}
                }
              });
    }

    threadPool.shutdown();
    threadPool.awaitTermination(300, SECONDS);

    // Since we're not doing any more cache operations, and the cache only expires/evicts when doing
    // other operations, the cache and the removal queue won't change from this point on.

    // Verify that each received removal notification was valid

 *   <li><b>Convenience methods:</b> These are implementations of common operations that are
 *       typically implemented by opening a reader using one of the methods in the first category,
 *       doing something and finally closing the reader that was opened.
 * </ul>
 *
 * <p>Several methods in this class, such as {@link #readLines()}, break the contents of the source

            // If this CAS succeeds, we know that the provided callable will never be invoked,
            // so when oldFuture completes it is safe to allow the next submitted task to
            // proceed. Doing this immediately here lets the next task run without waiting for
            // the cancelled task's executor to run the noop AsyncCallable.
            //
            // ---
            //

         * than a handful of exceptions. But it seems prudent to set a cap on how many we'll cache.
         * This avoids out-of-control memory consumption, and it keeps the cache from growing so
         * large that doing the lookup is noticeably slower than redoing the work would be.
         *
         * Ideally we'd have a real eviction policy, but until we see a problem in practice, I hope

         *     <li> {@link org.codehaus.groovy.runtime.callsite.CallSiteArray#replaceCallSite}
         * </ul>
         *
         * Because of it doing so, our decorated call site is removed from the dynamic invocation code path, and we lose the ability to track dynamic call entry points. <p>
         *

	// maxSwitch is the maximum toolchain switching depth.
	// Most uses should never see more than three.
	// (Perhaps one for the initial GOTOOLCHAIN dispatch,
	// a second for go get doing an upgrade, and a third if
	// for some reason the chosen upgrade version is too small
	// by a little.)
	// When the count reaches maxSwitch - 10, we start logging
	// the switched versions for debugging before crashing with

			return n, err
		}
	}
	return
}

// Discard is a [Writer] on which all Write calls succeed
// without doing anything.
var Discard Writer = discard{}

type discard struct{}

// discard implements ReaderFrom as an optimization so Copy to
// io.Discard can avoid doing unnecessary work.
var _ ReaderFrom = discard{}

func (discard) Write(p []byte) (int, error) {
	return len(p), nil
}

Flask is a "microframework", it doesn't include database integrations nor many of the things that come by default in Django.

This simplicity and flexibility allow doing things like using NoSQL databases as the main data storage system.

As it is very simple, it's relatively intuitive to learn, although the documentation gets somewhat technical at some points.

						}
					}
				}
			})
		}
	}
}

// Baseline:
//   - ~150us via agent if cached for A/AAAA
//   - ~300us via agent when doing the cname redirect
//   - 5-6ms to upstream resolver directly
//   - 6-7ms via agent to upstream resolver (cache miss)
//
// Also useful for load testing is using dnsperf. This can be run with:
//

         * than a handful of exceptions. But it seems prudent to set a cap on how many we'll cache.
         * This avoids out-of-control memory consumption, and it keeps the cache from growing so
         * large that doing the lookup is noticeably slower than redoing the work would be.
         *
         * Ideally we'd have a real eviction policy, but until we see a problem in practice, I hope