* **보안 - HTTPS**
* **서버 시작 시 실행**
* ***재시작***
* 복제(실행 중인 프로세스 수)
* **메모리**
* **시작하기 전의 이전 단계**

## 컨테이너와 도커 { #containers-and-docker }

다음 장인 [컨테이너에서의 FastAPI - 도커](docker.md)에서는 다른 **배포 개념들**을 처리하기 위해 사용할 수 있는 몇 가지 전략을 설명하겠습니다.

단일 Uvicorn 프로세스를 실행하기 위해, **처음부터 여러분만의 이미지를 직접 빌드**하는 방법을 보여드리겠습니다. 이는 간단한 과정이며, **Kubernetes** 같은 분산 컨테이너 관리 시스템을 사용할 때 아마도 이렇게 하고 싶을 것입니다.

      } catch (e: Exception) {
        AndroidLog.androidLog(
          loggerName = OkHttpClient::class.java.name,
          logLevel = Platform.WARN,
          message = "unable to load android socket classes",
          t = e,
        )
        null
      }
  }

        uses: astral-sh/setup-uv@v7
        with:
          enable-cache: true
          cache-dependency-glob: |
            pyproject.toml
            uv.lock
      - name: Install docs extras
        run: uv sync --locked --no-dev --group docs
      - name: Export Language Codes
        id: show-langs
        run: |
          echo "langs=$(uv run ./scripts/docs.py langs-json)" >> $GITHUB_OUTPUT

  build-docs:
    needs:

```json
{
  "version": "1",
  "deploymentid": "90e81272-45d9-4fe8-9c45-c9a7322bf4b5",
  "time": "2024-05-09T07:38:10.449688982Z",
  "event": "",
  "trigger": "incoming",
  "api": {
    "name": "PutObject",
    "bucket": "testbucket",
    "object": "hosts",
    "status": "OK",
    "statusCode": 200,
    "rx": 401,
    "tx": 0,
    "timeToResponse": "13309747ns",
    "timeToResponseInNS": "13309747"
  },

import org.opensearch.search.aggregations.bucket.filter.FilterAggregationBuilder;
import org.opensearch.search.aggregations.bucket.global.GlobalAggregationBuilder;
import org.opensearch.search.aggregations.bucket.histogram.HistogramAggregationBuilder;
import org.opensearch.search.aggregations.bucket.missing.MissingAggregationBuilder;
import org.opensearch.search.aggregations.bucket.range.IpRangeAggregationBuilder;

    execution time and the sharding
    [could create an overhead on the test execution](https://github.com/bazelbuild/bazel/issues/2113#issuecomment-264054799).

2.  Using [Docker](https://www.docker.com) and TensorFlow's CI scripts.

    ```bash
    # Install Docker first, then this will build and run cpu tests
    tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/...
    ```

    See

	// Therefore, find the first bucket that holds the counter for
	// requests with a latency at least as large as the given request
	// latency and update its and all subsequent counters.
	bucket := slices.IndexFunc(k.latencyBuckets, func(b time.Duration) bool { return latency < b })
	if bucket < 0 {
		bucket = len(k.latencyBuckets) - 1
	}
	for i := bucket; i < len(k.latency); i++ {
		k.latency[i].Add(1)
	}

import org.opensearch.search.aggregations.bucket.filter.FilterAggregationBuilder;
import org.opensearch.search.aggregations.bucket.global.GlobalAggregationBuilder;
import org.opensearch.search.aggregations.bucket.histogram.HistogramAggregationBuilder;
import org.opensearch.search.aggregations.bucket.missing.MissingAggregationBuilder;
import org.opensearch.search.aggregations.bucket.range.IpRangeAggregationBuilder;

### Cloud Storage Clients

- **S3Client**: AWS SDK v2, `s3://bucket/path`, properties: `endpoint`, `accessKey`, `secretKey`, `region`
- **GcsClient**: Google Cloud SDK, `gcs://bucket/path`, properties: `projectId`, `credentialsFile`, `endpoint`
- **StorageClient**: MinIO SDK, `storage://bucket/path`

### Services

- **UrlQueueService**: URL queue management (FIFO), duplicate detection

	}
	return dst
}

// bucketsUsageInfo returns the buckets usage info as a map, with
// key as bucket name
func (d *dataUsageCache) bucketsUsageInfo(buckets []BucketInfo) map[string]BucketUsageInfo {
	dst := make(map[string]BucketUsageInfo, len(buckets))
	for _, bucket := range buckets {
		e := d.find(bucket.Name)
		if e == nil {
			continue
		}
		flat := d.flatten(*e)
		bui := BucketUsageInfo{