. Using this technique, alerts can be generated for the raising and falling edges of cpu usage, as well as any outliers. Now that the basics have been covered, here is a more real world example. Once the metrics from several hosts are streaming to Kapacitor, it is possible to do something like: Aggregate and group the cpu usage for each service running in each datacenter, and then trigger an alert based off the 95th percentile.In this blog post, we discuss using Telegraf as your core metrics collection platform with the Splunk App for Infrastructure SAI version 2.

#255 Node-Red, InfluxDB, and Grafana Tutorial on a Raspberry Pi

We provided steps and examples to make sense of everything along the way, and there are also links to resources for more advanced workflows and considerations. Telegraf is a metrics collection engine that runs on virtually any platform. It can collect metrics from virtually any source, and more inputs are being added pretty regularly.

Most importantly, as of version 1. Telegraf is a modular system that allows you to define inputs, processors, aggregators, serializers, and outputs. Inputs, as you would expect, are the sources of metrics. Processors and aggregators are internal methods that allow you to rename things, build internal aggregations, and define almost as many other user-defined customizations as you want.

Serializers and outputs are where the magic happens: they define the format of the output data, and where and how to send it. Version 1. You define the serializer in the [[output]] stanza.

This lets you format your metrics in different ways for different destinations. This configuration tells Telegraf that all metrics data the output sends will be in a Splunk-compatible format. The data format looks like this:. If you decide to send data to Splunk by writing to the HEC, you need to wrap the event in a bit of metadata. The resulting data looks like this:. This is the output that Telegraf uses to write metrics data to a file. Configure your Splunk Universal Forwarder to monitor that file.

This is what is done at TiVo. This is the output that Telegraf uses to write metrics data to HEC. Configuring Telegraf to output directly to the HEC is not quite as straightforward as using the file-based outputs configuration because you have to deal with authentication using HEC tokens.

We removed most of the comments from the stanza so we could focus on the important parts, but the HTTP output Telegraf provides has info about how to deal with HTTP basic authentication.

The other important info you got from your Splunk administrator is the HEC token. When Splunk introduced the metrics store, they also add two 2 SPL commands to help you access the metrics data. The newest version of SAI, version 2. Telegraf is treated the same as other metrics collectors e.

Entities are auto-discovered, appropriate graphs are drawn in the Entity Overview, and potentially interesting graphs are pre-populated in the Analysis Workspace. You can set alerts, groups, etc. This allows SAI to know that the source of the metrics is Telegraf and to configure entity discovery and out of-the-box dashboards accordingly.

Those two lines provide you with wonderful prebuilt charts like these:. Telegraf is a highly-configurable metrics collector that runs on a variety of platforms, collects metrics from a variety of sources, and allows you to use that data in Splunk. With the release of SAI 2. For further information, check out the following resources:. The telegraf integration with Splunk App for Infrastructure is supported as part of the open source Splunk metrics serializer project.

For questions regarding setup and management of telegraf for sending data to Splunk please see the metrics serializer section of the telegraf project. You can also ask any questions in the splunk-usergroups Slack workspace.

Information about signing up can be found, here. Look for the it-infra-monitoring channel. Nick Tankersley is a riddle inside a mystery wrapped in an enigma surrounded by a sudoku that didn't look that hard but has taken most of the flight even though the person next to you finished it in, like, 10 minutes.Comment 0. Telegrafwhich is part of the TICK Stackis a plugin-driven server agent for collecting and reporting metrics. It also has output plugins to send metrics to a variety of other datastores, services, and message queues not restricted then to InfluxDB only.

In this article I am going to describe the setup to pull metrics from Java applications hosted inside a Docker container. I am also assuming you have the Telegraf agent already installed in the node where the Docker container is going to be shipped. Telegraf comes with a plugin to pull metrics from Docker containers.

Please have a look at the Github readme file for this plugin for the full list of metrics you can pull from any Docker container. Those metrics give you a full picture of what's going on for the containers, but no specific info about the Java application running inside.

The Daily Telegraf: Getting Started with Telegraf and Splunk

For this reason, you have to configure a second Telegraf plugin. In order to pull metrics from any application running on a JVM, Telegraf comes with a specific plugin which uses the Jolokia agent. Jolokia is an agent based approach for remote JMX access. In the Telegraf configuration, you have to setup the Jolokia endpoint details for the input plugin:.

At this stage, the Telegraf configuration is ready. You need to restart the agent to make it effective. One thing is still missing for this process: a running Jolokia agent listening at the port specified in the plugin configuration and attached to the Java application process.

The Jolokia agent can be shipped in the same Docker container for the Java application to be monitored. You need to add some instructions to the Dockerfile for your Java application. Build your image as usual and then ship and start a container in the destination machine where you started the Telegraf agent.

When starting the container you have to map the Jolokia agent listening port along with any other port used by the Java application:. Assuming your Telegraf agent is configured to send data to InfluxDByou should see in the database a table named jolokiawhich contains the following fields keys:.

Once understood how to set up the proper configurations, the process to implement the monitoring of Java applications inside Docker containers using Telegraf is pretty straightforward.

Performance Zone. Over a million developers have joined DZone. Let's be friends:. DZone 's Guide to. Learn how to configure Telegraf, a plugin-driven server agent for collecting and reporting metrics, to pull metrics from a Java app inside a Docker container. Free Resource. Like 2. Join the DZone community and get the full member experience. Join For Free. Globs accepted. Telegraf Configuration for Jolokia In order to pull metrics from any application running on a JVM, Telegraf comes with a specific plugin which uses the Jolokia agent.

In the Telegraf configuration, you have to setup the Jolokia endpoint details for the input plugin: Read JMX metrics through Jolokia [[inputs. Here's an example: List of metrics collected on above servers Each metric consists in a name, a jmx path and either a pass or drop slice attribute. This collects all heap memory usage metrics.