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Amanda Saso, Sr. Tech Writer

Our Help? It’s in the Cloud.

06.16.2014 | Posted by Amanda Saso, Sr. Tech Writer

I like to fashion myself as a lower-level Cloud evangelist. I’m amazed at the opportunities the Cloud has afforded me both professionally and personally in the past four or five years. I tend to run head-first into any Cloud solution that promises to make my life better, and I’m constantly advocating Cloud adoption to my friends and family.

The consumer-level Cloud services that have developed over the past few years have changed how I relate to technology. Just like everyone else, I struggle with balancing work, mom duties, volunteer activities, and so on. Being able to keep my data handy simplifies my life–having records in the Cloud has saved me in several situations where I could just call up a document on my iPhone or iPad. No matter which Cloud app I’m using, I’m in the loop if I’m sitting at work or watching my kids at gymnastics (so long as I remember to charge my phone–there’s that darn single point of failure).

I respect Sumo for being a Cloud company that behaves like a Cloud company. We might have one physical server rattling around in an otherwise-empty server room, but I don’t know the name of it–I don’t ever need to access it. We run in the Cloud, we scale in the Cloud, we live in the Cloud. To me, that gives Sumo Logic an uncommon brand of Cloud legitimacy.

So what does all this have to do with Sumo Logic’s Help system? I started making noise about moving our online Help into the Cloud because I wanted the ability to dynamically update Help. At the time, my lovingly written files were somewhat brutally checked into the code, meaning that my schedule was tied to the engineering upgrade schedule. That worked for a while, but as we trend towards continuous delivery of our product, it wasn’t scaling. I knew there had to be a better way, so I looked to the Cloud.

My sense of urgency wasn’t shared by everyone, so I made a fool of myself at a Hack-a-Thon, attempting to make it happen. It was an epic failure, but a great learning experience for me. Knowing that I could spin up an instance of whatever kind of server my little heart desired was a game changer–what was once something that required capital expense (buying a Linux box or a Windows Server) was now available with a few clicks at minimal cost.

Within a month or so, I had convinced my manager of the legitimacy of my project. Eventually our Architect, Stefan Zier, took pity on me. He set up an S3 Bucket in AWS (Sumo runs in AWS, so this is a natural choice), then configured our test and production deployments to point to the URL I chose for our Help system. The last bit of engineering magic was leveraging an internal engineering tool that I use to update the URL for one or more deployments. Within a few days it worked. I now can push updates to Help from my own little S3 Bucket whenever I like. That is some awesome agility.

To those who are not tech writers, this may seem unremarkable, but I don’t know any other organizations with Cloud-based tech pubs delivery systems. I couldn’t find any ideas online when I was trying to do this myself. No blog posts, no tools. It was uncharted. This challenge really lit a fire under me–I couldn’t figure out why nobody seemed to be delivering Help from the Cloud.

The Cloud also improves the quality of my work, and grants me new options. Using an S3 Bucket means that I can potentially set up different Help systems for features that are only accessed by a subset of customers. I can take down anything that contains errors–which very, very rarely happens (yeah, right). I can take feedback from our Support team, Project Managers, Customer Success Team, Sales Engineers, and even from guys sitting around me who mumble about things that are missing when they try to write complicated queries. (Yes, our engineers learn about Sumo Logic operators using the very same Help system as our customers.)

Here’s the best part. As our team of tech writers grows (it’s doubled to two in 2014!), I don’t need an IT guy to configure anything; my solution scales gracefully. The authoring tool we use, Madcap Flare, outputs our Help in HTML 5, meaning that the writers don’t need any IT or admin support converting files, nor hosting them in a specific way. (Incidentally, when you check out our Help, everything you see was customized with the tools in Flare, using, of all things, a mobile help template.) Flare has earned a special place in my heart because my deliverables were ready for Cloud deployment; no changes in my process were needed.  There are no wasted resources on tasks that the writers are perfectly capable of performing, from generating output to posting new files. That’s the great part about the Cloud. I can do myself what it would take an IT guy to handle using any on-premise server solution.

Funny, that sounds just like Sumo Logic’s product: Instead of wasting time racking servers, people can do their job right out of the gate. That’s value added. That’s the Cloud.

Amanda Saso, Sr. Tech Writer

Logs and laundry: What you don’t know can hurt you

05.29.2013 | Posted by Amanda Saso, Sr. Tech Writer

Have you ever put your cell phone through the wash?  Personally, I’ve done it. Twice.  What did I learn, finally?  To always double-check where I put my iPhone before I turn on the washing machine.  It’s a very real and painful threat that I’ve learned to proactively manage by using a process with a low rate of failure.  But, from time to time, other foreign objects slip through, like a lipstick, my kids’s crayon, a blob of Silly Putty—things that are cheaper than an iPhone yet create havoc in the dryer.  Clothes are stained, the dryer drum is a mess, and my schedule is thrown completely off while I try to remember my grandmother’s instructions for removing red lipstick from a white shirt.  

What do low-tech laundry woes have to do with Sumo Logic’s big data solution? Well, I see LogReduce as a tool that helps fortify your organization against known problems (for which you have processes in place) while guarding against unknown threats that may cause huge headaches and massive clean-ups.

When you think about it, a small but messy threat that you don’t know you need to look for is a nightmare. These days we’re dealing with an unbelievable quantity of machine data that may not be human-readable, meaning that a proverbial Chap Stick in the pocket could be lurking right below your nose. LogReduce takes the “noise” out of that data so you can see those hidden threats, problems, or issues that could otherwise take a lot of time to resolve.

Say you’re running a generic search for a broad area of your deployment, say billing errors, user creations, or log ins. Whatever the search may be, it returns thousands and thousands of pages of results. So, you could take your work day slogging through messages, hoping to find the real problem, or you can simply click Log Reduce. Those results are logically sorted into signatures–groups of messages that contain similar or relevant information. Then, you can teach Sumo Logic what messages are more important, and what data you just don’t need to see again. That translates into unknown problems averted.

Of course your team has processes in place to prevent certain events. How do you guard against the unknown? LogReduce can help you catch a blip before it turns into a rogue wave. Oh, and if you ever put Silly Putty through the washer and dryer, a good dose of Goo Gone will do the trick.

Amanda Saso, Sr. Tech Writer

Mapping machine data (pun intended)

01.25.2013 | Posted by Amanda Saso, Sr. Tech Writer

When you’re talking analytics, who said that an unfair advantage has to be ugly? Our newest feature is drop-dead gorgeous:


What you’re seeing is the result of a geo lookup query, which matches extracted IP addresses to their geographical location–another troubleshooting tool from Sumo Logic. (If you’re ready to skip right to the good stuff and start using this feature, see our Knowledge Base article here.)

Geo lookup queries use four Sumo Logic search language components: IP addresses are parsed, then the lookup operator compares the extracted IPs against a hosted IP geolocation table. The count and sort aggregate functions order the data; using these aggregate functions allows you to add a map to a Dashboard. The results are plugged in to the Google Maps API, and in a few seconds you’ve got a map showing the location of IP addresses. The syntax looks like this:

| parse “remote_ip=*]” as ip_address
| lookup latitude, longitude, country_code, country_name, city, postal_code from geo://default on ip = ip_address
| count by latitude, longitude, country_code, country_name, city, postal_code
| sort _count

It’s important to note the flexibility of geolocation fields that you can choose to use in geo lookup queries. Longitude and latitude are required, but the hosted geolocation table includes fields for different levels of granularity, such as country_name, postal_code, and area_code; depending on the area of the world you’re concentrating on, you can pick and choose which fields make sense in your query.

I also like using the familiar Google Maps interface–there’s no learning curve. The zoom slider/control is displayed both in the Search page, and in a Dashboard:


In addition, clicking one of the markers on a map immediately zooms down to street level, meaning that you don’t have to worry about zooming on the wrong area:


To learn more about using geo lookup queries to build maps, see Mapping IP addresses with geo lookup queries in the Sumo Logic Labs beta feature section of our Support Portal. While you’re there, be sure to drop us a line!

Or, get started now using Sumo Logic Free!