// wait for other goroutines prepared
		<-stmt.prepared
		if stmt.prepareErr != nil {
			return Stmt{}, stmt.prepareErr
		}

		return *stmt, nil
	}
	// check db.Stmts first to avoid Segmentation Fault(setting value to nil map)
	// which cause by calling Close and executing SQL concurrently
	if db.Stmts == nil {
		db.Mux.Unlock()
		return Stmt{}, ErrInvalidDB
	}
	// cache preparing stmt first

	if err == io.EOF {
		err = io.ErrUnexpectedEOF
	}
	r.err = err
	return r.current, err
}

// next will read one entry from the stream.
// Generally not recommended for fast operation.
func (r *metacacheReader) next() (metaCacheEntry, error) {
	r.checkInit()
	if r.err != nil {
		return metaCacheEntry{}, r.err
	}
	var m metaCacheEntry
	var err error
	if r.current.name != "" {

      if (spinCount > MAX_BUSY_WAIT_SPINS) {
        /*
         * If we have spun a lot, just park ourselves. This will save CPU while we wait for a slow
         * interrupting thread. In theory, interruptTask() should be very fast, but due to
         * InterruptibleChannel and JavaLangAccess.blockedOn(Thread, Interruptible), it isn't
         * predictable what work might be done. (e.g., close a file and flush buffers to disk). To

### Parallele Hamburger

Stellen wir uns jetzt vor, dass es sich hierbei nicht um „nebenläufige Hamburger“, sondern um „parallele Hamburger“ handelt.

Sie gehen los mit Ihrem Schwarm, um paralleles Fast Food zu bekommen.

Sie stehen in der Schlange, während mehrere (sagen wir acht) Kassierer, die gleichzeitig Köche sind, die Bestellungen der Leute vor Ihnen entgegennehmen.

                            }
                          },
                          {
                            "name": "envoy.filters.http.fault",
                            "typed_config": {
                              "@type": "type.googleapis.com/envoy.extensions.filters.http.fault.v3.HTTPFault"
                            }
                          },
                          {

        I'm asking this because answering questions and solving problems in GitHub is what consumes most of the time.

        I end up not being able to add new features, fix bugs, review pull requests, etc. as fast as I wish because I have to spend too much time handling questions.

It was one of the first extremely fast Python frameworks based on `asyncio`. It was made to be very similar to Flask.

/// note | "Technical Details"

It used <a href="https://github.com/MagicStack/uvloop" class="external-link" target="_blank">`uvloop`</a> instead of the default Python `asyncio` loop. That's what made it so fast.

OkHttp is **not** source-compatible for Kotlin callers, but upgrading should be automatic thanks to
Kotlin’s powerful deprecation features. Most developers should be able to use IntelliJ’s _Code
Cleanup_ for a safe and fast upgrade.


Backwards-Incompatible Changes
------------------------------

#### OkHttpClient final methods

`OkHttpClient` has 26 accessors like `interceptors()` and `writeTimeoutMillis()` that were non-final

many as necessary with `addSubjectAlternativeName()`. This mechanism also supports a very limited
form of wildcards `*.example.com` where the `*` must be first and doesn't match nested subdomains.

By default certificates use fast and secure 256-bit ECDSA keys. For interoperability with very old
clients use `HeldCertificate.Builder.rsa2048()`.

Download
--------

```kotlin
implementation("com.squareup.okhttp3:okhttp-tls:4.12.0")
```

p-with-fastapi-part-one-7c049292ad37 title: Creating a CRUD App with FastAPI (Part one) - author: Farhad Malik author_link: https://medium.com/@farhadmalik link: https://towardsdatascience.com/build-and-host-fast-data-science-applications-using-fastapi-823be8a1d6a0 title: Build And Host Fast Data Science Applications Using FastAPI - author: Navule Pavan Kumar Rao author_link: https://www.linkedin.com/in/navule/ link: https://www.tutlinks.com/deploy-fastapi-on-azure/ title: Deploy FastAPI on Azure...