As you can see, there are some reasonably comprehensible values, like `-0.50357585`, `-0.222`, `0.075432`, but there's also values like `-1.1984697154842467e-66` and `1.9052595476929043e-65`. Mathematically, these values end up being continous and suitable for generating a noisemap but for a human being doing development work and examining raw data, it's almost impossible to have any intuitive grasp of the numbers I'm seeing. Furthermore, when I pass these values to a visualization tool or serialize them to a storage format, I want them to be meaningful and contextually "sane". The noisemap values describe the absolute height of terrain at a given (X,Y) coordinate pair. If we assume that terrain hight is measured in meters, a world whose total height ranges between -1 meter and 1 meter isn't very sensible. A good visualization tool can accomodate this data, but it's not good enough for my purposes.
As you can see, there are some reasonably comprehensible values, like `-0.50357585`, `-0.222`, `0.075432`, but there's also values like `-1.1984697154842467e-66` and `1.9052595476929043e-65`. Mathematically, these values end up being continuous and suitable for generating a noisemap but for a human being doing development work and examining raw data, it's almost impossible to have any intuitive grasp of the numbers I'm seeing. Furthermore, when I pass these values to a visualization tool or serialize them to a storage format, I want them to be meaningful and contextually "sane". The noisemap values describe the absolute height of terrain at a given (X,Y) coordinate pair. If we assume that terrain height is measured in meters, a world whose total height ranges between -1 meter and 1 meter isn't very sensible. A good visualization tool can accomadate this data, but it's not good enough for my purposes.
To that end, I'm working on implementing a quantization function to scale the [-1,1] float values to arbitrary user defined output spaces. For example, a user might desire a world with very deep oceans, but relatively short mountain features. They should be able to request from the map generator a range of [-7500, 1000], and Quantize() should evenly distribute inputs between those desired outputs.
To that end, I'm working on implementing a quantization function to scale the [-1,1] float values to arbitrary user defined output spaces. For example, a user might desire a world with very deep oceans, but relatively short mountain features. They should be able to request from the map generator a range of [-7500, 1000], and Quantize() should evenly distribute inputs between those desired outputs.
@ -37,7 +37,7 @@ This query looks at the metric `http_request_duration_microseconds`, buckets it
## Labels
## Labels
Prometheus lets you apply labels to your metrics. Some are specificed in the scrape configurations; these are usually things like the hostname of the machine, its datacenter or geographic region, etc. Instrumented applications can also specify labels when generating metrics; these are used to indicate things known at runtime like the specific HTTP route ( e.g. `/blog` or `/images/kittens` ) being measured.
Prometheus lets you apply labels to your metrics. Some are specified in the scrape configurations; these are usually things like the hostname of the machine, its datacenter or geographic region, etc. Instrumented applications can also specify labels when generating metrics; these are used to indicate things known at runtime like the specific HTTP route ( e.g. `/blog` or `/images/kittens` ) being measured.
Prometheus queries allow you to specify labels to match against which will let you control how your data is grouped together; you can query against geographic regions, specific hostnames, etc. It also supports regular expressions so you can match against patterns instead of literal strict matches.
Prometheus queries allow you to specify labels to match against which will let you control how your data is grouped together; you can query against geographic regions, specific hostnames, etc. It also supports regular expressions so you can match against patterns instead of literal strict matches.
Nick Dumas
3 months ago