process** (e.g. a Uvicorn process running your FastAPI application). They would all be **identical containers**, running the same thing, but each with its own process, memory, etc. That way you would take advantage of **parallelization** in **different cores** of the CPU, or even in **different machines**.

And the distributed container system with the **load balancer** would **distribute the requests** to each one of the containers with your app **in turns**. So, each request could be handled...

    }

    // TODO take repo mans into account as one may be aggregating prefixes of many
    // TODO collect at the root of the repository, read the one at the root, and fetch remote if something is missing
    // or the user forces the issue

    public Plugin findPluginForPrefix(String prefix, MavenSession session) throws NoPluginFoundForPrefixException {
        // [prefix]:[goal]

        if (session.getCurrentProject() != null) {

streaming compression due to its stability and performance.

This algorithm is specifically optimized for machine generated content.
Write throughput is typically at least 500MB/s per CPU core,
and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

This means that in cases where raw IO is below these numbers
compression will not only reduce disk usage but also help increase system throughput.

    // TODO take repo mans into account as one may be aggregating prefixes of many
    // TODO collect at the root of the repository, read the one at the root, and fetch remote if something is missing
    // or the user forces the issue

    private List<MojoExecution> calculateForkedGoal(
            MojoExecution mojoExecution,
            MavenSession session,
            MavenProject project,

    checkNotNull(cache);
    checkNotNull(key);
    LocalCache<K, V> map = toLocalCache(cache);
    return map.getEntry(key);
  }

  /**
   * Forces the segment containing the given {@code key} to expand (see {@link Segment#expand()}).
   */
  static <K, V> void forceExpandSegment(Cache<K, V> cache, K key) {
    checkNotNull(cache);
    checkNotNull(key);

    `float16` for acceleration on Volta and Turing Tensor Cores. This feature
    can be enabled by wrapping an optimizer class with
    `tf.train.experimental.enable_mixed_precision_graph_rewrite()`.
*   Add environment variable `TF_CUDNN_DETERMINISTIC`. Setting to "true" or "1"
    forces the selection of deterministic cuDNN convolution and max-pooling

       * If we make some other optimizations, this one will no longer be necessary. The optimization
       * could actually hurt in some cases, as it forces us to keep all inputs in memory until the
       * final input completes.
       */
      ImmutableCollection<? extends Future<? extends InputT>> localFutures =
          collectsValues ? futures : null;

    checkNotNull(cache);
    checkNotNull(key);
    LocalCache<K, V> map = toLocalCache(cache);
    return map.getEntry(key);
  }

  /**
   * Forces the segment containing the given {@code key} to expand (see {@link Segment#expand()}).
   */
  static <K, V> void forceExpandSegment(Cache<K, V> cache, K key) {
    checkNotNull(cache);
    checkNotNull(key);

    // avoids this.
    Preconditions.checkState(aBoolean, "", aBoolean ? "" : anInt, (Object) anInt);

    // ambiguous without the .booleanValue() call since the boxing forces us into phase 2 resolution
    short s = 2;
    Preconditions.checkState(boxedBoolean.booleanValue(), "", s);
  }

  @J2ktIncompatible
  @GwtIncompatible // NullPointerTester
  public void testNullPointers() {

    // avoids this.
    Preconditions.checkState(aBoolean, "", aBoolean ? "" : anInt, (Object) anInt);

    // ambiguous without the .booleanValue() call since the boxing forces us into phase 2 resolution
    short s = 2;
    Preconditions.checkState(boxedBoolean.booleanValue(), "", s);
  }

  @J2ktIncompatible
  @GwtIncompatible // NullPointerTester
  public void testNullPointers() {