вы получаете производительность лучше, чем у большинства протестированных NodeJS фреймворков
и на уровне с Go, который является компилируемым языком близким к C <a href="https://www.techempower.com/benchmarks/#section=data-r17&hw=ph&test=query&l=zijmkf-1" class="external-link" target="_blank">(всё благодаря Starlette)</a>.

### Получается, конкурентность лучше параллелизма?

Нет! Мораль истории совсем не в этом.

 *         .catching(DBException.class, e -> "no user", directExecutor())
 *         .finishToFuture();
 * }</pre>
 *
 * In this example, when the {@code userName} {@link Future} is done, the transaction and the query
 * result cursor will both be closed, even if the operation is cancelled or fails.
 *
 * <h4>Manually closing</h4>
 *
 * If you want to close the captured objects manually, after you've used the final result, call

Esse desacoplamento de partes, e sendo um "microframework" que pode ser extendido para cobrir exatamente o que é necessário era um recurso chave que eu queria manter.

Dada a simplicidade do Flask, parecia uma ótima opção para construção de APIs. A próxima coisa a procurar era um "Django REST Framework" para Flask.

/// check | "**FastAPI** inspirado para"

  @VisibleForTesting
  ConcurrentHashMultiset(ConcurrentMap<E, AtomicInteger> countMap) {
    checkArgument(countMap.isEmpty(), "the backing map (%s) must be empty", countMap);
    this.countMap = countMap;
  }

  // Query Operations

  /**
   * Returns the number of occurrences of {@code element} in this multiset.
   *
   * @param element the element to look for
   * @return the nonnegative number of occurrences of the element

      assertEquals(200, response.code)
    }
  }

  fun buildCloudflareIp(bootstrapClient: OkHttpClient): DnsOverHttps {
    return DnsOverHttps.Builder().client(bootstrapClient)
      .url("https://1.1.1.1/dns-query".toHttpUrl())
      .build()
  }

  private fun enableTls() {
    client =
      client.newBuilder()
        .sslSocketFactory(
          handshakeCertificates.sslSocketFactory(),

   * by applying the given {@code AsyncFunction} to the result of the original {@code Future}.
   * Example usage:
   *
   * <pre>{@code
   * FluentFuture<RowKey> rowKeyFuture = FluentFuture.from(indexService.lookUp(query));
   * ListenableFuture<QueryResult> queryFuture =
   *     rowKeyFuture.transformAsync(dataService::readFuture, executor);
   * }</pre>
   *

  TF_Graph* graph = TF_NewGraph();

  // Make a placeholder operation.
  TF_Operation* feed = Placeholder(graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Test TF_Operation*() query functions.
  EXPECT_EQ(string("feed"), string(TF_OperationName(feed)));
  EXPECT_EQ(string("Placeholder"), string(TF_OperationOpType(feed)));
  EXPECT_EQ(string(""), string(TF_OperationDevice(feed)));

      Multimap<K, V> synchronizedMultimap(Multimap<K, V> multimap) {
    return Synchronized.multimap(multimap, null);
  }

  /**
   * Returns an unmodifiable view of the specified multimap. Query operations on the returned
   * multimap "read through" to the specified multimap, and attempts to modify the returned
   * multimap, either directly or through the multimap's views, result in an {@code

                    }
                }
                // == 5*128 = 640

                // . and .. entries = 200 byte (includes alignment)

                // + 64 byte header
                // + 8 byte query response overhead
                // + 110 bytes entry
                // -> 1022 predicted message size <= 1023 maximum buffer size
                // 112 bytes to alignment

* Fix logrotate config on GCI ([#29139](https://github.com/kubernetes/kubernetes/pull/29139), [@adityakali](https://github.com/adityakali))
* Do not query the metadata server to find out if running on GCE.  Retry metadata server query for gcr if running on gce. ([#28871](https://github.com/kubernetes/kubernetes/pull/28871), [@vishh](https://github.com/vishh))