ninjarobot

Tracing with Application Insights

Application logging is ubiquitous and invaluable for troubleshooting. Structured logging enables you to log formatted messages and the data fields separately so that you can see the messages but also filter on the data fields. Tracing takes this a step further, where you can correlate many log entries together as you follow a trace of execution through an application. Traces also include additional information about the execution process, such as the sequence of calls to dependencies and how long any given call may take.

Application Insights lets you see all of this data correlated together in an application. You can search for an error log and then see in the execution flow that the log entry was added right after a failed call to another service. Or you can see that a certain web request is slower than others because it spends a lot of time on many redundant data access calls.

What about OpenTelemetry?

alfonsogarciacaro

Deploying an F# ASP.NET Core app to Azure

Last week I spent a lot of time trying to deploy an F# ASP.NET Core app (a Giraffe app, specifically) to Azure because the information to complete all the steps was scattered in several places. So I'm writing this hopefully it will save the pain to others :)

Preparation

The following steps are mostly taken from this guide and it's only necessary to do them once:

1. Create an account in Azure (or use an existing one)
2. Create a resource group (or use an existing one)

enricofoltran

package main

import (
	"context"
	"flag"
	"fmt"
	"log"
	"net/http"
	"os"
	"os/signal"

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

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mathiasverraes

<?php
function it($m,$p){echo ($p?'✔︎':'✘')." It $m\n"; if(!$p){$GLOBALS['f']=1;}}function done(){if(@$GLOBALS['f'])die(1);}

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t