// GlobalStaleUploadsCleanupInterval - Cleanup interval when the stale uploads cleanup is initiated.
	GlobalStaleUploadsCleanupInterval = time.Hour * 6 // 6 hrs.

	// Refresh interval to update in-memory iam config cache.
	globalRefreshIAMInterval = 10 * time.Minute

	// Limit of location constraint XML for unauthenticated PUT bucket operations.
	maxLocationConstraintSize = 3 * humanize.MiByte

                """
            ),
        ],
        refreshUrl: Annotated[
            Optional[str],
            Doc(
                """
                The URL to refresh the token and obtain a new one.
                """
            ),
        ] = None,
        scheme_name: Annotated[
            Optional[str],
            Doc(
                """

    }

    public void reloadConfiguration() {
        reloadConfiguration(true);
    }

    public void reloadConfiguration(final boolean resetJobs) {
        ComponentUtil.getSearchEngineClient().refresh();

        ComponentUtil.getSuggestHelper().init();
        ComponentUtil.getPopularWordHelper().init();

        ComponentUtil.getLabelTypeHelper().update();
        ComponentUtil.getPathMappingHelper().update();

        "options": [],
        "query": "prometheus",
        "queryValue": "",
        "refresh": 1,
        "regex": "",
        "skipUrlSync": false,
        "type": "datasource"
      }
    ]
  },
  "time": {
    "from": "now-30m",
    "to": "now"
  },
  "timepicker": {
    "refresh_intervals": [
      "30s",
      "1m",
      "5m",
      "15m",
      "30m",

The steps are:

* Create a SQLAlchemy model *instance* with your data.
* `add` that instance object to your database session.
* `commit` the changes to the database (so that they are saved).
* `refresh` your instance (so that it contains any new data from the database, like the generated ID).

```Python hl_lines="18-24  31-36"
{!../../../docs_src/sql_databases/sql_app/crud.py!}
```

!!! info

index.reindex.size=100
index.reindex.body={"source":{"index":"__SOURCE_INDEX__","size":__SIZE__},"dest":{"index":"__DEST_INDEX__"},"script":{"source":"__SCRIPT_SOURCE__"}}
index.reindex.requests_per_second=adaptive
index.reindex.refresh=false
index.reindex.timeout=1m
index.reindex.scroll=5m
index.reindex.max_docs=

# query
query.max.length=1000
query.timeout=10000
query.timeout.logging=true
query.track.total.hits=10000

### 创建数据

现在创建实用程序函数来创建数据。

它的步骤是：

* 使用您的数据创建一个 SQLAlchemy 模型*实例。*
* 使用`add`来将该实例对象添加到您的数据库。
* 使用`commit`来对数据库的事务提交（以便保存它们）。
* 使用`refresh`来刷新您的数据库实例（以便它包含来自数据库的任何新数据，例如生成的 ID）。

```Python hl_lines="18-24  31-36"
{!../../../docs_src/sql_databases/sql_app/crud.py!}
```

!!! tip
    SQLAlchemy 模型`User`包含一个`hashed_password`，它应该是一个包含散列的安全密码。

### ✍ 💽

🔜 ✍ 🚙 🔢 ✍ 💽.

🔁:

* ✍ 🇸🇲 🏷 *👐* ⏮️ 👆 📊.
* `add` 👈 👐 🎚 👆 💽 🎉.
* `commit` 🔀 💽 (👈 👫 🖊).
* `refresh` 👆 👐 (👈 ⚫️ 🔌 🙆 🆕 📊 ⚪️➡️ 💽, 💖 🏗 🆔).

```Python hl_lines="18-24  31-36"
{!../../../docs_src/sql_databases/sql_app/crud.py!}
```

!!! tip
    🇸🇲 🏷 `User` 🔌 `hashed_password` 👈 🔜 🔌 🔐 #️⃣ ⏬ 🔐.

  // HPAContainerMetrics is enabled
  optional string type = 1;

  // object refers to a metric describing a single kubernetes object
  // (for example, hits-per-second on an Ingress object).
  // +optional
  optional ObjectMetricSource object = 2;

  // pods refers to a metric describing each pod in the current scale target
  // (for example, transactions-processed-per-second).  The values will be

  // HPAContainerMetrics is enabled
  optional string type = 1;

  // object refers to a metric describing a single kubernetes object
  // (for example, hits-per-second on an Ingress object).
  // +optional
  optional ObjectMetricSource object = 2;

  // pods refers to a metric describing each pod in the current scale target
  // (for example, transactions-processed-per-second).  The values will be