![]() The preceding sections outline a pattern that isn't too esoteric: we'd like to fetch JSON objects, most likely munge the data a little bit, and index it within Elasticsearch. One of Elastic's Source Code principles is that of Space, Time: room to experiment and learn with new and interesting approaches to problems. We can make this process more resilient, performant, and generally useful. But how can we make this better? By writing a small utility to do the work of fetching metrics and indexing them into Elasticsearch for us. This is great! We can send these metrics along to Elasticsearch to create custom dashboards in Kibana and even leverage alerts to keep an eye on key metrics. Typically, we would inject some time to wait between these two requests to let metrics aggregate between requests. The response to this request is a large JSON object containing metrics for various points of presence, including a Timestamp key to use for later requests: $ curl -H"Fastly-Key: $FASTLY_KEY" | jq. We use the tools curl and jq to send and parse responses: $ curl -H"Fastly-Key: $FASTLY_KEY" ![]() Per the documentation, we send a timestamp of 0 in the initial request. Given a service ID (an alphanumeric string), we can retrieve a snapshot of real-time metrics scoped to this service. The Fastly real-time metrics API is relatively straightforward. The real-time metrics API can help answer these types of questions. For example, we might want to find which region errors are spiking in, or determine how traffic volume has changed within the past hour in Austria. Gaining visibility into the behavior of specific points of presence can be critical when digging deep into problems, especially when offering content on a global scale. Log streaming with Elasticsearch provides a native logging integration, and the real-time metrics API offers collection of different statistics. Key Metricsįastly provides a few ways to gain better insight into your services. Keeping an eye on the performance and behavior of our CDN is important to ensure we're operating at the level we expect. One of these services is Fastly, which helps us optimize delivery to regions around the world. The Stack Infra team at Elastic uses many services to provide downloads for projects like Elasticsearch and Kibana to our users. Just i -> fromJust $ M.lookup (newIndex i) charsByIndexĪlso note that the last line can be reduced to: rotStr = map. Indices = foldr add M.empty $ zip alnum ĬharsByIndex = foldr add M.empty $ zip alnum Import qualified Data.Map as M (Map, empty, insert, lookup)Īlnum = ++ ++ ![]() Here's a possible implementation using Maps: import Data.List Again, this might not matter much, but you could use Data.Maps for lookup in order to get better performance. This means that the expression elem c alnum = alnum !! (mod (r + fromJust (elemIndex c alnum)) anlen) will go through the list 2 times. Things like elemIndex c alnum and alnum !! run in linear time in the size of alnum.
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