act | Run your GitHub Actions | Continous Integration library

C++ Copy Constructors: must I spell out all member variables in the initializer list?

Yes, if you write a user defined copy constructor, then you must write an initialiser for every sub object - unless you wish to default initialise them, in which case you don't need any initialiser - or if you can use a default member initialiser.

the object contains some member variables which are pointers to its other member variables)

This is a design that should be avoided when possible. Not only does this force you to define custom copy and move assignment operators and constructors, but it is often unnecessarily inefficient.

But, in case that is necessary for some reason - or custom special member functions are needed for any other reason - you can achieve clean code by combining the normally copying parts into a separate dummy class. That way the the user defined constructor has only one sub object to initialise.

Like this:

I rewrote your code with some better ways to create table. My idea was to pick out the buttons that fell onto a range of type and then loop through those buttons and change its color to those type.

I'm not terribly familiar with your stack, so this is a high-level answer to hit on your "Why". There WILL be a more specific answer for you, somewhere down the pipe (e.g. someone that can confirm whether this thread is relevant).

While I'm no Spring Daisy (or Spring dev), you bind an expression to filmMono that resolves as the query select film.* from film..... You reference that expression four times, and it's resolved four times, in separate contexts. The ordering of the statements is likely a partially-successful attempt by the lib author to lazily evaluate the expression that you bound locally, such that it's able to batch the four accidentally identical queries. You most likely resolved this by collecting into an actual container, and then mapping on that container instead of the expression bound to filmMono.

In general, this situation is because the options available to library authors aren't good when the language doesn't natively support lazy evaluation. Because any operation might alter the dataset, the library author has to choose between:

• A, construct just enough scaffolding to fully record all resources needed, copy the dataset for any operations that need to mutate records in some way, and hope that they can detect any edge-cases that might leak the scaffolding when the resolved dataset was expected (getting this right is...hard).
• B, resolve each level of mapping as a query, for each context it appears in, lest any operations mutate the dataset in ways that might surprise the integrator (e.g. you).
• C, as above, except instead of duplicating the original request, just duplicate the data...at every step. Pass-by-copy gets real painful real fast on the JVM, and languages like Clojure and Scala handle this by just making the dev be very specific about whether they want to mutate in-place, or copy then mutate.

In your case, B made the most sense to the folks that wrote that lib. In fact, they apparently got close enough to A that they were able to batch all the queries that were produced by resolving the expression bound to filmMono (which are only accidentally identical), so color me a bit impressed.

Many access patterns can be rewritten to optimize for the resulting queries instead. Your milage may vary...wildly. Getting familiar with raw SQL, or else a special-purpose language like GraphQL, can give much more consistent results than relational mappers, but I'm ever more appreciative of good IDE support, and mixing domains like that often means giving up auto-complete, context highlighting, lang-server solution-proofs and linting.

Given that the scope of the question was "why did this happen?", even noting my lack of familiarity with your stack, the answer is "lazy evaluation in a language that doesn't natively support it is really hard."

The EXPORTHOW mechanism is only for overriding the metaclass that will be used for package declarators, with the slight extension that EXPORTHOW::DECLARE also performs a grammar tweak that introduces a new package declarator.

While one can call .HOW on a Sub, the result does not relate to the subroutine itself, but rather the metaclass of the Sub type, of which a subroutine is an instance.

Really, EXPORTHOW is an "easy things easy" mechanism (to the degree it's fair to call anything relating to meta-programming easy!) It was also a straightforward thing to provide: the parsing of package declarations was already extremely regular, and the compiler already maintained a mapping table from package keyword to metaclass, so providing a way for a module to replace entries in that table (or add new ones for DECLARE) was barely a few hours of compiler hackery.

Routines are vastly less regular, even if that's only somewhat apparent syntactically. While packages pretty much parse the keyword (class, role, grammar, etc.) and what follows is the very same syntax and semantics for all of them (modulo roles permitting a signature), there are separate parse rules and semantics behind each of sub, method, macro, and rule. Their interaction with the overall compilation process is also rather more involved. The ongoing RakuAST effort is bringing a bit more order to that chaos, and ultimately - when coupled with slangs - will offer a way to introduce new sub-like constructs, as well as to give them semantics.

With POSIX awk, I'd use match and the builtin RSTART and RLENGTH variables:

• StreamReader.ReadLine() is blocking by design.

• There is an asynchronous alternative, .ReadLineAsync(), which returns a Task instance that you can poll for completion, via its .IsCompleted property, without blocking your foreground thread (polling is your only option in PowerShell, given that it has no language feature analogous to C#'s await).

Here's a simplified example that focuses on asynchronous reading from a StreamReader instance that happens to be a file, to which new lines are added only periodically; use Ctrl-C to abort.

