@Override
  public Hasher newHasher() {
    return new SipHasher(c, d, k0, k1);
  }

  // TODO(kak): Implement and benchmark the hashFoo() shortcuts.

  @Override
  public String toString() {
    return "Hashing.sipHash" + c + "" + d + "(" + k0 + ", " + k1 + ")";
  }

  @Override
  public boolean equals(@CheckForNull Object object) {
    if (object instanceof SipHashFunction) {

Guava is a set of core Java libraries from Google that includes new collection
types (such as multimap and multiset), immutable collections, a graph library,
and utilities for concurrency, I/O, hashing, primitives, strings, and more! It
is widely used on most Java projects within Google, and widely used by many
other companies as well.



Guava comes in two flavors:

 * if any intersection is found. The sizes of both arguments are assumed to be so small, and the
 * likelihood of an intersection so great, that it is not worth the CPU cost of sorting or the
 * memory cost of hashing.
 */
internal fun Array<String>.hasIntersection(
  other: Array<String>?,
  comparator: Comparator<in String>,
): Boolean {
  if (isEmpty() || other == null || other.isEmpty()) {
    return false
  }

> **NOTE:** **Each pool you add must have the same erasure coding parity configuration as the original pool, so the same data redundancy SLA is maintained.**

## 3. Test your setup

          .put(3, 0x9fcc3c3fde4d5ff7L)
          .put(2, 0x090966a836e5fa4bL)
          .put(1, 0x8199675ecaa6fe64L)
          .put(0, 0x23ad7c904aa665e3L)
          .build();
  private static final HashFunction HASH_FN = Hashing.fingerprint2011();

  // If this test fails, all bets are off
  public void testReallySimpleFingerprints() {
    assertEquals(8473225671271759044L, fingerprint("test".getBytes(UTF_8)));
    // 32 characters long

    Map<K, Collection<V>> result = asMap;
    return (result == null) ? asMap = createAsMap() : result;
  }

  abstract Map<K, Collection<V>> createAsMap();

  // Comparison and hashing

  @Override
  public boolean equals(@CheckForNull Object object) {
    return Multimaps.equalsImpl(this, object);
  }

  /**
   * Returns the hash code for this multimap.
   *

      addressAsLong = ByteBuffer.wrap(ip6.getAddress(), 0, 8).getLong();
    }

    // Many strategies for hashing are possible. This might suffice for now.
    int coercedHash = Hashing.murmur3_32_fixed().hashLong(addressAsLong).asInt();

    // Squash into 224/4 Multicast and 240/4 Reserved space (i.e. 224/3).
    coercedHash |= 0xe0000000;

		// time when fresh disks were supplied, it is a two dimensional
		// array second dimension represents list of disks used per set.
		Sets [][]string `json:"sets"`
		// Distribution algorithm represents the hashing algorithm
		// to pick the right set index for an object.
		DistributionAlgo string `json:"distributionAlgo"`
	} `json:"xl"`
}

书接上文，多个关联模型这种情况很常见。

特别是用户模型，因为：

* **输入模型**应该含密码
* **输出模型**不应含密码
* **数据库模型**需要加密的密码

/// danger | "危险"

千万不要存储用户的明文密码。始终存储可以进行验证的**安全哈希值**。

如果不了解这方面的知识，请参阅[安全性中的章节](security/simple-oauth2.md#password-hashing){.internal-link target=_blank}，了解什么是**密码哈希**。

///

## 多个模型

下面的代码展示了不同模型处理密码字段的方式，及使用位置的大致思路：

//// tab | Python 3.10+

```Python hl_lines="7  9  14  20  22  27-28  31-33  38-39"

Никогда не храните пароли пользователей в чистом виде. Всегда храните "безопасный хэш", который вы затем сможете проверить.

Если вам это не знакомо, вы можете узнать про "хэш пароля" в [главах о безопасности](security/simple-oauth2.md#password-hashing){.internal-link target=_blank}.

///

## Множественные модели

Ниже изложена основная идея того, как могут выглядеть эти модели с полями для паролей, а также описаны места, где они используются: