ABE | Attribute-based Encryption | Authorization library

Ordering is determined by collations in SQL Server and DB2. It seems your iSeries DB2 is configured with an EBCDIC collation so you could add an explict COLLATE clause to the ORDER BY expression to coerce EBCDIC ordering rules for SQL Server since your SQL Server collation is apparently different.

Below is an example of your original DB2 query with the clause added for the SQL Server:

You could write some logic where you would split the string into an array of characters and then compare the separate values using map() and replace when needed. Then join the array back to a string.

Here is what I came up with, by simply using readtable.

I would use a child component that's doing all the checks and then render the good component.

Solution in Python:

Seems like a regular reduce side join, so it may be somehow of a plain use case, however those kinds of computations tend to get really brutal on the workload side of things. This means that we must find ways to cut corners for us to make sure that the application is going to scale good enough for larger scales of input.

The most common way of saving time/space for the application's execution is either trying to design the potentially several MR jobs in a way where we can "cut" one or more jobs while keeping all of the functionality, or trying to minimize the number of (custom) mappers that are going to be implemented at the input data. The latter of the two is quite common for this kind of filtering you are trying to achieve, because we can easily use just one Map function which each instance of it will check the name of the file that it's currently reading to act accordingly.

More specifically, we can get the File1.txt and File2.txt file names before the mappers start to run through the setup function of the Map class, and use the name of the current file to be read to determine how to chop and store the data from the files into key-value pairs. For your problem, this Map function will output two types of key-value pairs:

• (for data in File1.txt)

• (for data in File2.txt)

Then the instances of the Reduce function will run for each customer (because customer IDs and names are unique, of course) and access the grouped values which are nothing more than a number of Text objects that either hold this customer's name or an order ID. We only want to output the customers without any orders on their records, so all we have to do is check if this list of values has a length of 1 (aka if there are no pair values for this customer other that one with his name).

To showcase this, I put both of the input files in a directory /input in HDFS (I used two-tab delimiters for the columns in File1.txt and three-tab delimiters for the columns of File2.txt. In case your files have different tabs or spaces between the column, you can change them accordingly):

File1.txt

Make these changes to your main().

Starting the match with a . dot not make sure that there is a char a-zA-Z before it.

If you use the named group word in the replacement, that will contain the value of the last iteration as it is by itself in a repeated group.

You can match 2 or more dots with 1 or 2 times a char a-zA-Z and replace the dots with an empty string when there is a match instead.

To prevent aaa.b.cc from matching, you could make use of word boundaries \b

Disclaimer: I see that some of the comments were deleted. So let me start by saying that, though short (in terms of code-golf), the following answer is not the most efficient in terms of steps involved. Though, looking at the nature of the question and its "puzzle" aspect, it will probably do fine. For a more efficient answer, I'd like to redirect you to this answer.

Here is my attempt: