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March 24, 2021
Back in the day, I LOVED me some lookups. Being able to add lookup tables in Splunk was a huge win for me: adding context to events for things like cost centers for systems, GeoIP tagging and environment/dependencies helped make data far more useful. Later on, when I was living in a multiple-tool world, I obsessed over the fact that I could do that enrichment in Cribl LogStream, and have the enriched data be consistent in every system.
Unfortunately, if the data you want to use is of any considerable size, CSV files hit their limitations pretty quickly. Performance suffers, and pretty soon you’re avoiding enrichment, because the benefit no longer outweighs the self-flagellation.
So imagine my glee when I found out that the Redis function made it into the LogStream 2.4 release. At this time, I had been talking to a company called GreyNoise about partnering with them to use their solution with LogStream. If you’re not aware of them, definitely check them out. They run a fleet of sensors around the internet listening (and categorizing) all of the internet’s noise. There is constant background noise on the internet – scans, attacks, and just day-to-day traffic patterns. The intent of GreyNoise is to weed out all of the “benign” noise – meaning traffic that is not a threat – to allow you to focus only on the events that matter.
Of course, I’m thinking about workflows with things like AWS VPC Flow Logs or PAN firewall logs, and using the GreyNoise data to decide whether to send events to my log/metrics systems. As you can probably imagine, GreyNoise captures a ton of data: just pulling 1 day’s worth of IP-address–categorized data can yield around 350K records. Calling an “offsite API” in a LogStream pipeline is really not feasible, as it would slow down the pipeline considerably (just by latency alone, no fault of the API). So I exported the GreyNoise data to a CSV file, and while it worked, it crushed my test system.
So, fast forward to 2.4, now with Redis! Now, I can have a Redis instance sitting right next to my LogStream worker group (so I don’t add significant latency), and I can feed all of that data into the Redis instance, and use it as a lookup in my pipelines. Since it’s Redis, I can feed the data to the cluster any way I want – I can run a script on my laptop, run an AWS Lambda function, or use LogStream’s Data Collection feature.
I set about using all these great tools at my fingertips to help me tame my VPC Flow Logs. We run a number of internal workloads in AWS, and of course, we enable and capture VPC Flow Logs. If you’ve ever looked at VPC Flow Logs, you know there can be a LOT of data to sift through. So I decided on what I needed to do:
I am going to end up with something along these lines:
A couple things of note here:
I’ll be covering the details of building this in detail in part 2 of this blog post, but for now, in the spirit of the Underwear Gnomes, let’s skip task 2 and get right to the profit!
First, the metrics – Here’s a snapshot of the extremely simple dashboard I built for this:
In this case, we’re seeing four of our accounts, with breakdowns of their traffic by source IP (bottom) and destination IP (top). If you look closely, you can see that during the period we’re looking at, the overwhelming majority of our traffic is benign, internal, or “no data” (which is a designation I add for traffic from/to IP addresses that GreyNoise doesn’t see, meaning most of our legitimate traffic). For the purposes of this effort, I’m considering that “not interesting” traffic, but depending on what I’m trying to do, I might omit it.
So, now if I look at my events – first, looking at the stats from my LogStream S3 collector – I see that it collected over 3.6 million events.
However, if I look at the events in Splunk over the same time period, I see that the pipeline only sent 347,678 events. That means my filtering reduced the events going into Splunk by a bit over 90%.
Clearly, in this scenario, I’m using this enrichment data to make routing and reduction decisions in my pipeline. This is where having all of this data be “replayable” via LogStream’s data collection feature comes in really handy. If I decide that I actually want to include those “no data” events, I simply change the pipeline to pass those through, and re-run the collection (you’ll probably want to delete the events and metrics in Splunk first, though).
In the next installment of this two-part series, I’ll walk through the steps to set this up, including LogStream configuration and other details.In the meantime, if you want to either learn or refresh your memory about LogStream’s enrichment capabilities, I suggest you go run through the Enrichment sandbox, the Lookups Three Ways sandbox, or both.