Obtain LLDP topology data¶
In this section we will show you how to run workflows to collect LLDP (Link Layer Discovery Protocol) information and store that information in the inventory (Elasticsearch).
The goal of this workflow is to collect LLDP information from different networking devices, reconcile that information into a single topology based on the IETF topology data model, store it and finally transform it in a dot G notation to allow for visualization.
Together with FRINX Machine, we ship sample topologies that allow you to explore our workflows without the need to provide access to your own networking devices.
Make sure you didn’t skip mounting all devices in inventory, otherwise this workflow might not work correctly.
Collect LLDP Information from Devices and Build Topology¶
In the following step we will start a workflow that goes to each mounted device, collects LLDP information, reconciles that information and finally stores that information in the inventory.
Then search for the workflow Build_read_store_LLDP_topology.
Go to the input tab of the workflow. The workflow has default parameters filled out. The values should be the following:
node_aggregation: system-name link_aggregation: bidirectional-abbreviations per_node_read_timeout: 30 concurrent_read_nodes: 8 destination_topology: lldp
Click on “Execute”.
After the workflow has completed, go to back to Dashboard menu and select Inventory tile to open Kibana and look for an entry called “lldp”. You should see a similar view like the following:
Exporting the IETF topology information in graphviz format¶
To export the LLDP topology data in a format that can be used for visualization in 3rd party tools run the following workflow:
Click onand search for Export_LLDP_topology.
Execute it and click on the workflow ID, select the workflow again, choose “Execution flow” and click on the green box with the workflow name to display the workflow output details. Click “Unescape” to unescape the Output.
Finally you can use any 3rd party visualization tool that can support the graphviz format like https://dreampuf.github.io/GraphvizOnline.
All workflows can be executed manually as shown in this demo or can be scheduled via the workflow scheduling features.