Add diagrams test

.gitignore

@@ -130,6 +130,7 @@ celerybeat.pid
 .venv
 env/
 venv/
+myenv/
 ENV/
 env.bak/
 venv.bak/

catch-all/07_diagrams_as_code/01_workers_aws.py

@@ -0,0 +1,15 @@
+#! /usr/bin/env python
+
+from diagrams import Diagram
+from diagrams.aws.compute import EC2
+from diagrams.aws.database import RDS
+from diagrams.aws.network import ELB
+
+with Diagram("Grouped Workers", show=False, direction="TB"):
+    ELB("lb") >> [
+        EC2("worker1"),
+        EC2("worker2"),
+        EC2("worker3"),
+        EC2("worker4"),
+        EC2("worker5")
+    ] >> RDS("events")

catch-all/07_diagrams_as_code/02_cluster_web.py

@@ -0,0 +1,28 @@
+#! /usr/bin/env python
+
+from diagrams import Cluster, Diagram
+from diagrams.aws.compute import ECS
+from diagrams.aws.database import ElastiCache, RDS
+from diagrams.aws.network import ELB
+from diagrams.aws.network import Route53
+
+with Diagram("Clustered Web Services", show=False):
+    dns = Route53("dns")
+    lb = ELB("lb")
+
+    with Cluster("Services"):
+        svc_group = [
+            ECS("web1"),
+            ECS("web2"),
+            ECS("web3")
+        ]
+
+    with Cluster("DB Cluster"):
+        db_primary = RDS("userdb")
+        db_primary - [RDS("userdb ro")]
+
+    memcached = ElastiCache("memcached")
+
+    dns >> lb >> svc_group
+    svc_group >> db_primary
+    svc_group >> memcached

catch-all/07_diagrams_as_code/03_event_aws.py

@@ -0,0 +1,32 @@
+#! /usr/bin/env python
+
+from diagrams import Cluster, Diagram
+from diagrams.aws.compute import ECS, EKS, Lambda
+from diagrams.aws.database import Redshift
+from diagrams.aws.integration import SQS
+from diagrams.aws.storage import S3
+
+with Diagram("Event Processing", show=False):
+    source = EKS("k8s source")
+
+    with Cluster("Event Flows"):
+        with Cluster("Event Workers"):
+            workers = [
+                ECS("worker1"),
+                ECS("worker2"),
+                ECS("worker3")
+            ]
+
+        queue = SQS("event queue")
+
+        with Cluster("Processing"):
+            handlers = [Lambda("proc1"),
+                        Lambda("proc2"),
+                        Lambda("proc3")]
+
+    store = S3("events store")
+    dw = Redshift("analytics")
+
+    source >> workers >> queue >> handlers
+    handlers >> store
+    handlers >> dw

catch-all/07_diagrams_as_code/04_message_gcp.py

@@ -0,0 +1,37 @@
+#! /usr/bin/env python
+
+from diagrams import Cluster, Diagram
+from diagrams.gcp.analytics import BigQuery, Dataflow, PubSub
+from diagrams.gcp.compute import AppEngine, Functions
+from diagrams.gcp.database import BigTable
+from diagrams.gcp.iot import IotCore
+from diagrams.gcp.storage import GCS
+
+with Diagram("Message Collecting", show=False):
+    pubsub = PubSub("pubsub")
+
+    with Cluster("Source of Data"):
+        [
+            IotCore("core1"),
+            IotCore("core2"),
+            IotCore("core3")
+        ] >> pubsub
+
+    with Cluster("Targets"):
+        with Cluster("Data Flow"):
+            flow = Dataflow("data flow")
+
+        with Cluster("Data Lake"):
+            flow >> [
+                BigQuery("bq"),
+                GCS("storage")
+            ]
+
+        with Cluster("Event Driven"):
+            with Cluster("Processing"):
+                flow >> AppEngine("engine") >> BigTable("bigtable")
+
+            with Cluster("Serverless"):
+                flow >> Functions("func") >> AppEngine("appengine")
+
+    pubsub >> flow

