mitzen

Regardless of web / console / service app that you're running, you typically calling the same piece of code  which is teletmetry client, TrackMetric() for example and pass in the required parameter. The only difference is how to create your Startup code (entry point) code and started setting up your telemetry client) For web, you need only a single package     For function app v2, you need             And configure your FunctionsStartup, using the following codes :- For console app, the package required is here :-         You need the following startup code (which you easy create using dotnet new worker)

Bump into this error and thanks for stackoverflow, was able to resolve it by  Tools->Nuget Package Manager-> Package manager Console ->  And type,  "dotnet restore"  Or you can go into the command prompt of that project and type  "dotnet restore" Which ever work faster for you, i guess.

Sometimes you might send custom metrics data to Azure. A use case would be you trying to push metric information or signal that something has been processed and then it gets display in Azure monitor dashbaord. The following code snippet works using TrackMetrics whch is a appinsights libarary. And yes, the metrics (whatever metrics you created) is visible in App Insights. Sometimes it is like a witch hunt trying to figure out where the metrics would come out. For me, it took about a day before it is visible.

ASP.Net Core < ItemGroup > < PackageReference Include = "Microsoft.ApplicationInsights.AspNetCore" Version = "2.8.0" /> </ ItemGroup > Startup.cs public void ConfigureServices ( IServiceCollection services ) { // The following line enables Application Insights telemetry collection. services.AddApplicationInsightsTelemetry(); // This code adds other services for your application. services.AddMvc(); }

I think this is the best article that helps to import sub-directory as module, https://timothybramlett.com/How_to_create_a_Python_Package_with___init__py.html The only thing i wanted to say is, if you use __all__ = ['mymodule1', 'mymodule2'] That's to accomodate syntax such as this from sub-directory import * See the magically asterisk there...... all this is for this little baby. :)

Perhaps not the best of ways to work with optional interface, but here is something i come out with to prevent error coughing up with optional interface in C#8.

You should make use of pytohn io as defined here to work with stream data. So there are basically 3 main types of stream io which are text io, binary io, Instead of reading everything into memory, you are able to read this line by line as a stream.... import subprocess ## assuming r.csv is a large file self .streamedOutput = subprocess.Popen([ 'cat' , 'r.csv' ], stdout = subprocess.PIPE) self .sendOutput( self .streamedOutput) while True : executionResult = streamedOutput.stdout.readline() if executionResult: print (executionResult) else : break

Was having a issue trying to bind to a specific port on my machine. Then this command came about, :) sudo netstat - nlp | grep : 80

As simple as using the following command :- gunicorn --bind 0.0.0.0:8888 FalconRestService:api

You can start using vscode to step into python library / imports. All you need is to have it in the following configuration below. Once you have it in your launch.json, keep on pressing F11 :) { // Use IntelliSense to learn about possible attributes. // Hover to view descriptions of existing attributes. // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 "version" : "0.2.0" , "configurations" : [ { "name" : "Python: Current File (Integrated Terminal)" , "type" : "python" , "request" : "launch" , "program" : "${file}" , "console" : "integratedTerminal" , "justMyCode" : false }, ] }

Great link to setup kafka how it should be. Scalable and reliable.  This link is really useful. https://success.docker.com/article/getting-started-with-kafka However images no longer exsit, and requires some tuning :-  sudo docker service create --network kafka-net --name broker          --hostname="{{.Service.Name}}.{{.Task.Slot}}.{{.Task.ID}}"          -e KAFKA_BROKER_ID={{.Task.Slot}} -e ZK_SERVERS=tasks.zookeeper          qnib/plain-kafka:2019-01-28_2.1.0

Here a some quick sample code to work with json objects in python. Deserializing to object commands = json.loads(commandstrings) commandResultList = [] for command in commands: o = CommandType(**command) commandResultList.append(o) Serializing to Json import json cmd1 = CommandType( "list" , "ls" , "al" ) cmd2 = CommandType( "list" , "pwd" , "" ) cmd3 = CommandType( "list" , "ls" , "www.google.com" ) cmds = [cmd1, cmd2, cmd3] a = json.dumps(cmds, default = lambda o : o. __dict__ )

To squash last 3 commits git rebase -i HEAD~3 Then you get something like this, you need to change one of it to "pick". In this case, i pick "c8659b4" pick c8659b4 Merged PR 1914: ensure strongly type response messages are factor in. s 986cad8 Updated azure-pipelines.yml s bdb2086 1 2 3 As long as you have a single pick or all pick statement, it will be good. You should be able to rebase (squash) your commits

If you follow python celery first step from the official site, you probably gonna get some heart attack trying to get it work. Please use the following docker command : docker run -d -p 5672:5672 rabbitmq First you need to tell celery, which method you would like to register. This allows celery to provide task registration and execution to. This is an example of tasks.py from time import sleep from celery import Celery app = Celery( 'tasks' , broker = 'amqp://guest:guest@localhost:5672' ) @app.task def add ( x , y ): sleep( 4 ) print ( "executing stuff" ) return x + y Now that you have it registered, next is to run it. This queue "add" task (defined earlier) and put it into motion. from tasks import add add.delay( 4 , 4 )

When I go into Azure Monitor log diagnostics and setup what logs I would like to include in my workspace, I have difficulty figuring out what information I will be getting. To make matter worst, how do i know what type of activities i should be querying or appearing in my workspace. The thing is, it is hard. You can try to have a go at this site here..... https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/azure-monitor/platform/resource-logs-overview.md

I guess a common mistake is to load the right jar file when loading excel file. Yes, you have to use version 2.11 and not 2.12, :) You can try using the following command line pyspark --packages com.crealytics:spark-excel_2.11:0.11.1 And use the following code to load an excel file in a data folder. If you have not created this folder, please create it and place an excel file in it. from com.crealytics.spark.excel import * ## using spark-submit with option to execute script from command line ## spark-submit --packages spark-excel_2.11:0.11.1 excel_email_datapipeline.py ## pyspark --packages spark-excel_2.11:0.11.1 ## pyspark --packages com.crealytics:spark-excel_2.11:0.11.1 from pyspark.sql import SparkSession spark = SparkSession.builder.appName( "excel-email-pipeline" ).getOrCreate() df = spark.read.format( "com.crealytics.spark.excel" ).option( "useHeader" , "true" ).option( "inferSchema" , ...

When you are working with Az cli, you often need to pass in the correct data type. This can be pretty time consuming to figure out the correct syntax and what the data type looks like. For demo purposes, I assume we are working with an event hub Update resource  When you update, you need to use the following syntax. Notice the equal sign and setting the value 'Deny' to a nested properties called  properties.defaultAction . az resource update -n "myeventhubnamespace/networkrulesets/default" -g "myfakeresourcegroup" --resource-type "Microsoft.EventHub/namespaces" - -set properties.defaultAction=Deny Adding resources  When you add, you use the following syntax.  Notice no more equal sign . In this scenario, we wanted to add entries to an array / list of key value pairs. The syntax looks something like this :- az resource update -n "myeventhubnamespace/networkrulesets/default" -g "myfakeresourcegroup" --resource-t...

One way of connectg to mongodb database using mongodb (not the usual mongodb driver), is using the following codes. First it starts off with command line (to download driver from maven repository) and then run the code to connect and show. # Start the code with the followings.. # pyspark --conf "spark.mongodb.input.uri=mongodb://127.0.0.1/apptest.product?readPreference=primaryPreferred" \ # --conf "spark.mongodb.output.uri=mongodb://127.0.0.1/apptest.product" \ # --packages org.mongodb.spark:mongo-spark-connector_2.11:2.4.1 from pyspark.sql import SparkSession spark = SparkSession.builder.appName( "mongo-email-pipeline" ).config( "spark.mongodb.input.uri" , "mongodb://127.0.0.1/apptest.product" ).config( "spark.mongodb.output.uri" , "mongodb://127.0.0.1/apptest.product" ).getOrCreate() df = spark.read.format( "mongo" ).load() df.show()

Looking at different way you can import python modules Generally it begins by path to a module, for example, if you have a custom module, you can do something like this :: from email.emailer import * Hence 'from' keyword is for.  The asterisk tells python to import all. Don't forget the __init__.py file

Did some digging and found out that openssl does have some code example that you can find here :- https://github.com/appcoreopc/openssl/tree/master/demos/bio  (this is a forked repo) you can find recent codes from the master repo Have fun