### Memory per Process

Now, when the program loads things in memory, for example, a machine learning model in a variable, or the contents of a large file in a variable, all that **consumes a bit of the memory (RAM)** of the server.

| `minio_system_memory_total`      | `gauge` | Total memory on the node           | `server` |
| `minio_system_memory_buffers`    | `gauge` | Buffers memory on the node         | `server` |
| `minio_system_memory_cache`      | `gauge` | Cache memory on the node           | `server` |
| `minio_system_memory_shared`     | `gauge` | Shared memory on the node          | `server` |

| `minio_node_mem_buffers_avg`   | Buffers memory on the node (avg).         |
| `minio_node_mem_buffers_max`   | Buffers memory on the node (max).         |
| `minio_node_mem_cache`         | Cache memory on the node.                 |
| `minio_node_mem_cache_avg`     | Cache memory on the node (avg).           |
| `minio_node_mem_cache_max`     | Cache memory on the node (max).           |

* 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).

    *   Object metadata has now been deprecated and no longer saved to the
        SavedModel.

*   TF Core:

    *   Added `tf.config.experimental.reset_memory_stats` to reset the tracked
        peak memory returned by `tf.config.experimental.get_memory_info`.

*   `tf.data`:

    *   Added `target_workers` param to `data_service_ops.from_dataset_id` and

```

!!! tip
    The app above is a minimal and simple example to demonstrate how to handle and broadcast messages to several WebSocket connections.

    But keep in mind that, as everything is handled in memory, in a single list, it will only work while the process is running, and will only work with a single process.

This is the basic idea. But you will probably want to take care of some additional things, like:

* Security - HTTPS
* Running on startup
* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting

* Ensure traffic between mesh workloads is securely encrypted with an Istio identity.
* Be lightweight enough to not limit adoption.
  * This puts a much tighter budget on CPU, memory, latency, and throughput requirements than traditional Istio sidecars.

Ztunnel was not designed to be a feature-rich data plane.
Quite the opposite - an *aggressively* small feature set is the key feature that makes ztunnel viable.

test:rbe_cross_compile_macos_x86 --config=rbe_cross_compile_base
# Increase the test timeout as tests often take longer on mac.
test:rbe_cross_compile_macos_x86 --test_timeout=300,450,1200,3600
# Limit jobs to 100 to avoid running into "out of memory" issues (b/316266643)
build:rbe_cross_compile_macos_x86 --jobs=100
test:rbe_cross_compile_macos_x86 --jobs=100
# END MACOS CROSS-COMPILE CONFIGS
# END CROSS-COMPILE CONFIGS

    * This was reported internally by [@rushilsrivastava](https://github.com/rushilsrivastava) as a memory leak when the server had unhandled exceptions that would produce internal server errors, the memory allocated before that point would not be released.