catch-all/07_diagrams_as_code/05_exposed_pod_k8s.py

@@ -0,0 +1,12 @@
+#! /usr/bin/env python
+
+from diagrams import Diagram
+from diagrams.k8s.clusterconfig import HPA
+from diagrams.k8s.compute import Deployment, Pod, ReplicaSet
+from diagrams.k8s.network import Ingress, Service
+
+with Diagram("Exposed Pod with 3 Replicas", show=False):
+    net = Ingress("domain.com") >> Service("svc")
+    net >> [Pod("pod1"),
+            Pod("pod2"),
+            Pod("pod3")] << ReplicaSet("rs") << Deployment("dp") << HPA("hpa")

catch-all/07_diagrams_as_code/06_stateful_k8s.py

@@ -0,0 +1,20 @@
+#! /usr/bin/env python
+
+from diagrams import Cluster, Diagram
+from diagrams.k8s.compute import Pod, StatefulSet
+from diagrams.k8s.network import Service
+from diagrams.k8s.storage import PV, PVC, StorageClass
+
+with Diagram("Stateful Architecture", show=False):
+    with Cluster("Apps"):
+        svc = Service("svc")
+        sts = StatefulSet("sts")
+
+        apps = []
+        for _ in range(3):
+            pod = Pod("pod")
+            pvc = PVC("pvc")
+            pod - sts - pvc
+            apps.append(svc >> pod >> pvc)
+
+    apps << PV("pv") << StorageClass("sc")

catch-all/07_diagrams_as_code/07_web_onpremise.py

@@ -0,0 +1,39 @@
+#! /usr/bin/env python
+
+from diagrams import Cluster, Diagram
+from diagrams.onprem.analytics import Spark
+from diagrams.onprem.compute import Server
+from diagrams.onprem.database import PostgreSQL
+from diagrams.onprem.inmemory import Redis
+from diagrams.onprem.aggregator import Fluentd
+from diagrams.onprem.monitoring import Grafana, Prometheus
+from diagrams.onprem.network import Nginx
+from diagrams.onprem.queue import Kafka
+
+with Diagram("Advanced Web Service with On-Premise", show=False):
+    ingress = Nginx("ingress")
+
+    metrics = Prometheus("metric")
+    metrics << Grafana("monitoring")
+
+    with Cluster("Service Cluster"):
+        grpcsvc = [
+            Server("grpc1"),
+            Server("grpc2"),
+            Server("grpc3")
+        ]
+
+    with Cluster("Sessions HA"):
+        primary = Redis("session")
+        primary - Redis("replica") << metrics
+        grpcsvc >> primary
+
+    with Cluster("Database HA"):
+        primary = PostgreSQL("users")
+        primary - PostgreSQL("replica") << metrics
+        grpcsvc >> primary
+
+    aggregator = Fluentd("logging")
+    aggregator >> Kafka("stream") >> Spark("analytics")
+
+    ingress >> grpcsvc >> aggregator

catch-all/07_diagrams_as_code/08_web_onpremise_color.py

@@ -0,0 +1,43 @@
+#! /usr/bin/env python
+
+from diagrams import Cluster, Diagram, Edge
+from diagrams.onprem.analytics import Spark
+from diagrams.onprem.compute import Server
+from diagrams.onprem.database import PostgreSQL
+from diagrams.onprem.inmemory import Redis
+from diagrams.onprem.aggregator import Fluentd
+from diagrams.onprem.monitoring import Grafana, Prometheus
+from diagrams.onprem.network import Nginx
+from diagrams.onprem.queue import Kafka
+
+with Diagram(name="Advanced Web Service with On-Premise (colored)", show=False):
+    ingress = Nginx("ingress")
+
+    metrics = Prometheus("metric")
+    metrics << Edge(color="firebrick", style="dashed") << Grafana("monitoring")
+
+    with Cluster("Service Cluster"):
+        grpcsvc = [
+            Server("grpc1"),
+            Server("grpc2"),
+            Server("grpc3")
+        ]
+
+    with Cluster("Sessions HA"):
+        primary = Redis("session")
+        primary - Edge(color="brown", style="dashed") - \
+            Redis("replica") << Edge(label="collect") << metrics
+        grpcsvc >> Edge(color="brown") >> primary
+
+    with Cluster("Database HA"):
+        primary = PostgreSQL("users")
+        primary - Edge(color="brown", style="dotted") - \
+            PostgreSQL("replica") << Edge(label="collect") << metrics
+        grpcsvc >> Edge(color="black") >> primary
+
+    aggregator = Fluentd("logging")
+    aggregator >> Edge(label="parse") >> Kafka("stream") >> Edge(
+        color="black", style="bold") >> Spark("analytics")
+
+    ingress >> Edge(color="darkgreen") << grpcsvc >> Edge(
+        color="darkorange") >> aggregator

