// ContainerResourceMetricSource indicates how to scale on a resource metric known to
// Kubernetes, as specified in requests and limits, describing each pod in the
// current scale target (e.g. CPU or memory).  The values will be averaged
// together before being compared to the target.  Such metrics are built in to
// Kubernetes, and have special scaling options on top of those available to

// ContainerResourceMetricSource indicates how to scale on a resource metric known to
// Kubernetes, as specified in requests and limits, describing each pod in the
// current scale target (e.g. CPU or memory).  The values will be averaged
// together before being compared to the target.  Such metrics are built in to
// Kubernetes, and have special scaling options on top of those available to

main Istio components normalized by 1000 requests/second. When idle or low traffic, this chart will be blank. The curve for istio-proxy refers to the services sidecars only.\n- **vCPU:** vCPU utilization by Istio components, not normalized.\n- **Memory:** memory footprint for the components. Telemetry and policy are normalized by 1k rps, and no data is shown  when there is no traffic. For ingress and istio-proxy, the data is per instance.\n- **Bytes transferred / sec:** shows the number of bytes flowing...

	processResidentMemoryBytesMD      = NewGaugeMD(processResidentMemoryBytes, "Resident memory size in bytes")
	processVirtualMemoryBytesMD       = NewGaugeMD(processVirtualMemoryBytes, "Virtual memory size in bytes")
	processVirtualMemoryMaxBytesMD    = NewGaugeMD(processVirtualMemoryMaxBytes, "Maximum virtual memory size in bytes")
)

func loadProcStatMetrics(ctx context.Context, stat procfs.ProcStat, m MetricValues) {

* You don't have to use `File()` in the default value of the parameter.
* It uses a "spooled" file:
    * A file stored in memory up to a maximum size limit, and after passing this limit it will be stored in disk.
* This means that it will work well for large files like images, videos, large binaries, etc. without consuming all the memory.
* You can get metadata from the uploaded file.

* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting

## Memory

If you run **a single process per container** you will have a more or less well-defined, stable, and limited amount of memory consumed by each of those containers (more than one if they are replicated).

    snowball: # automatically activated if the source is local
      disable: true # optionally turn-off snowball archive transfer
#      batch: 100 # upto this many objects per archive
#      inmemory: true # indicates if the archive must be staged locally or in-memory
#      compress: false # S2/Snappy compressed archive
#      smallerThan: 5MiB # create archive for all objects smaller than 5MiB
#      skipErrs: false # skips any source side read() errors

 * java.util.LinkedHashSet}. Generally speaking, this class reduces object allocation and memory
 * consumption at the price of moderately increased constant factors of CPU. Only use this class
 * when there is a specific reason to prioritize memory over CPU.
 *
 * @author Louis Wasserman
 */
@GwtIncompatible // not worth using in GWT for now
@ElementTypesAreNonnullByDefault

FESS_LOG_PATH=${packaging.fess.log.dir}
FESS_CONF_PATH=${packaging.fess.conf.dir}
FESS_VAR_PATH=${packaging.fess.var.dir}

FESS_DICTIONARY_PATH=/var/lib/opensearch/config/

# Port
FESS_PORT=8080

# Heap Memory
FESS_HEAP_SIZE=512m

# Elasticsearch Path
SEARCH_ENGINE_HOME=/usr/share/opensearch/

# Elasticsearch URL

* 403: `status.HTTP_403_FORBIDDEN`
* etc.

It can be convenient to quickly access HTTP (and WebSocket) status codes in your app, using autocompletion for the name without having to remember the integer status codes by memory.

Read more about it in the [FastAPI docs about Response Status Code](https://fastapi.tiangolo.com/tutorial/response-status-code/).

## Example

```python
from fastapi import FastAPI, status

app = FastAPI()