// ContainerResourceMetricSource indicates how to scale on a resource metric known to
// Kubernetes, as specified in the requests and limits, describing a single container in
// each of the pods of 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 into
// Kubernetes, and have special scaling options on top of those available to

```
./bin/spark-submit --class org.apache.spark.examples.SparkPi \
    --master yarn-client \
    --num-executors 1 \
    --driver-memory 512m \
    --executor-memory 512m \
    --executor-cores 1 \
    examples/jars/spark-examples*.jar 10
```

The job should produce an output as shown below. Note the value of pi in the output.

```

* 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()

the value in the form of a `Handle[T]`.
Two `Handle[T]` are equal if and only if the values used to produce the
handles are equal, allowing programs to deduplicate values and reduce
their memory footprint.
Comparing two `Handle[T]` values is efficient, reducing down to a simple

- A maximum of 100M inside a single zip is allowed. However, a reasonable limit of 100,000 files inside a single ZIP archive is recommended for best performance and memory usage trade-off.

## Content-Type

* 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.

models or graphs is equivalent to running untrusted code.

If you need to run untrusted models, execute them inside a
[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions
in TensorFlow ops can be recognized as security issues only if they are
reachable and exploitable through production-grade, benign models.

### Compilation

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

# ADR-0002 - Avoid using Java serialization

## Date

2012-12-01

## Context

In Gradle we often need to serialize in-memory objects for caching, or to transmit them across process barriers, etc.
Java serialization is one way to implement this, however, despite its simplicity of implementation, it has several drawbacks:

- **Performance:**
Java's built-in serialization mechanism is often slower compared to other serialization solutions.

 */
package okhttp3.internal.tls

import java.security.cert.X509Certificate
import javax.security.auth.x500.X500Principal

/** A simple index that of trusted root certificates that have been loaded into memory. */
class BasicTrustRootIndex(vararg caCerts: X509Certificate) : TrustRootIndex {
  private val subjectToCaCerts: Map<X500Principal, Set<X509Certificate>>

  init {