catch-all/07_diagrams_as_code/09_consumers_rabbitmq.py

@@ -0,0 +1,25 @@
+#! /usr/bin/env python
+
+from urllib.request import urlretrieve
+
+from diagrams import Cluster, Diagram
+from diagrams.aws.database import Aurora
+from diagrams.custom import Custom
+from diagrams.k8s.compute import Pod
+
+# Download an image to be used into a Custom Node class
+rabbitmq_url = "https://jpadilla.github.io/rabbitmqapp/assets/img/icon.png"
+rabbitmq_icon = "rabbitmq.png"
+urlretrieve(rabbitmq_url, rabbitmq_icon)
+
+with Diagram("Broker Consumers", show=False):
+    with Cluster("Consumers"):
+        consumers = [
+            Pod("worker"),
+            Pod("worker"),
+            Pod("worker")
+        ]
+
+    queue = Custom("Message queue", rabbitmq_icon)
+
+    queue >> consumers >> Aurora("Database")

catch-all/07_diagrams_as_code/README.md

@@ -0,0 +1,359 @@
+
+# Ejemplos de Diagramas como Código
+
+Este repositorio contiene ejemplos de diagramas de arquitectura generados mediante código utilizando la biblioteca `diagrams` en Python. Esta herramienta permite crear representaciones visuales de arquitecturas de software de manera programática.
+
+## Instalación
+
+Crear un entorno virtual y activarlo:
+
+```bash
+python3 -m venv venv
+source venv/bin/activate
+```
+
+
+Instalar la biblioteca `diagrams`, puedes utilizar `pip`:
+
+```bash
+pip install diagrams
+```
+
+Al terminal se puede desactivar el entorno virtual con el comando:
+
+```bash
+deactivate
+```
+
+
+## Ejemplos
+
+### Trabajadores Agrupados en AWS
+
+Este diagrama muestra un balanceador de carga (ELB) distribuyendo el tráfico a múltiples instancias de EC2, las cuales interactúan con una base de datos RDS.
+
+```python
+from diagrams import Diagram
+from diagrams.aws.compute import EC2
+from diagrams.aws.database import RDS
+from diagrams.aws.network import ELB
+
+with Diagram("Grouped Workers", show=False, direction="TB"):
+    ELB("lb") >> [EC2("worker1"),
+                  EC2("worker2"),
+                  EC2("worker3"),
+                  EC2("worker4"),
+                  EC2("worker5")] >> RDS("events")
+```
+
+<p align="center">
+  <img src="https://diagrams.mingrammer.com/img/grouped_workers_diagram.png" alt="Grouped Workers" width="500"/>
+</p>
+
+### Servicios Web en Clúster
+
+Este diagrama ilustra una arquitectura de servicios web en AWS. Incluye balanceo de carga, clúster de servicios, almacenamiento en caché y una base de datos principal con réplica.
+
+```python
+from diagrams import Cluster, Diagram
+from diagrams.aws.compute import ECS
+from diagrams.aws.database import ElastiCache, RDS
+from diagrams.aws.network import ELB
+from diagrams.aws.network import Route53
+
+with Diagram("Clustered Web Services", show=False):
+    dns = Route53("dns")
+    lb = ELB("lb")
+
+    with Cluster("Services"):
+        svc_group = [ECS("web1"),
+                     ECS("web2"),
+                     ECS("web3")]
+
+    with Cluster("DB Cluster"):
+        db_primary = RDS("userdb")
+        db_primary - [RDS("userdb ro")]
+
+    memcached = ElastiCache("memcached")
+
+    dns >> lb >> svc_group
+    svc_group >> db_primary
+    svc_group >> memcached
+```
+<p align="center">
