- Reduced server startup time. For IAM encryption with the root credentials, MinIO had
   to use a memory-hard function (Argon2) that (on purpose) consumes a lot of memory and CPU.
   The new KMS-based approach can use a key derivation function that is orders of magnitudes
   cheaper w.r.t. memory and CPU.
- Root credentials can now be changed easily. Before, a two-step process was required to

* 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.

          "build-cache",
          "flow-services",
          "input-tracking",
          "internal-integ-testing",
          "model-core",
          "persistent-cache",
          "process-memory-services",
          "scala",
          "snapshots",
          "testing-base-infrastructure"
        ],
        "parallelizationMethod": {
          "name": "TestDistribution"
        }
      },

/**
 * A pluggable service for processing model objects during model building.
 *
 * <p>This service allows implementations to:</p>
 * <ul>
 *   <li>Pool identical objects to reduce memory footprint</li>
 *   <li>Intern objects for faster equality comparisons</li>
 *   <li>Apply custom optimization strategies</li>
 *   <li>Transform or modify objects during building</li>
 * </ul>
 *

podAnnotations: {}

# Additional pod labels
podLabels: {}

## Configure resource requests and limits
## ref: http://kubernetes.io/docs/user-guide/compute-resources/
##
resources:
  requests:
    memory: 16Gi

## List of policies to be created after minio install
##
## In addition to default policies [readonly|readwrite|writeonly|consoleAdmin|diagnostics]

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

## Memory { #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).

 * 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

 * 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

- **Caching** - Bean descriptors and reflection metadata are cached for improved performance
- **Lazy initialization** - Resources and expensive operations are initialized only when needed  
- **Memory efficient** - Specialized collections like `LruHashMap` and `ArrayMap` for memory-conscious applications
- **Java 21 features** - Switch expressions and pattern matching for reduced overhead

## 🧪 Building and Testing

### Development Setup
```bash

* 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()