* specific length.
   *
   * <p>For example, if you need 1024-bit hash codes, you could join two {@link Hashing#sha512} hash
   * functions together: {@code Hashing.concatenating(Hashing.sha512(), Hashing.sha512())}.
   *
   * @since 19.0
   */
  public static HashFunction concatenating(
      HashFunction first, HashFunction second, HashFunction... rest) {

# OAuth2 mit Password (und Hashing), Bearer mit JWT-Tokens

Da wir nun über den gesamten Sicherheitsablauf verfügen, machen wir die Anwendung tatsächlich sicher, indem wir <abbr title="JSON Web Tokens">JWT</abbr>-Tokens und sicheres Passwort-Hashing verwenden.

Diesen Code können Sie tatsächlich in Ihrer Anwendung verwenden, die Passwort-Hashes in Ihrer Datenbank speichern, usw.

Wir bauen auf dem vorherigen Kapitel auf.

## Über JWT

## Password hashing

"Hashing" means converting some content (a password in this case) into a sequence of bytes (just a string) that looks like gibberish.

Whenever you pass exactly the same content (exactly the same password) you get exactly the same gibberish.

But you cannot convert from the gibberish back to the password.

### Why use password hashing

Sie sollten niemals Klartext-Passwörter speichern, daher verwenden wir ein (gefaktes) Passwort-Hashing-System.

Wenn die Passwörter nicht übereinstimmen, geben wir denselben Fehler zurück.

#### Passwort-Hashing

„Hashing“ bedeutet: Konvertieren eines Inhalts (in diesem Fall eines Passworts) in eine Folge von Bytes (ein schlichter String), die wie Kauderwelsch aussieht.

Let's put that data in the Pydantic `UserInDB` model first.

You should never save plaintext passwords, so, we'll use the (fake) password hashing system.

If the passwords don't match, we return the same error.

#### Password hashing

"Hashing" means: converting some content (a password in this case) into a sequence of bytes (just a string) that looks like gibberish.

   * double the index of the entry in entrySet.asList.)
   *
   * The basic data structure is described in https://en.wikipedia.org/wiki/Open_addressing.
   * The pointer to a key is stored in hashTable[Hashing.smear(key.hashCode()) % table.length],
   * save that if that location is already full, we try the next index, and the next, until we
   * find an empty table position.  Since the table has a power-of-two size, we use

      LocalCache<Object, Object> map = makeLocalCache(builder);

      Object keyOne = new Object();
      Object valueOne = new Object();
      int hashOne = map.hash(keyOne);
      ReferenceEntry<Object, Object> entryOne = map.newEntry(keyOne, hashOne, null);
      ValueReference<Object, Object> valueRefOne = map.newValueReference(entryOne, valueOne, 1);
      assertSame(valueOne, valueRefOne.get());

      LocalCache<Object, Object> map = makeLocalCache(builder);

      Object keyOne = new Object();
      Object valueOne = new Object();
      int hashOne = map.hash(keyOne);
      ReferenceEntry<Object, Object> entryOne = map.newEntry(keyOne, hashOne, null);
      ValueReference<Object, Object> valueRefOne = map.newValueReference(entryOne, valueOne, 1);
      assertSame(valueOne, valueRefOne.get());

      this.key = key;
      this.firstEntry = this;
      this.lastEntry = this;
      // Round expected values up to a power of 2 to get the table size.
      int tableSize = Hashing.closedTableSize(expectedValues, VALUE_SET_LOAD_FACTOR);

      @SuppressWarnings({"rawtypes", "unchecked"})
      @Nullable
      ValueEntry<K, V>[] hashTable = new @Nullable ValueEntry[tableSize];

  }

  @Override
  public boolean contains(@CheckForNull Object target) {
    @Nullable Object[] table = this.table;
    if (target == null || table.length == 0) {
      return false;
    }
    for (int i = Hashing.smearedHash(target); ; i++) {
      i &= mask;
      Object candidate = table[i];
      if (candidate == null) {
        return false;
      } else if (candidate.equals(target)) {
        return true;
      }