fs.c | File system API much like Node 's fs module | Reactive Programming library

Use Promise.all to await the resolution of all promises that are created in the for-await loop:

Check if Spring Boot is importing version 3 and version 4 of Open SAML. If it is use only version 4.

Spring Security Samples has an example for SAML2. The build.gradle in the project contains the following:

By definition simple PDF structure is not tabular there is one cell (the page) and that one column can be subdivide into two or more rows with null spaces between the text sub columns.

That is why tables are difficult to sub[ex]tract

So adding coloured rows in one area is fairly simple to make like a table, thus to make vertical sub dividers is more difficult, However that feature was added in January 2022 https://github.com/natancabral/pdfkit-table/files/7865078/document-5.pdf

For exsample see https://github.com/natancabral/pdfkit-table/issues/16#issuecomment-1012389097

The default template parameter for the second check function is wrong, it should be std::bool_constant and not std::bool_constant.

The call is_fs.check() has no matching function, because you're testing is_same, bool_constant> in the first overload and is_same, bool_constant> in the second overload.

Also note that right now the call is_ri.check() is ambiguous, because both check functions are valid in is_right.

The static arrays contain definitions, so they need to be in a .c file.

If you put them in .h files, different translation units will define the same constants.

In your case, data is static, so by no means it will be in a .h file. It is not linked with anything externally, so no need to declare it in a header file.

If the static data is generated by some other program in a file "data", you can do so:

file.c:

You can use the Download repository archive (zip) endpoint, documented here

Take into consideration the following:

• You will perform a GET request to /repos/{owner}/{repo}/zipball/{ref}

• The first request will send a redirect (302 response), you need to follow the redirect properly, in this case, since you're using axios, you will need to handle it manually. The response will include a zipball_url you will use in the following request.

• Since you are downloading a private repo release, take into consideration these links are temporary and expire after five minutes.

• The first request accepts a user or installation token (in case you're consuming the API as a GitHub Application). Read more about the difference between a GitHub App and a GitHub OAuth App here

• If you are using a GitHub OAuth application, ensure you have the repo scope, for GitHub Application, ensure you have the Contents permissions

Check out the following working example:

There are a couple of things you can do to improve your computation:

Improve parallelism

As Nithish mentioned in the comments, you don't have enough partitions in your input data frames to make use of all your CPU cores. You're not using all your CPU capability and this will slow you down.

To increase your parallelism, repartition dfc to at least your number of cores:

dfc = dfc.repartition(dfc.sql_ctx.sparkContext.defaultParallelism)

You need to do this because your crossJoin is run as a BroadcastNestedLoopJoin which doesn't reshuffle your large input dataframe.

Separate your computation stages

A Spark dataframe/RDD is conceptually just a directed action graph (DAG) of operations to run on your input data but it does not hold data. One consequence of this behavior is that, by default, you'll rerun your computations as many times as you reuse your dataframe.

In your fuzzy_match_approve function, you run 2 separate filters on your df, this means you rerun the whole cross-join operations twice. You really don't want this !

One easy way to avoid this is to use cache() on your fuzzy_match result which should be fairly small given your inputs and matching criteria.

By creating a local variable to track whether you've already encountered the first record, you can prepend the comma to all entries after the first one, achieving the same effect:

Note that, because whitespace is insignificant in JSON, you will decrease the size of the resulting file by omitting the newlines

The async IIFE is your problem. That IIFE returns a promise that you are doing nothing with, thus the for loop just keeps running because nothing is awaiting that promise that the IIFE returns.

It would be better to fix your code by just removing both IIFEs and removing the new Promise() wrapper entirely like this: