Why are car rentals in Switzerland & Austria so expensive (yet so cheap in Italy)?

I’m planning a 4-week trip next summer and have noticed that renting the car (via autoeurope.com site) in Switzerland or Austria is much more expensive (approx 1200 USD) than Italy or Germany (approx 700 USD). Interestingly, the rates for Italy actually include CDW and Theft Protection insurance, which seems like a steal (so there must be a catch)!

I was able to find that in CH/AT they show a “credit card block” of 300 CHF or 660 Euro respectively, but it wasn’t clear if that was included in the listed price and then refunded upon return or if there are some other taxes/fees imposed in those countries for some reason.

The rates above were all for the same time frame, car type and company (Hertz). I just changed the pickup/dropoff location to all the cities we could fly into. Is there a reason for the jump in those 2 countries and the apparent deal in Italy?

Edit: I did find that AutoEurope includes CDW/TP in the rate on rentals in Italy, but that doesn’t explain why the base rate is so much cheaper in comparison to Germany, for example.

In Italy, the law requires that you must carry certain types of insurances: Collision Damage Waiver (CDW) and Theft Protection. For your convenience, Auto Europe’s rates in Italy include these insurances….

How to use file system as cache for products which are expensive to produce?

Context

The consumer client components consume files. They access files in two step:

  • Clients call a REST API with a parameters what they want, and the API responds with a file path.
  • Clients access to the file using file system access.

There are two contradicting constraint: Producing the files has high cost, and takes time, so on demand production them is not an option. However it can be predicted all files will not fit on the storage, there will be more.

My first thought is to start with producing and storing the files, (and serve if there is a request using the API described above), and if the storage is near full the API detects, and starts deleting the less demanded files (based on a strategy, which is out of topic). In case if a file is requested and not on the filesystem yet, produce, store and respond with the path.

So it seems it is a file product cache on the file system.

Question

Is there any open source component which fits to this scenario, of do I have to implement myself?

Is AWS really more expensive than Linode? [on hold]

I moved my server from Linode to AWS, however, the cost almost tripled.

Then I take a look at the pricing page of Linode, they have much more free data transfer usage than AWS.

On their pricing page: https://www.linode.com/pricing#all

The $ 40 / mo plan has 5TB transfer limit.

However, for AWS, last month I have got 1,900.969 GB which is 1.9TB and this cost me about 216USD which is a HUGE money for me. And that doesn’t count the other cost AWS charged.

Any tips for me?

Thanks,

Working through the single responsibility principle (SRP) in Python when calls are expensive

Some base points:

  • Python method calls are “expensive” due to its interpreted nature. In theory, if your code is simple enough, breaking down Python code has negative impact besides readability and reuse (which is a big gain for developers, not so much for users).
  • The single responsibility principle (SRP) keeps code readable, is easier to test and maintain.
  • The project has a special kind of background where we want readable code, tests, and time performance.

For instance, code like this which invokes several methods (x4) is slower than the following one which is just one.

from operator import add  class Vector:     def __init__(self,list_of_3):         self.coordinates = list_of_3      def move(self,movement):         self.coordinates = list( map(add, self.coordinates, movement))         return self.coordinates      def revert(self):         self.coordinates = self.coordinates[::-1]         return self.coordinates      def get_coordinates(self):         return self.coordinates  ## Operation with one vector vec3 = Vector([1,2,3]) vec3.move([1,1,1]) vec3.revert() vec3.get_coordinates() 

In comparison to this:

from operator import add  def move_and_revert_and_return(vector,movement):     return list( map(add, vector, movement) )[::-1]  move_and_revert_and_return([1,2,3],[1,1,1]) 

If I am to parallelize something such as that, it is pretty objective I lose performance. Mind that is just an example; my project has several mini routines with math such as that – While it is much easier to work with, our profilers are disliking it.


How and where do we embrace the SRP without compromising performance in Python, as its inherent implementation directly impacts it?

Are there workarounds, like some sort of pre-processor that puts things in-line for release?

Or is Python simply poor at handling code breakdown altogether?

Will renting a car ad hoc in NYC be much more expensive than booking it beforehand?

I will be in NYC and upstate from late June to early July. I’m planning to rent a car for the second half of the stay, but it could turn out I need one earlier.

Should I absolutely book a car now to make sure I will get one at a reasonable price, or can I wait until there and rent one on the same day without a huge change in price?

In smaller places in Europe I would be nervous about short-term availability. Is this an issue in NYC?