Alayshia Knighten [Sr. Implementation Engineer]:
Hello everyone, my name is Alayshia Knighten. And today we’ll be discussing Honeycomb Exploring and Collaboration. Now let’s beeline in. In this section, we will discuss permalinks, dataset details, sidebar, board, Slack, and query template links. Let’s deep dive into permalinks. At the top of every query, of course, is a URL. Every URL is going to be a permalink, which means that it is unique only to that specific query. The cool part is that you can drop it in Slack, place it in a document, reference it in support, or whatever else tickles your fancy. Regardless, whenever this URL loads, its graph will always appear exactly the way it did when it originally ran because it’s cache forever. You may lose the ability to view the raw dataset at some point because that underlying data has aged out. But at Honeycomb, the graph and the permalink will last forever.
So guess what? We have never cleared them out. Smiley face. Oh, it also gets even better than that. Let’s say your coworker created this beautiful graph with a bunch of things he or she cares about, and you want to refactor this query for whatever odd reason. Although you’re using their permalink, it does not change the graphical presentation of that permalink. Actually, a new permalink is available and you can share that URL. You can iterate your eyeballs out as everything is immutable and shareable. Quite often when investigating stuff, we all have the tendency to either hit dead ends or take that mental break. What if there was a query that you did not save the permalink for, but you actually need it? Well, you’re in luck. We do keep track of all the queries that you have ever run in a dataset, and you can see them all in a query history sidebar. So potty break, mental break, no problem. You can easily pick back up where you were from the previous good state and continue to iterate from there.
The clocks are representative of the history. They’re located here or here. We’re going to go here. Once we click on the history, I can actually see my history from today, yesterday, way before that. I can also see from a graphical perspective, the graphs that I ran, whether it was a visualize count, or a heat map, or whatever it was. I can also see the filtering as well. So I can see if there is a where, or group by, and things of that nature. The cool part is, no matter what I’m doing, if it was from yesterday, I can go back to it. I can click on reference points or whatever the case may be. So simplistically, you can easily pick back up from where you were previously in that good state and continue to iterate from there. If you were running queries from, let’s just say five, six months ago from that same incident that popped back up, you can literally go back to that. And have that aha moment and say, Oh, well, hmm this is what happened. I can use this to see what the problem was.
If we really want to, we can also name queries and describe them as well. So other folks, our future folks can even understand what the query represents. I can literally go at the top of my query and type whatever I want to name it. And write a description. And save. Run that query again. Voila, what do you see? Whatever I want to name it. All of this brings up a valid tip. Let’s say you have a subject matter expert for X application but have no idea what they did to find the issue that was resolved. Well, you can literally go back and see what queries that expert ran to follow along with the investigation, or put your own two cents in. To do that, I can literally go to team activity. From here, I can see what my teammates have ran. And if I want to load more, I can load more as well. I can literally step into it if I’d like and it will show me exactly what they saw. And the data associated with that. So this is really collaboration made easy. It also makes having the ability to look at past incidents, explorations, et cetera, easy as well. You also have leverage if something comes up.
Let’s talk about the details of the dataset. When you’re interacting with the dataset, we’ll include overview information about that dataset on the right-hand side. So I’ll go ahead and click the information button, by selecting the information button, it gets us to the dataset details. This will include overview information about the dataset, like its name and type. So for example, I’m looking at a string action. We can even filter. Let’s say, I’m searching for “has name,” as I’m typing, it is actively searching for me until it comes up with a specific value. There’s also the ability to add a description to each field. Sometimes certain fields are self-explanatory. Other times, well, fields are not as obvious as we would hope them to be. So by adding that description, we avoid having our coworkers upset with us in our naming selections.
So to add a description, we can add it here and save. You can also see the recent values, as well as, the last time data was received. And when was the last time any of this was edited? There’s also the option of boards. Boards are a way of letting folks group a handful of queries and view them all at once. They’re meant as jumping-off points. Clicking on each one would drop you into the query builder and you can continue investigating to your heart’s content. Simply put, boards are a great way for specific service owners and other stakeholders to express what they care about, or what’s an interesting thing to them. We can directly add to board here. Clicking the add to board, we can click which board we would like to add to and create a description here.
You also have options of displaying only the graph, displaying the graph and table, as well as displaying the table only. You can also click on the left-hand side to click into boards. Once in boards, you can see all of the boards known to you, as well as the public view, the list view, and the visual view. I’ll click into one. Here, for example, we’re looking at Irving’s useful queries, where he has a bunch of queries that he finds useful. You can see an overview of all of those queries however he wants them to be displayed. As well as the original time range. And you can change the time range as well.
We also have a really cool Slack integration that you can literally send your query directly to any Slack channel. If you do the Slack integration, it will literally show you the graph plus key information for the data with comments you add. To do so, I can just simply click the share button, indicate what Slack channel I would like to put it in. Type up a quick note if I like. And then press send. Once sent, it’ll properly be displayed. And this is typically what it looks like after it’s been received by Slack.
Also, keep in mind, you can also simply just drop the permalink straight into Slack and the graph will On pro with useful information. Let’s talk about the get template link for just a second. It lets me directly link a query that we want to load. This is useful if you’re trying to say swap out filter values on the fly or track useful Honeycomb links in a playbook. For example, if you have another incident management system and you need the ability to template a Honeycomb query and stick it right in there, you can do that. That’s what differs from templates and permalinks. Template links will run that query as if it were a new query. Once again, I’m Alayshia Knighten, with Honeycomb Exploring and Collaboration. As always go beeline in.
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