This is normally called a **webhook**.

## Webhooks steps

The process normally is that **you define** in your code what is the message that you will send, the **body of the request**.

You also define in some way at which **moments** your app will send those requests or events.

And **your users** define in some way (for example in a web dashboard somewhere) the **URL** where your app should send those requests.

# Default paths for TF_SYSTEM_LIBS
build:linux --define=PREFIX=/usr
build:linux --define=LIBDIR=$(PREFIX)/lib
build:linux --define=INCLUDEDIR=$(PREFIX)/include
build:linux --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include
build:macos --define=PREFIX=/usr
build:macos --define=LIBDIR=$(PREFIX)/lib
build:macos --define=INCLUDEDIR=$(PREFIX)/include
build:macos --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include

         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>

  <!-- Shared parent. Doesn't define a lot of things about Apache like general mailing lists, but does
       define the settings common to all projects at Apache -->
  <groupId>org.apache</groupId>
  <artifactId>apache</artifactId>
  <version>1</version>
  <packaging>pom</packaging>

#### API "schema"

In this case, <a href="https://github.com/OAI/OpenAPI-Specification" class="external-link" target="_blank">OpenAPI</a> is a specification that dictates how to define a schema of your API.

This schema definition includes your API paths, the possible parameters they take, etc.

#### Data "schema"

The term "schema" might also refer to the shape of some data, like a JSON content.

Other modules and platforms define the services that they contribute to the runtime.

    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:
    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.
    * And validators allow complex data schemas to be clearly and easily defined, checked and documented as JSON Schema.

	return fmt.Sprintf("minio-%s.log", fmt.Sprintf("%X", time.Now().UTC().UnixNano()))
}

// Options define configuration options for Writer
type Options struct {
	// Directory defines the directory where log files will be written to.
	// If the directory does not exist, it will be created.
	Directory string

	// MaximumFileSize defines the maximum size of each log file in bytes.
	MaximumFileSize int64

Here we define the config `env_file` inside of your Pydantic `Settings` class, and set the value to the filename with the dotenv file we want to use.

### Creating the `Settings` only once with `lru_cache`

export ISTIO_GO
SHELL := /bin/bash -o pipefail

# Version can be defined:
# (1) in a $VERSION shell variable, which takes precedence; or
# (2) in the VERSION file, in which we will append "-dev" to it
ifeq ($(VERSION),)
VERSION_FROM_FILE := $(shell cat VERSION)
ifeq ($(VERSION_FROM_FILE),)
$(error VERSION not detected. Make sure it's stored in the VERSION file or defined in VERSION variable)
endif
VERSION := $(VERSION_FROM_FILE)-dev
endif

* **Cloud services** that handle this for you
    * The cloud service will probably **handle replication for you**. It would possibly let you define **a process to run**, or a **container image** to use, in any case, it would most probably be **a single Uvicorn process**, and the cloud service would be in charge of replicating it.

!!! tip