uom | Extensible unit of measure conversion | Runtime Evironment library

It seems like you have row by row processing in your [tmpFinalTethers] and [tmpGetFinalTethers] cte's.

Each row returned in [tmpFinalTethers] is based on [tmpOrderedTethers] and [tmpOrderedTethers]'s data is based on [tmpTethers]. Therefore the logic which contains in [tmpOrderedTethers] and [tmpTethers] will be executed n times, where n is a number of rows returned by [tmpFinalTethers].

The reason is because cte's are not materialized objects. They are not get stored in memory or disc, so they're executing each time you reference them outside of declaration.

Loading the resultset of [tmpOrderedTethers] to temp table may help if you really need row by row processing for your task and don't have other options.

Also it seems like your [tmpFinalTethers] and [tmpGetFinalTethers] have the same logic inside. I am not sure what the purpose for it. Mb you can do final select from [tmpFinalTethers] and get rid of [tmpGetFinalTethers].

Edited:

Try smth like this:

Use a counter - assuming exact same number of entries per row

http://sandbox.onlinephpfunctions.com/code/555be47daf3bc3e99d496585f702bfc9dfae4e4e

Like others said, you're

• missing a hash implementation
• using a pretty inefficient key type

I'd simplify on both accounts:

Live On Compiler Explorer

Your mistakes lies in this function:

HasForeignKey specifies the (shadow) property name. The conventional column name is the same as the property name, however for owned entity types it includes the owner property name (similar to other properties).

Hence what you need is to specify the column name similar to the "working" example. e.g. knowing that the conventional shadow FK property name in this case is UOMId, it could be set as follows

I'm resolving this problem. The solution is to change the file "src/api/TotalRecordsController" inside the folder "~/vendor/coroowicaksono/chart-js-integration" so that it suits PostgreSQL.

This is much easier with the data in "long" format. Below is an example with fake data. We use the lag function to get changes over different time ranges. Once you've added the changes over various timescales, you can subset and reshape the data as needed to create your final tables.

1- Check your database name and password.
2- Open Mongodb atlas and in "Network Access", change the IP address as "0.0.0.0/0 (includes your current IP address) ".
3- Maybe you can try adding "await" before client.close().

Aggregate solution
(requires MongoDB 5, else query would be a little bigger)

Query

• filter for the _id with the $match stage
• get last element of $instock, and then field qty
• project to keep only the above part

*we do it like we would do it in a programming language, get last element, and get a field value.

Test code here