steam | rendering stack used by both Wagon and Station ( new name

I'm looking to broadcast some FMV games (eg. Her Story) to friends via Steam Broadcasting (to a Steam web page, NOT Steam Remote Play) so we can all play the game together with me acting as their fingers (in Her Story, for example, I'd be typing in the search terms).

This seemed to be going well, until we realised that there was a 13 second + delay on the stream, which was long enough to turn the entire exercise into a chore (people would be asking me to play a video while it was already playing on my side, etc).

By contrast, games which had Remote Play enabled ran with very minimal lag, making action games very playable over my connection which, according to the Google Internet Speed Test, is...

139.9Mbps Download | 25.7Mbps Upload | 3ms Latency - rated by Google as 'very fast'.

I'm using default settings for Steam Broadcast. Can anybody tell me how to improve the speed and reduce the latency for broadcasting games (ideally so that we can play something like Her Story via the Broadcast websites)? Mostly a networking and streaming newbie here, so any help is appreciated!

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How to add the JSON file that are nested to the datatable i created. I always encounter this problem:

DataTables warning: table id=tb_friendlist - Invalid JSON response. For more information about this error, please see http://datatables.net/tn/1

HTML

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I have a dataframe with 33 varialbles and 1 dependable variable. I need to perform two-way ANOVA test to see their impacts. Now I have to type vars manually:

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I am writing a parser bot for Steam that will keep track of which items come and go from a Steam user's inventory. I wrote a code that gets all the user's items and returns in the form of a dictionary with a nested list, where KEY = USER NAME, VALUE = ITEM NAME AND ITS QUANTITY. Now I need to compare Data1 and Data2 (updated data).

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Goal: To change a column of NAs in one dataframe based on a "key" in another dataframe (something like a VLookUp, except only in R)

Given df1 here (For Simplicity's sake, I just have 6 rows. The key I have is 50 rows for 50 states):

Index State_Name Abbreviation 1 California CA 2 Maryland MD 3 New York NY 4 Texas TX 5 Virginia VA 6 Washington WA

And given df2 here (This is just an example. The real dataframe I'm working with has a lot more rows) :

Index State Article 1 NA Texas governor, Abbott, signs new abortion bill 2 NA Effort to recall California governor Newsome loses steam 3 NA New York governor, Cuomo, accused of manipulating Covid-19 nursing home data 4 NA Hogan (Maryland, R) announces plans to lift statewide Covid restrictions 5 NA DC statehood unlikely as Manchin opposes 6 NA Amazon HQ2 causing housing prices to soar in northern Virginia

Task: To create an R function that loops and reads the state in each df2$Article row; then cross-reference it with df1$State_Name to replace the NAs in df2$State with the respective df1$Abbreviation key based on the state in df2$Article. I know it's quite a mouthful. I'm stuck with how to start, and finish this puzzle. Hard-coding is not an option as the real datasheet I have have thousands of rows like this, and will update as we add more articles to text-scrape.

The output should look like:

Index State Article 1 TX Texas governor, Abbott, signs new abortion bill 2 CA Effort to recall California governor Newsome loses steam 3 NY New York governor, Cuomo, accused of manipulating Covid-19 nursing home data 4 MD Hogan (Maryland, R) announces plans to lift statewide Covid restrictions 5 NA DC statehood unlikely as Manchin opposes 6 VA Amazon HQ2 causing housing prices to soar in northern Virginia

Note: The fifth entry with DC is intended to be NA.

Any links to guides, and/or any advice on how to code this is most appreciated. Thank you!

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I am currently pretty confused why Inertia.js is not reloading or rerendering my page.

I have a form that could be filled, once submitting this will execute:

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I have a dataframe as mentioned. Steam Pressure

I want to add a new column after doing simple math.

First I tried:

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I've got a code that runs as follows:

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We have a spark-streaming micro batch process which consumes data from kafka topic with 20 partitions. The data in the partitions are independent and can be processed independently. The current problem is the micro batch waits for processing to be complete in all 20 partitions before starting next micro batch. So if one partition completes processing in 10 seconds and other partition takes 2 mins then the first partition will have to wait for 110 seconds before consuming next offset.

I am looking for a streaming solution where we can process the 20 partitions independently without having to wait for other partition to complete a process. The steaming solution should consume data from each partition and progress offsets at its own rate independent of other partitions.

Anyone have suggestion on which streaming architecture would allow to achieve my goal?

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