concurrent | Concurrent execution strategies for JVM and Android | Architecture library

First time actually using anything to do with swing - sorry for the poor code and crude visuals!
Using swing for a massively over-complicated password checker school project, and when I came to loading in a JMenuBar, it doesn't render properly the first time. Once I run through one of the options first, it reloads correctly, but the first time it comes out like this: First render attempt
But after I run one of the methods, either by clicking one of the buttons that I added to check if it was just the JFrame that was broken or using one of the broken menu options, it reloads correctly, but has a little grey bar above where the JMenuBar actually renders: Post-method render

The code for the visuals is as follows:

I'm struggling to use the Micronaut HTTPClient for multiple calls to a third-party REST service without receiving a io.micronaut.http.client.exceptions.ReadTimeoutException

To remove the third-party dependency, the problem can be reproduced using a simple Micronaut app calling it's own service.

Example Controller:

I am trying to learn how python handles multiprocessing and have followed a youtube tutorial for some basic code but I am now trying to implement a ProcessPoolExecuter myself.

I have the following code which is causing the problem:

I am trying to run a test case which basically copies a file from my machine to a mock server running in docker. The same test works fine on Mac and Ubuntu. But on Windows it's getting failed with the following error:-

I need to push messages to external rabbitmq. My java configuration successfully declares queue to push, but every time I try to push, I have next exception:

I am trying to contribute to a Github Page/Jekyll site and want to be able to visualise changes locally but when I run bundle exec jekyll serve but I get this output:

We are using stream ingestion from Event Hubs to Azure Data Explorer. The Documentation states the following:

The streaming ingestion operation completes in under 10 seconds, and your data is immediately available for query after completion.

I am also aware of the limitations such as

Streaming ingestion performance and capacity scales with increased VM and cluster sizes. The number of concurrent ingestion requests is limited to six per core. For example, for 16 core SKUs, such as D14 and L16, the maximal supported load is 96 concurrent ingestion requests. For two core SKUs, such as D11, the maximal supported load is 12 concurrent ingestion requests.

But we are currently experiencing ingestion latency of 5 minutes (as shown on the Azure Metrics) and see that data is actually available for quering 10 minutes after ingestion.

Our Dev Environment is the cheapest SKU Dev(No SLA)_Standard_D11_v2 but given that we only ingest ~5000 Events per day (per metric "Events Received") in this environment this latency is very high and not usable in the streaming scenario where we need to have the data available < 1 minute for queries.

Is this the latency we have to expect from the Dev Environment or are the any tweaks we can apply in order to achieve lower latency also in those environments? How will latency behave with a production environment loke Standard_D12_v2? Do we have to expect those high numbers there as well or is there a fundamental difference in behavior between Dev/test and Production Environments in this concern?

I followed the instructions at Structured Streaming + Kafka and built a program that receives data streams sent from kafka as input, when I receive the data stream I want to pass it to SparkSession variable to do some query work with Spark SQL, so I extend the ForeachWriter class again as follows:

I have a question around how best to manage concurrent job instances in AWS glue.

I have a job defined like so: