Alayshia Knighten [Sr. Implementation Engineer]:
Hello, everyone. My name is Alayshia Knighten, and today we’ll be discussing Honeycomb Dataset Tools. Now, let’s beeline in. Let’s take a look at the overview page for the dataset. Keep in mind, these are admin-like functions. To get there, you select the dataset icon. Once in the dataset icon, select the settings for the specific dataset. So we have landed on the overview page. At the top of the overview page, you will see a section for descriptions. Right under descriptions are suggested queries. The suggested queries feature allows you to select a board to provide suggested queries to for that set.
In the next section are retention and usage. There is also the daily event trackers for the last 60 days. Also, if you scroll to the bottom, you can see the event latency history, as well as the event rate. Just so you know, many people get a little nervous with the latency. The latency we are showing you is actually a max calculation. What it’s doing is that it’s calculating the difference between the timestamp on an event and the current time as shown in our API. So basically, it’s comparing time.now when the API first gets it. So we’ll scroll up to the schema tab. So in the schema tab, there are unique fields. If we select the arrow to expand that for each of the fields, we can change the display name if we choose to, else it defaults to the field name. We can change the type if we like. Changing the type could be important in certain situations. For example, if we incorrectly identified an energy field as a string, we can then go in and fix that. We can also hide fields as well as add descriptions. If a field was edited, we can see that. We can also see the last time data was received for this field. If there is a max length, we can add that as well.
We also point out to you stale fields, which are fields that have not been seen within the retention window. In this case, the standard retention is 60 days, so 60 days. If you ever accidentally leak PII into a field and you need to delete it, you cannot do that yourself. Still, you can open a support ticket at Honeycomb, and we can purge any field that you need the PII removed from. Let’s talk about derived columns. Our derived column builder lets you craft expressions that allow you to do all kinds of things, including combine ands, ors, et cetera. Think about it as the little Microsoft Excel builder, in a way, where you use parentheses and commas to build your expressions. You are able to operate on any existing field and build complex expressions that you can output a Boolean or some kind of string or integer value from that. You could also do regex matching, which Our Docs talk about. Our regex is basically Go regex.
So for each existing derived column, we see a display name, the actual expression, the type, the description, and when it was last edited. And if you want to add a new one, you simply click here. nd as previously stated, you can go to Our Docs, and we have a link here for you to access it directly. Now let’s go to the definitions tab. In the definitions tab, we will show you a cool visualization of how many home and tracing fields have been mapped. Usually, if those fields are coming from Beeline, then we typically have captured them correctly. If it is coming from something else like open telemetry, we may not be as accurate on that. So here is where you can set up those mappings.
So for example, let’s look at user. This is the user field, and it’s a string name. If it needs to be something different, we can simply exit out and apply the correct thing and then press update. Let’s go to the SLO tab. The SLO tab is where you set up, view, modify, and delete SLOs. I talk about SLOs in a different video. You can refer to that video in regards to setting up, modifying, and deleting SLOs. Here is the trigger tab you can set up, view, and modify triggers. I have talked about triggers in a different video. You can refer to said video regarding setting up, modifying, and deleting triggers.
Here is the markers tab. At Honeycomb, we use markers for deployments. You can configure different types of markers that will have a color. We also conveniently show you how to send markers and how to annotate any dataset with either a cURL or our Honeycomb marker tool. Triggers can also add markers to a dataset. Deployment markers are beautiful. You can also add custom markers with labels like “that time Alayshia crashed everything” marker. It is stored through your data retention window so that you always have those. Last but not least, there is the delete tab where you can simply delete the dataset. I strongly suggest talking to people before doing so. Once again, I am Alayshia Knighten with Honeycomb Dataset Tools. As always, go beeline in.
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