Not a bug! Are you kidding? This kind of "new" functionality is completely out of use.

Bedankt Sander,

I do not have a problem to import and work with data in a different way as to make use of datasets. But as a novice in Mathematica programming I would be happy to get somewhere an official statement that everything related to datasets is still under development and should be only applied on files up to ...kByte? From my point of view it is not good and not fair to avoid all kind of communication about known bugs. Each and every software has bugs, but it is a matter of customer support and finally customer satisfaction either to let them fall into a trap or to make clear what are the product boundaries. But for the Wolfram Team it seems to be impossible until now to admit there is an issue. In Mathematica we would describe this in the follwing way:

Association[Key -> "Communication"]

The result of the entire discussion for me is, that I will simply forget the Wolfram dataset philosophy and I hope the next (for me unknown) known bug is not already waiting for me around the corner.

As of now (start 2019), Dataset are not really designed for large datasets. It has a lot of overhead, which you are feeling. Having rectangular arrays all of the same type, and using packed arrays is the best way to handle large datasets.

Datasets are convenient and very flexible but that comes at a cost because of that. Same holds for DateObjects. If you leave them as strings, the speed up is as expected:

dates = DateRange["jan. 1st, 2001", "2019"];
Map[AbsoluteTime, dates]; // AbsoluteTiming
ParallelMap[AbsoluteTime, dates, Method -> "CoarsestGrained"]; // AbsoluteTiming

But note that the interpretation from a date-string to an AbsoluteTime is a much tougher operation as compared to converting a DateObject to AbsoluteTime. The heavy lifting (the string interpretation) you had already done using e.g. SemanticImport.

Personally, I am afraid that you will be disappointed by Wolfram support, which is typically total silence in a case when you hit some real issue.

May be in the next release will be your problem solved, may be not. There are some bugs, which remains unsolved for many years.

I hope your statement is incorrect, because I have made an investment to buy a state of the art software that allows me to do everything up to Deep Learning.

We are still talking here about basics of Data Analysis, not rocket science. The csv file comes from an R course I have taken many years ago. Meantime I have made the same analysis with Matlab and Maple without any problem and I thought I could make this training unit with Mathematica again. Okay, Matlab and Maple do not offer direct Parallel commands, at least is this my current knowledge status.

I have to admit that I have assumed to buy a stable and robust software package, but it seems to me that this assumption is wrong. On the other hand, I am wondering why the Wolfram Team does not raise hands to convince all readers that your (and my) statement is incorrect.

A simple question but hard to answer.

1. The funtion Dataset is basically a wrapper that puts your data in a form that uses Association to label each value in you dataset such that it becomes more easy to extract data without being bothered to count columns and row or knowing where or what your data is. Basically Mathematica figures it out four you.
2. Your code has a date format that is ambiguous e.g. 12/10 can be 12th of October or 10th of December therefore it generates warnings that it want to print to the front-end (suppressed by Quiet) making it slow. In my code i explicitly provide the date format to expect such that Mathematica does not has to figure it out by it self, therefore it becomes faster.
3. Mathematica is a smart language that can figure out a lot about your data and handle it accordingly. However this takes time. Data is just numbers and if you tell Mathematica how to treat these numbers it does not have to figure it out by it self therefore it becomes faster.

So basically the more you think your self and provide Mathemaitca with what it needs to know it becomes faster. If you want Mathematica to figure it out on its own it will most of the time but becomes slower and unexpected things can happen.

Without providing Mathematica with the correct input it interprets you data as year/month/day if i'm not mistaken. But your data is formated as month/day/year. So actually your code gives a wrong output. If you would define a month interpreter yourself you can do it much faster. Observe that this code runs the entire dataset, 190K values, in the same time a the DateString function for only 10K values. But without providing the DateString with the correct input its output is wrong

Thank you ! Based on your suggestion it is possible for me to run all Parallel commands for the entire file in appropriate times. So, what is the difference apart from using the Quiet command? I did work completely with Datasets starting with the use of SemanticImport. You just imported the CSV file as is (List). My Mathematica knowledge and experience is still on a low level, but it seems to me now, that Mathematica is going into trouble with large files, datasets and parallelization.

Are there different opinions?

I am rather interested to know why the Dataset is the problem. Anyhow, I have made two tests:

1. with the following command and result

2. with the ParallelMap command

My observation is, that this Parallel-Command takes also more time compared to the Single-Command.

It is pretty much impossible to determine what might be issues without actual input that shows the problem.

How much memory (ram) does your data consume? How much memory is consumed during the actual computation? One guess is that the computation you are requesting inflates the memory so much that the paging file on your hard drive is being used to complete the computation.

Have you tried ParallelTable options such as "FinestGrained" and "CoarsestGrained"? Either way, if the data communication overhead is significant ParallelTable will not be faster than Table.