timely | Accumulo backed time series database | Time Series Database library

I am currently working on visual studio C# to make a windows service.

I exported the project as "Release" "Any Core" and tried it on my computer, and it worked fine using :

• sc create myService binPath= C:/myDocs/theService.exe
• net start myService

I then copied it to my virtual machine (itself on a server that I connect to using distant desktop)

when doing the same commands on the virtual machine it tells me

I have successfully ported my desktop application from Dagger/Spring to Micronaut/Spring (there's just way too much Spring code in there for me to be able strip it all out in a timely manner). Using Micronaut and PicoCLI everything is working beautifully in Eclipse/Gradle. It's launching, tests are passing, so now I want to do a build and install it to make sure that everything will work for our customer.

Our build (via Gradle) uses Shadow (com.github.johnrengelman.shadow) to combine our application with all of its dependencies into a single Jar file. I've checked and all expected dependencies appear to be present, and from the look of it the generated Micronaut classes are also present in the shadow jar. We then use Launch4J to make this shadow jar executable. From everything that I can tell, the output from Launch4J looks to be correct, however when I try to run the installed application, the splash screen appears for a fraction of a second and that's it. When I run the Launch4J generated executable as a jar file, I get the following:

I have an app that I haven't updated in like a year or so, today I make 1 tiny change, doesn't break anything, but when pushing to master I got a lot of stuff... as you'll see below. Did some Googling, noticing it may be a node version issue, so I added to heroku buildpack so the result is like so, having the heroku/nodejs buildpack first is what allows Heroku to use my specified versions in packages.json rather than its defaults:

When I attempt to run my .Net Web Application as a Windows Service, it does not start. It works if I just run the executable but mysteriously fails when attempting to run as a service. I have logged the relevant error messages and reported my solution below.

The following exception is in the event viewer.

Application: WebApplication5.exe CoreCLR Version: 6.0.222.6406 .NET Version: 6.0.2 Description: The process was terminated due to an unhandled exception. Exception Info: System.NotSupportedException: The content root changed from "C:\Windows\system32" to "C:\Program Files (x86)\My Company\webapp5". Changing the host configuration using WebApplicationBuilder.Host is not supported. Use WebApplication.CreateBuilder(WebApplicationOptions) instead. at Microsoft.AspNetCore.Builder.ConfigureHostBuilder.ConfigureHostConfiguration(Action1 configureDelegate) at Microsoft.Extensions.Hosting.HostingHostBuilderExtensions.UseContentRoot(IHostBuilder hostBuilder, String contentRoot) at Microsoft.Extensions.Hosting.WindowsServiceLifetimeHostBuilderExtensions.UseWindowsService(IHostBuilder hostBuilder, Action1 configure) at Microsoft.Extensions.Hosting.WindowsServiceLifetimeHostBuilderExtensions.UseWindowsService(IHostBuilder hostBuilder) at Program.$(String[] args) in C:\Users\Jonathan\source\repos\WebApplication5\WebApplication5\Program.cs:line 10

My application is based on the ASP.NET Core Web API template. I followed instructions on https://docs.microsoft.com/en-us/aspnet/core/host-and-deploy/windows-service?view=aspnetcore-6.0&tabs=visual-studio#app-configuration and added UseWindowsService() to Program.cs

I am trying to use a USB 3 capture card with GStreamer, however the only exposed resolution on the capture card seems to be 1080p60 and this seems to be too much data for my Coral Dev Board to handle, convert and process through object detection in a timely manner.

I have used a USB 2 card at 480p30 and this works but would like something a bit higher. I have tried two USB 3 cards, an Elgato game capture hd60 s+ and a pengo 1080p grabber, both of which seem to have the same issue.

When I use my USB 2 card, it exposes multiple different resolutions with different framerates, both in OBS on windows and when listing available formats on Linux, however, both USB3 cards only expose 1080p60.

I get very slow, laggy inference at 1080p60, and the program crashes with any other parameters, including 1080p30, 720p60, and 720p30. I think 720p30 would be ideal, however am unsure how to achieve this.

I have been using this script to run inference on a coral dev board 4GB. I would like to stick to python if possible.

Warning when 1080p60 is used, as well as being slow and laggy:

There are a couple of notable packages on pub.dev that offer video compression. I've tried them, and other sketchy packages, and none work well once a video gets around 300MB. They crash or have other issues on various platforms and hardware. Namely, video compress and light compressor. The GH commits and support are concerning as well on the packages I've seen for video compression in pub.dev. PR's not being pulled in and issues not being resolved in a timely manner and some quite serious for recent android APK updates. So not something I want in my dependency stack.

I am uploading to Google Cloud Storage using FlutterFire. While my code does upload using FireBaseStorage upload task it does not have any ability to compress on the client side or handle background uploading when the app is closed.

So, currently on the server side, I have a GCF that triggers on file uploaded. Then I use nodejs ffmpeg, which is baked into GCF's to compress server side and convert to H264. And finally delete the original large upload video and save the compressed video to storage.

This solution works, but depending on a user's connection and whether they are on wifi, can take an awful long time and when it fails or the user closes the app, my current solution is useless.

I wish there was a solid native library on Android and iOS, that I could tap into, to confidently perform compression and conversion from any format to H264 and also allow uploading, whether my app is closed or in the background, to GC storage. Any thoughts? I wish this was standard in FlutterFire's cloud storage handling!

I have yet to test flutter_ffmpeg, but only because some have said it runs so slowly on client. So again, Flutter/Dart can access natively written code, but I don't know where to start on Android/iOS to do this the right way. And I understand this is what some of the packages are doing, but they do not work with large videos, so I'm hoping someone can point me in the right direction on Android and iOS.

My code for handling upload tasks to GC storage.

Good morning, Since yesterday, I'm having timeouts doing requests to ebay website. The code is simple:

I'm getting error string index out of range when I getting simple text from post request and want to show data in array.

For a University assignment, I have to implement a simulation of the Raft protocol in Akka (I am using Akka typed, using Behaviors). In the Raft protocol, interactions between actors have a 1:1 mapping between a request and a response; responses must be delivered in a timely manner. Therefore, it makes sense to use the ask pattern as demonstrated by the documentation in the Request-Response with ask between two actors example.

In my implementation, requests and responses must be context-aware: this means that, when an actor that performed a query receives a response, it must know what query the response was for. The example in the documentation suggests to include a query ID in the message.

What I need to solve can be described with the following example:

• Actor A sends a query with ID=1 to actor B (it includes the query ID in the message).
• B does not reply in time (the network, or B itself, may be slow), thus A re-issues a query with ID=2 to B.
• Actor B receives the query with ID=1, and replies to actor A (including the query ID in the message).
• Actor A receives B's reply with ID=1. A knows that the last query it sent had ID=2, thus must NOT process the reply but wait for the one with ID=2.

I think that, to "filter" replies that do not have a correct query ID, I can put a BehaviorInterceptor in actor A that checks that the ID in the reply matches the expected query ID.

To summarize:

• Actor A writes in a hashmap the query ID to be expected from actor B's next reply,
• The interceptor uses this hashmap to check the ID in the reply. Is this a good design?

Moreover, I don't understand whether ask is blocking or not. Ideally, I would like to use ask in a non-blocking way: actor A asks actor B, and, while waiting for B's reply, A can do other operations. While waiting for B's reply, actor A can also change its behavior if needed (also a Behavior that does not handle B's replies).

Thank you for any insight!