compare | source code of How to Generate a Good Word Embedding | Recommender System library

For columns with only strings, you can use pandas df.equals() that compares two dataframes or series (cols)

If you're going to use the CompareOptions enum, I feel like you might as well use it with the CompareInfo class that it's documented as being designed for:

Defines the string comparison options to use with CompareInfo.

Then you can just use the GetHashCode(string, CompareOptions) method from that class (and even the Compare(string, string, CompareOptions) method if you like).

You can find the cost per resource in the cost management tab in the Azure portal then in cost analysis

You could also see the amount of messages in the service bus metrics tab, just filter by incoming messages and there you will see them, this metric is available only for 30 days so you won't be able to see previous months metrics unless you saved them in a storage account or any SIEM.

To compare, you first need to put your condition inside while (reader.Read()){} . then add the column number of your table to reader[].toString() in your if-else condition. learn more about Database adapters before you do these!

You have not specified which platform you're on!

Just to address your attempt, the correct syntax for that would be

if (a == b || a == c || a == d || a == e || a == f || a == g || a == h || a == i) ....

But depending on your use case, it's probably advisable to loop over the array instead.

You need to implement the __eq__ and __lt__ methods to allow you to sort the objects and then compare them:

As given in this answer, Storms Internal Metrics are just estimated by a percentage of the real data flow. Initially, it uses 5% of incoming tuples to make those estimations. This may lead to inaccuracies for extreme high or low throughputs.

EDIT: The documentation describes the following:

In general all of these tuple count metrics are randomly sub-sampled unless otherwise stated. This means that the counts you see both on the UI and from the built in metrics are not necessarily exact. In fact by default we sample only 5% of the events and estimate the total number of events from that. The sampling percentage is configurable per topology through the topology.stats.sample.rate config. Setting it to 1.0 will make the counts exact, but be aware that the more events we sample the slower your topology will run (as the metrics are counted in the same code path as tuples are processed). This is why we have a 5% sample rate as the default.

EDIT 2 In this post, there is more information about the estimation:

The way it works is that if you choose a sampling rate of 0.05, it will pick a random element of the next 20 events in which to increase the count by 20. So if you have 20 tasks for that bolt, your stats could be off by +-380.

By the way, execute_count is just an increasing number, while your tuple_inRate.count is a rate, isn`t it?

Barring the undefined behavior which can be easily be fixed, you're seeing the effect of denormal numbers. They're extremely slow (see Why does changing 0.1f to 0 slow down performance by 10x?) so in modern FPUs there are usually denormals-are-zero (DAZ) and flush-to-zero (FTZ) flags to control the denormal behavior. When DAZ is set the denormals will compare equal to zero which is what you observed

Currently you'll need platform-specific code to disable it. Here's how it's done in x86:

As the order is the same one way is to simply search for adjacent key names & parse out whats in-between: