money | Python money class with optional CLDR | Code Quality library

Advice/opinion

A database is for persistent storage of numbers and text.

An app is for computing and deciding things.

That is, the complex business logic you describe may be more appropriately encoded in Java (or other app language). Meanwhile, the breakpoints, etc for interest rates should be stored in the database.

Phrased differently: "Business logic" belongs in the app, not the database. (There is, of course, a gray lines between them. For example, SQL is excellent at summing up all the data in a table; so, I would do that in SQL, not Java.)

Decimal

Banks have picky rules that may not be the same as what DECIMAL or DOUBLE provide -- in Java or MySQL or _any other computer language. Be careful of the rules that may be required. In particular, DECIMAL(10, 4) is unlikely to be adequate in your application. On the other hand, apy DECIMAL(4, 2) may be adequate if that is what is presented to the customer. But, again, beware of rounding rules when generating that number. You may be forced to enter that number manually.

Be aware of the difference in rounding characteristics between DECIMAL and DOUBLE.

Answer

If you choose to implement an algorithm in the database, then see CREATE STORED PROCEDURE and CREATE FUNCTION.

Stored Procs can be used to encapsulate a set of SQL statements. It can receive and send strings and numbers, but not arrays. It is capable of reading tables (hence "arrays" of a sort.)

Functions can be called anywhere an expression can occur. It can receive some numbers/strings and produce one number or string.

Array

I'm not clear on what is needed, but I envision a table of a few rows or a few dozen rows with each row saying "for values up to $xxx, use y.yy% interest rate".

Stored Procs have "cursors" and "loops" but they are clumsy; the app language is likely to have better code.

This is possible but takes a little bit of work because in addition to being columnar Parquet also requires a schema.

The rough workflow is:

1. Open a parquet file for reading.

2. Then use iter_batches to read back chunks of rows incrementally (you can also pass specific columns you want to read from the file to save IO/CPU).

3. You can then transform each pa.RecordBatch from iter_batches further. Once you are done transforming the first batch you can get its schema and create a new ParquetWriter.

4. For each transformed batch call write_table. You have to first convert it to a pa.Table.

5. Close the files.

Parquet requires random access, so it can't be streamed easily from a URI (pyarrow should support it if you opened the file via HTTP FSSpec) but I think you might get blocked on writes.

One approach I can think of is, you can customize the amount of donation of some/all employees and distribute the rest to others equally. For example:

I want to donate $10,000 to 10 employees. Originally, each of them would receive $1000. But I want Employee-A and Employee-B to receive $1200 and $1300 respectively. Now I am left with $8500 and 8 employees. So, each 8 of them will receive $1062.5.

You have to add some input fields in your front-end. This is to customize which employee should receive how much donation. You should keep track of the employeeId as you will need it later while sending the payload to your server.

Create a JSON list (from your form) like this and pass it to the API call.

Maybe your validator range (0.0 to 5.0) is too narrow. If a newly calculated value falls outside the range, the validator state won't be QValidator::Acceptable anymore, and, as a side effect, the line edit will no longer emit the editingFinished signal.

You could try keeping the validator's top() to its default (infinity) value, maybe using the other constructor and using setters to set decimals and range bottom:

This data structure will allow many different player names:

I see two problems with this program. One of them I know how to fix, but the other apparently requires more knowledge of recursion schemes than I have.

The one I can fix is that it's looking up the wrong values in its cache. When given = 10, of course validCoins = [10,5,1], and so we find (zeroes, toProcess) = ([0], [5,9]). So far so good: we can give a dime directly, or give a nickel and then make change for the remaining five cents, or we can give a penny and change the remaining nine cents. But then when we write lookup 9 attr, we're saying "look 9 steps in history to when curr = 1", where what we meant was "look 1 step into history to when curr = 9". As a result we drastically undercount in pretty much all cases: even change 100 is only 16, while a Google search claims the right result is 292 (I haven't verified this today by implementing it myself).

There are a few equivalent ways to fix this; the smallest diff would be to replace

I found a workaround for this, but this is no final official solution.

It seems that if a user consented to Personalized ads, a string in SharedPreferences, which key is IABTCF_VendorConsents, will contain ones and zeros corresponding to some vendors (I think). If he didn't consent, this string will be equal to 0.

I have managed to solve my own problem using solve() from Symbolic Toolbox. There were two issues with my original approach: I needed to set up n simultaneous equations for n parameters, and the solve() doesn't cope well with exponentials:

I haven't tested your full setup with electrumx and the ethereum stuff present in your docker-compose file, but regarding your problem, the following steps worked properly, and I think it will do as well in your complete setup.

I ran with docker a bitcoin node based in the ulamlabs/bitcoind-custom-regtest:latest image you provided: