// if we need to update artifactScope of nearest to use farthest artifactScope, use the
                            // nearest version, but farthest artifactScope
                            nearest.disable();
                            farthest.getArtifact()
                                    .setVersion(nearest.getArtifact().getVersion());

        a.addDependency(c);
        ArtifactSpec dNearest = createArtifactSpec("d", "2.0");
        b.addDependency(dNearest);
        ArtifactSpec dFarthest = createArtifactSpec("d", "3.0", farthestScope);
        c.addDependency(dFarthest);

        /* system and provided dependencies are not transitive */
        if (!Artifact.SCOPE_SYSTEM.equals(nearestScope) && !Artifact.SCOPE_PROVIDED.equals(nearestScope)) {

//         greaterThan: 1MiB # match objects with size greater than this value (e.g. 1MiB)
//       purge:
//           # retainVersions: 0 # (default) delete all versions of the object. This option is the fastest.
//           # retainVersions: 5 # keep the latest 5 versions of the object.
//
//     - type: deleted # objects with delete marker as their latest version

    static final GetCheckedTypeValidator BEST_VALIDATOR = getBestValidator();

    enum WeakSetValidator implements GetCheckedTypeValidator {
      INSTANCE;

      /*
       * Static final fields are presumed to be fastest, based on our experience with
       * UnsignedBytesBenchmark. TODO(cpovirk): benchmark this
       */
      /*
       * A CopyOnWriteArraySet<WeakReference> is faster than a newSetFromMap of a MapMaker map with

The key features are:

* **Fast**: Very high performance, on par with **NodeJS** and **Go** (thanks to Starlette and Pydantic). [One of the fastest Python frameworks available](#performance).
* **Fast to code**: Increase the speed to develop features by about 200% to 300%. *
* **Fewer bugs**: Reduce about 40% of human (developer) induced errors. *

///

/// check | Inspired **FastAPI** to

Find a way to have a crazy performance.

That's why **FastAPI** is based on Starlette, as it is the fastest framework available (tested by third-party benchmarks).

///

### [Falcon](https://falconframework.org/) { #falcon }

The key features are:

* **Fast**: Very high performance, on par with **NodeJS** and **Go** (thanks to Starlette and Pydantic). [One of the fastest Python frameworks available](#performance).
* **Fast to code**: Increase the speed to develop features by about 200% to 300%. *
* **Fewer bugs**: Reduce about 40% of human (developer) induced errors. *

It was found that the directory staging committer was the fastest among the three, S3A connector should be configured with the following parameters for optimal results:

```
cat ${HADOOP_CONF_DIR}/core-site.xml | kv-pairify | grep "s3a"

fs.s3a.access.key=minio

   * previously configured separator between each.
   */
  public String join(Iterable<?> parts) {
    /*
     * If we can quickly determine how many elements there are likely to be, then we can use the
     * fastest possible implementation, which delegates to the array overload of String.join.
     *
     * In theory, we can quickly determine the size of any Collection. However, thanks to

      /*
       * The following static final fields exist for performance reasons.
       *
       * In UnsignedBytesBenchmark, accessing the following objects via static final fields is the
       * fastest (more than twice as fast as the Java implementation, vs ~1.5x with non-final static
       * fields, on x86_32) under the Hotspot server compiler. The reason is obviously that the