rps | Restore Point Simulator for Veeam | Continuous Backup library

React batches state changes, they do not happen in a predictable synchronous way. You need to wait for React to actually change the state before computing the score and use the setResult function.

This is where the useEffect hook comes in handy. The first argument is a callback and the second is dependency list. Basically, whenever an element of the list changes (the playerSelection and the CPUselect value in this case), React is going to call the function passed to the useEffect hook.

in order to switch to using make-series, you can replace this:

| summarize Throttled = countif(ResultType == "Throttled"), Total_Requests = count() by bin(Timestamp,1m)

with this:

| make-series Throttled = countif(ResultType == "Throttled"), Total_Requests = count() on Timestamp step 1m

I updated my answer to be a working correction of your original question. I have the db lines commented out for obvious reasons. You will just need to copy the new code in bot selections for PAPER and SCISSORS.

Yes, DynamoDB supports some of the highest-scale sites on the internet. Amazon published some metrics about Prime Day load.

https://aws.amazon.com/blogs/aws/amazon-prime-day-2020-powered-by-aws/

Amazon DynamoDB powers multiple high-traffic Amazon properties and systems including Alexa, the Amazon.com sites, and all Amazon fulfillment centers. Over the course of the 66-hour Prime Day, these sources made 16.4 trillion calls to the DynamoDB API, peaking at 80.1 million requests per second.

If you have a million RPS and each request is for 20 KB and you want them strongly consistent then you'd need to provision 5,000,000 RCU. If you're curious, you can calculate the cost based on us-east-1 pricing https://aws.amazon.com/dynamodb/pricing/provisioned/ which is $0.00013 per RCU-hour provisioned, so that's $650 per hour.

Write capacity and data storage have separate pricing.

You need the following query:

Monitoring different types of metrics (e.g. custom metrics) on most Kubernetes clusters is the foundation that leads to more stable and reliable systems/applications/workloads. As discussed in the comments section, to monitor custom metrics, it is recommended to use tools designed for this purpose rather than inventing a workaround. I'm glad that in this case the final decision was to use Prometheus and KEDA to properly scale the web application.

I would like to briefly show other community members who are struggling with similar considerations how KEDA works.

To use Prometheus as a scaler for Keda, we need to install and configure Prometheus. There are many different ways to install Prometheus and you should choose the one that suits your needs.

I've installed the kube-prometheus stack with Helm:
NOTE: I allowed Prometheus to discover all PodMonitors/ServiceMonitors within its namespace, without applying label filtering by setting the prometheus.prometheusSpec.podMonitorSelectorNilUsesHelmValues and prometheus.prometheusSpec.serviceMonitorSelectorNilUsesHelmValues values to false.

So finally after hours of debug I found the reason and solution.

First of all, I debugged size of the set each time I open it. And weirdly after first call its size becomes 0, so set is erased after the first call to ignite.set(). After this I switched to plain cache (instead of set) and just check cache.containsKey(user). Its size was persistent among the getOrCreateCache() calls but the NPO problem was still raised.

Then I found this tiny little answer on Ignite mailing list where it's said that Ignite caches implement AutoCloseable interface. Which means that after try-except block cache.close() is automatically called. And it means not just close the "connection" to cache but stopping the cache itself.

After this I changed my code to this:

You don't have to use a for loop. Instead of checking the whole message again, you only need to check the first argument (args[0]). You can simply use Array#includes and to make it case insensitive, convert this argument to lowercase. Your list already includes lowercase letters only, so the following will work:

There's three issues with your code:

1. open() takes in a second parameter that describes whether we want to read, write, or append to a file. In this case, we want to write to the file, so we should use open('score.txt', 'w') -- the w is for write.
2. You need to close your file after you're done writing to it using f.close().
3. The code to actually write to the file needs to be shifted over one indent. Otherwise, if we win, the score handling code will never be executed (since it's under an else branch that handles ties).

Here's the code with all three fixes: