Speichern Sie niemals das Klartext-Passwort eines Benutzers. Speichern Sie immer den „sicheren Hash“, den Sie verifizieren können.

Falls Ihnen das nichts sagt, in den [Sicherheits-Kapiteln](security/simple-oauth2.md#passwort-hashing){.internal-link target=_blank} werden Sie lernen, was ein „Passwort-Hash“ ist.

///

## Mehrere Modelle

      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;

/// danger

Never store user's plaintext passwords. Always store a "secure hash" that you can then verify.

If you don't know, you will learn what a "password hash" is in the [security chapters](security/simple-oauth2.md#password-hashing){.internal-link target=_blank}.

///

## Multiple models

Here's a general idea of how the models could look like with their password fields and the places where they are used:

//// tab | Python 3.10+

import java.nio.ByteBuffer;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import java.util.Collections;
import java.util.Comparator;
import java.util.EnumSet;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Iterator;
import java.util.LinkedHashMap;
import java.util.LinkedHashSet;
import java.util.List;
import java.util.Map;
import java.util.NavigableSet;

            EVERYONE = new SID("S-1-1-0");
            CREATOR_OWNER = new SID("S-1-3-0");
            SYSTEM = new SID("S-1-5-18");
        } catch (SmbException se) {
        }
    }

    static Map sid_cache = new HashMap();

    static void resolveSids(DcerpcHandle handle,
                LsaPolicyHandle policyHandle,
                SID[] sids) throws IOException {
        MsrpcLookupSids rpc = new MsrpcLookupSids(policyHandle, sids);

 * 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

                    }
                    return null;
                }).filter(o -> o != null && keySet.add(o.getFirst())).collect(HashMap<String, List<String>>::new,
                        (m, d) -> m.put(d.getFirst(), Arrays.asList(d.getSecond().split(","))), HashMap::putAll));
            }
            propMap.put(DEFAULT_LABEL_VALUES, map);
        }
        return map.entrySet().stream().flatMap(e -> {

		// 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"`
}

	}
	delay := job.Replicate.Flags.Retry.Delay
	if delay <= 0 {
		delay = batchReplJobDefaultRetryDelay
	}
	rnd := rand.New(rand.NewSource(time.Now().UnixNano()))

	hasTags := len(r.Flags.Filter.Tags) != 0
	isMetadata := len(r.Flags.Filter.Metadata) != 0
	isStorageClassOnly := len(r.Flags.Filter.Metadata) == 1 && strings.EqualFold(r.Flags.Filter.Metadata[0].Key, xhttp.AmzStorageClass)