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Extracting and integrating knowledge into your individual dashboard
In Half 1, my colleague Mel Delgado gave an introduction to ThousandEyes, and how one can deploy your individual ThousandEyes agent at residence by way of a raspberry pi. This SaaS platform is all about offering observability within the WAN with brokers working within the cloud, on prem, or in your finish host. Customers will get insights into latency, packet loss, community hops, BGP routing, but additionally HTTP web page load time, DNS server response time, DOM load time and so many extra stats! I believe it’s protected to say that ThousandEyes is accumulating tons of meta-data in regards to the well being of the entire Web and particularly to your working companies.
ThousandEyes already supplies a classy and customizable web-dashboard and this is perhaps adequate for many customers. Nevertheless, what if you want to combine knowledge from ThousandEyes into your individual utility or dashboard? Or, you want to use totally different visualization kinds, and even show aggregated datasets? In that case, learn on!
Extract and Combine ThousandEyes Knowledge
Extracting knowledge from ThousandEyes is straightforward with the intensive REST API. You possibly can get extra info on the developer reference web page. With the API you possibly can merely pull the historic or newest knowledge out of your ThousandEyes account to any database and visualize it in your dashboard utility of alternative. For example, I used a Python script to fetch and insert the information into the time-series database InfluxDB and visualized the information by way of the analytics & monitoring answer Grafana. To scale back the implementation time for brand new customers, I packaged all the things in a multi-container Docker utility by way of Docker-Compose.
The most effective half is, you possibly can attempt it out inside minutes! Get the code and clone the repository from the DevNet Code Alternate.
Getting historic knowledge and leveraging the Grafana dashboard
The python connector script permits to fetch even historic knowledge (the person can outline the time vary) from ThousandEyes which will likely be then inserted within the database. You possibly can then already see some knowledge within the pre-created Grafana dashboard template which you’ll be able to edit to your wants. You possibly can see some screenshots beneath. Moreover, you may also mix numerous knowledge sources into the identical dashboard (e.g. from WAN routers or firewalls) which may provide the final single view on the well being of your property.
Grafana Dashboard Template Web page 1
Grafana Dashboard Template Web page 2
Behind the Scenes
I selected to create a Python connector script to have full management of what knowledge must be inserted into influxDB and to have the flexibility to fetch historic knowledge from ThousandEyes. Another could be to create a Telegraf plugin in Go to get the newest knowledge.
The info is coming from assessments that are configured within the ThousandEyes dashboard. Since each check is accumulating totally different knowledge, and there are numerous assessments, the Python connector scripts solely help the most well-liked check sorts as of now – (Internet) web page load, (Internet) HTTP server, (Community) end-to-end metrics, (Community) path visualization. A very powerful info to get the proper knowledge from these assessments is the testId which will be queried by way of the REST API as documented right here. Upon getting the ID, you possibly can question the information from every of those check sorts.
I hope you should utilize this small utility to extract knowledge out of ThousandEyes! In fact, be at liberty to go to DevNet Code Alternate to increase it!
Find out how Cisco’s ThousandEyes supplies visibility and helps remedy networking issues throughout the web. DevNet Snack Minute Episode 35
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