I would expect the code to work the same if you adapt it to your stdout-reading System.Diagnostics.Process code.

HttpClient, please forgive me! It was not your fault!

Well, this investigation was huge. And I'll have to split the answer into two parts since there turned out to be two unconnected problems.

As I said, the utility was being tested from behind a corporate proxy. In case if you haven't known (like I haven't till the latest days) when using a proxy server it's not your machine performing DNS queries - it's the proxy server doing this for you.

I've made some measurements to understand for how long does the utility keep connecting to the wrong DC after the DNS record switch. And the answer was the fantastic exact 30 minutes. This experiment has also clearly shown that local Windows DNS cache has nothing to do with it: those 30 minutes were starting exactly at the point when the proxy server was waking up (was finally starting to send HTTP requests to the right DC).

The exact number of 30 minutes has helped one of our administrators to finally figure out that the proxy servers have a configuration parameter of minimal DNS TTL which is set to 1800 seconds by default. So the proxies have their own DNS cache. These are hardware Cisco proxies and the admin has also noted that this parameter is "hidden quite deeply" and is not even mentioned in the user manual.

As soon as the minimal proxies' DNS TTL was changed from 1800 seconds to 1 second (yeah, admins have no mercy) the issue stopped reproducing on my machine.

Well. As I also said there are several proxies. There is a single corporate proxy DNS name, but if you run nslookup for it - it shows multiple IPs behind it. Each time the proxy server's IP address is resolved (for example when local cache expires) - there's quite a bit of a chance that you'll jump onto another proxy server.

And that's exactly what ipconfig /flushdns has been doing to me. As soon as I started playing around with proxy servers using their direct IP addresses instead of their common DNS name I found that different proxies may easily route identical requests to different DCs. That's because some of them have those 30-minutes-cached DNS records while others have to perform resolving.

Unfortunately, after the proxies theory has been proven, another news came in: the production monitoring servers are placed outside of the corporate network and they do not use any proxy servers. So here we go...

The monitoring servers are configured to use 8.8.8.8 and 8.8.4.4 Google's DNS servers. Resolve responses for our short-lived DNS records from these servers are somewhat weird:

• The returned TTL of CNAME records swings at around 1 hour mark. It gradually decreases for several minutes and then jumps back to 3600 seconds - and so on.
• The returned TTL of the root A-record is almost always exactly 60 seconds. I was occasionally receiving various numbers less than 60 but there was no any obvious humanly-percievable logic. So it seems like these IP addresses in fact point to balancers that distribute requests between multiple similar DNS servers which are not synced with each other (and each of them has its own cache).

Windows is not stupid and according to my experiments it doesn't care about CNAME's TTL and only cares about the root A-record TTL, so its client cache even for CNAME records is never assigned a TTL higher than 60 seconds.

But due to the inconsistency (or in some sense over-consistency?) of the A-record TTL which Google's servers return (unpredictable 0-60 seconds) the Windows local cache gets confused. There were two facts which demonstrated it:

• Multiple calls to Resolve-DnsName for site1 and site2 over several minutes with random pauses between them have eventually led to Get-ClientDnsCache showing the local cache TTLs of the two site names diverged on up to 15 seconds. This is a big enough difference to sometimes mess the things up. And that's just my short experiment, so I'm quite sure that it might actually get bigger.
• Executing Invoke-WebRequest to each of the sites one right after another once in every 3-5 seconds while switching the DNS records has let me twicely face a situation when the requests went to different DCs.

The latter experiment had one strange detail I can't explain. Calling Get-DnsClientCache after Invoke-WebRequest shows no records appear in the local cache for the just-requested site names. But anyway the problem clearly has been reproduced.

It would take time to see whether my workaround with real-time DNS resolving would bring any improvement. Unfortunately, I don't believe it will - the DNS servers used at production (which would eventually be used by the monitoring utility for real-time IP resolving) are public Google DNS which are not reliable in my case.

And one thing which is worse than an intermittently failing monitoring utility is that real-world users are also relying on public DNS servers and they definitely do face problems during our maintenance works or significant failures.

So have we learned anything out of all this?

• Maybe a short DNS TTL is generally a bad practice?
• Maybe we should install additional routers, assign them static IPs, attach the DNS names to them and then route traffic internally between our DCs to finally stop relying on DNS records changing?
• Or maybe public DNS servers are doing a bad job?
• Or maybe the technological singularity is closer than we think?

I have no idea. But its quite possible that "yes" is the right answer to all of these questions.

However there is one thing we surely have learned: network hardware manufacturers shall write their documentation better.

Because it's a named export you should return an object in the mock

I found a way with Android's viewTreeObserver. It essentially is the Android version but it calls a callback that can be used in compose.