/**
   * When we need a new thread to run tasks, we call [Backend.execute]. A few microseconds later we
   * expect a newly-started thread to call [Runnable.run]. We shouldn't request new threads until
   * the already-requested ones are in service, otherwise we might create more threads than we need.
   *
   * We use [executeCallCount] and [runCallCount] to defend against starting more threads than we

    for (int i = 0; i < numThreads; i++) {
      threads[i] =
          new Thread() {
            @Override
            public void run() {
              assertSame(Boolean.TRUE, memoizedSupplier.get());
            }
          };
    }
    for (Thread t : threads) {
      t.start();
    }
    for (Thread t : threads) {
      t.join();
    }

    if (thrown.get() != null) {

    for (int i = 0; i < numThreads; i++) {
      threads[i] =
          new Thread() {
            @Override
            public void run() {
              assertSame(Boolean.TRUE, memoizedSupplier.get());
            }
          };
    }
    for (Thread t : threads) {
      t.start();
    }
    for (Thread t : threads) {
      t.join();
    }

    if (thrown.get() != null) {

   * there would exist a perfect hash function mapping threads to
   * slots that eliminates collisions. When we reach capacity, we
   * search for this mapping by randomly varying the hash codes of
   * colliding threads.  Because search is random, and collisions
   * only become known via CAS failures, convergence can be slow,
   * and because threads are typically not bound to CPUS forever,
   * may not occur at all. However, despite these limitations,

  }

  /**
   * Add a shutdown hook to wait for thread completion in the given {@link ExecutorService service}.
   * This is useful if the given service uses daemon threads, and we want to keep the JVM from
   * exiting immediately on shutdown, instead giving these daemon threads a chance to terminate
   * normally.
   *
   * @param service ExecutorService which uses daemon threads

```
hive.blobstore.use.blobstore.as.scratchdir=true
hive.exec.input.listing.max.threads=50
hive.load.dynamic.partitions.thread=25
hive.metastore.fshandler.threads=50
hive.mv.files.threads=40
mapreduce.input.fileinputformat.list-status.num-threads=50
```

    executorService =
        new ThreadPoolExecutor(
            NUM_THREADS,
            NUM_THREADS,
            Long.MAX_VALUE,
            TimeUnit.SECONDS,
            new ArrayBlockingQueue<Runnable>(1000));
    executorService.prestartAllCoreThreads();
    final AtomicInteger integer = new AtomicInteger();
    // Execute a bunch of tasks to ensure that our threads are allocated and hot
    for (int i = 0; i < NUM_THREADS * 10; i++) {

   * there would exist a perfect hash function mapping threads to
   * slots that eliminates collisions. When we reach capacity, we
   * search for this mapping by randomly varying the hash codes of
   * colliding threads.  Because search is random, and collisions
   * only become known via CAS failures, convergence can be slow,
   * and because threads are typically not bound to CPUS forever,
   * may not occur at all. However, despite these limitations,

            return jvmGcObj;
        }).toArray(n -> new JvmGcObj[n]);
        final Threads threads = jvmStats.getThreads();
        final JvmThreadsObj jvmThreadsObj = new JvmThreadsObj();
        jvmObj.threads = jvmThreadsObj;
        jvmThreadsObj.count = threads.getCount();
        jvmThreadsObj.peak = threads.getPeakCount();
        final Classes classes = jvmStats.getClasses();

   * there would exist a perfect hash function mapping threads to
   * slots that eliminates collisions. When we reach capacity, we
   * search for this mapping by randomly varying the hash codes of
   * colliding threads.  Because search is random, and collisions
   * only become known via CAS failures, convergence can be slow,
   * and because threads are typically not bound to CPUS forever,
   * may not occur at all. However, despite these limitations,