I am on the team that develops the Savvius Insight Appliance. During the development of Insight 1.0, I helped to define the value proposition, and the use cases for Insight, from there I helped to make choices about the hardware, the analysis, and the reporting. For the reporting we went with Splunk, and I designed the dashboards. For Insight 1.0 we decided to put a Splunk Forwarder on the appliance, but left it up to the user to provide their own Splunk Server.

We recently released Insight 2.0, which mainly added the ELK stack for long term reporting. This is great because with ELK on the Insight appliance, there is a full blown reporting solution built right in. In fact, the default mode for Insight 2.0 is to start capturing network traffic on the inline bridge ports, do the network flow and application analysis, output the analysis to CSV, feed the analysis through Logstash and into Elasticsearch, and make the analysis available through the Kibana dashboards, which if I do say so myself, look pretty nice. Going with the dark theme, was definitely the right choice.

Insight Dash

Our analysis is mostly flow based, with DPI allowing for application (layer 7) analysis. This provides metrics on applications above HTTP, like Salesforce, Google, CNN, Amazon, etc… This allows for a distinction between applications that are critical, and those that are not, and even those that are not allowed.

We also generate expert events on network behavior that may be the cause of network security and performance issues. All of this analysis is written to CSV files at a 1-minute interval. The interval can be changed by the user, but 1-minute is a reasonable default, providing the right balance of performance, history, and granularity for most network monitoring use cases.

But I digress. What I really want to talk about at the moment is our data, and what continues to amaze me about how it can be visualized in Kibana. The first step to this is knowing the data. During Insight 2.0, I thought I knew what data was going into CSV, and being picked up by Logstash pretty well. And I did know it well enough to put together a fairly rich set of dashboards. But in Insight 2.0 I was limited to the built-in visualizations, which limited my thinking about the data.

Since 2.0, I have been looking at Kibana plugins, which have really opened my eyes about different ways to visualize our data. And the great thing about these plugins, and really the reason I am writing this, is that they can be installed directly into an Insight 2.0 appliance, and used to create new and exciting dashboards and visualizations. And if you have Insight 1.0, the hardware for Insight 2.0 did not change, so you can easily upgrade to Insight 2.0 by going to the web config page. If your Insight has access to the Internet, it will inform you that an upgrade is available, and provide a button to push. If your Insight is not on the internet, there are easy instructions on the Insight portal to download the latest version, and upgrade the device.

Back to the Kibana plugins. There are two kinds of plugins that I have been experimenting with on Insight. One type, is an application plugin that has its own UI. Examples of these are Sense, Timelion, and Graph. These plugins cannot be used to create visualizations in a dashboard, but can be used to ask interesting multi-dimensional questions about your data, visualize the result in ways that look amazing, and may also give you some major insight about the behavior or your network. The other type of plugin adds visualizations that can be mapped to your data and added to dashboards. Some of these include Timeline, Sankey, and HTML. I even wrote my own, from instructions of course, that puts a real-time clock into my dashboard. I look forward to writing more of these type of plugins.

Now I am going to talk a little about the plugins I have played with, and the data I used in them. I recommend that you make the most of your Insight device and add these plugins as well. But before you add any of these plugins, you have to enable PERSIST, so that when you reboot, they will still be there. To enable persist, just open the /boot/grub/menu.lst file, and add the word PERSIST to the end of the kernel line, and reboot the device. Also, a word of caution. Installing Kibana plugins does require that you SSH into your Insight device, and run some commands, so you have to know at least some basics about things like Putty and Linux.

A list of both types of plugins I mentioned can be found on github: https://github.com/elastic/kibana/wiki/Known-Plugins. There are lots of others out there as well, and I suspect we will be seeing many more in the near future. So far my favorite visualization plugin is Swimlane. Swimlane was easy to install and apply to our network analysis on Savvius Insight. Below is a screenshot of the Swimlane visualization applied to application best response times.

Looks nice, right? And clearly Dropbox has the worst response time. But how did I create this visualization and map the application response times to it? Well, first I have to know what data is available. To understand that, I can go to the Kibana Discover tab and explore the data. The Savvius data is separated into different types that are prefixed with sv_. For application data, there is a type called sv_expert_apps. If you type ‘type:sv_expert_apps’ into the filter field, you will only see events of this type. You can then open one and see the available fields. For my Swimlanes visual, I just need the Name and Response Time fields. The available response time fields are Best Response Time, Worst Response Time, and Average Response Time.  Since we have filtered the events, let’s go ahead and save it as a search.  To do this, select the Save Search icon in the upper right and give it a name. Mine is called Expert Apps.

Now that we understand the data a bit and have saved a search, let’s head over to the Kibana Visualization tab. I have a couple of monitors, so I usually leave a browser open to the Discover tab showing my data fields, and open a separate browser window on another monitor to create or edit a visualization.  If you have already installed Swimlane, you should see it as a visualization choice in the Visualizations tab.

Select Swimlane, choose “From a saved search” from the “Select a Search Source” window, and select “Expert Apps” from the list of searches, if that is what you called your search for “type:sv_expert_apps”. In the visualization editor, select the Aggregation in the metrics section, which can be any one of the options provided in the pulldown menu. Having said that, Count does not make much sense.

In the Field pulldown, select any of the Response Time fields. And actually, it can be any of the number fields that are in the sv_expert_apps events.

In the buckets section, select Terms from the Aggregation pulldown menu, and Name.raw from the Field pulldown menu. You can also change the number of entries to display and whether they are displayed in Ascending or Descending order. Descending usually makes the most sense.

We are almost there. In the Time field section, use the defaults, which should be Sub Aggregation: Date Histogram, Field: @timestamp, and Interval: Auto.

In any of the sections, you can add a Custom Label and use the Advanced JSON Input to perform further calculations on the displayed data.

Finally, click the Green Arrow at the top. You should see the Swimlane visualization showing some number of application response times over time. Some visualizations have Options. In the Swimlane visualization, you can change the thresholds, or the color that will be displayed at different value ranges.

Now save that visualization, and either add it to an existing dashboard, or create a new one for it. I created a new dashboard, and added separate visualizations for Best, Worst, and Average Response Times. If you want to make your new dashboard easily accessible from the other dashboards, edit the Dashboards panel, and add it right in.

Well, I hope that was as fun and interesting for you as it was for me. I hope it gave you an idea about the power of knowing your data, and trying different Kibana plugins to visualize it. In my next write-up, I will show you how to make really great looking and insightful network graphs with the Kibana Graph plugin. Here is the teaser:


Screen 8

Written By :

Chris Bloom, Technology Evangelist, at Savvius