+  <img src="https://diagrams.mingrammer.com/img/clustered_web_services_diagram.png" alt="Clustered Web Services" width="500"/>
+</p>
+
+### Procesamiento de Eventos en AWS
+
+El siguiente diagrama representa un flujo de procesamiento de eventos en AWS, utilizando fuentes de eventos, colas para manejar los eventos, procesamiento mediante Lambdas, y almacenamiento en Redshift y S3.
+
+```python
+from diagrams import Cluster, Diagram
+from diagrams.aws.compute import ECS, EKS, Lambda
+from diagrams.aws.database import Redshift
+from diagrams.aws.integration import SQS
+from diagrams.aws.storage import S3
+
+with Diagram("Event Processing", show=False):
+    source = EKS("k8s source")
+
+    with Cluster("Event Flows"):
+        with Cluster("Event Workers"):
+            workers = [ECS("worker1"),
+                       ECS("worker2"),
+                       ECS("worker3")]
+
+        queue = SQS("event queue")
+
+        with Cluster("Processing"):
+            handlers = [Lambda("proc1"),
+                        Lambda("proc2"),
+                        Lambda("proc3")]
+
+    store = S3("events store")
+    dw = Redshift("analytics")
+
+    source >> workers >> queue >> handlers
+    handlers >> store
+    handlers >> dw
+```
+
+<p align="center">
+  <img src="https://diagrams.mingrammer.com/img/event_processing_diagram.png" alt="Event Processing" width="500"/>
+</p>
+
+### Sistema de Recolección de Mensajes en GCP
+
+Este diagrama detalla un sistema de recolección de mensajes implementado en Google Cloud Platform (GCP), destacando el uso de Pub/Sub, BigQuery, Dataflow y otras herramientas de GCP.
+
+```python
+from diagrams import Cluster, Diagram
+from diagrams.gcp.analytics import BigQuery, Dataflow, PubSub
+from diagrams.gcp.compute import AppEngine, Functions
+from diagrams.gcp.database import BigTable
+from diagrams.gcp.iot import IotCore
+from diagrams.gcp.storage import GCS
+
+with Diagram("Message Collecting", show=False):
+    pubsub = PubSub("pubsub")
+
+    with Cluster("Source of Data"):
+        [IotCore("core1"),
+         IotCore("core2"),
+         IotCore("core3")] >> pubsub
+
+    with Cluster("Targets"):
+        with Cluster("Data Flow"):
+            flow = Dataflow("data flow")
+
+        with Cluster("Data Lake"):
+            flow >> [BigQuery("bq"),
+                     GCS("storage")]
+
+        with Cluster("Event Driven"):
+            with Cluster("Processing"):
+                flow >> AppEngine("engine") >> BigTable("bigtable")
+
+            with Cluster("Serverless"):
+                flow >> Functions("func") >> AppEngine("appengine")
+
+    pubsub >> flow
+```
+
+<p align="center">
+  <img src="https://diagrams.mingrammer.com/img/message_collecting_diagram.png" alt="Message Collecting" width="500"/>
+</p>
+
+
+### Pod Expuesto con 3 Réplicas en Kubernetes
+
+Este ejemplo muestra un pod expuesto con un servicio de red en Kubernetes, ilustrando el uso de pods y réplicas.
+
+```python
+from diagrams import Diagram
+from diagrams.k8s.clusterconfig import HPA
+from diagrams.k8s.compute import Deployment, Pod, ReplicaSet
+from diagrams.k8s.network import Ingress, Service
+
+with Diagram("Exposed Pod with 3 Replicas", show=False):
+    net = Ingress("domain.com") >> Service("svc")
+    net >> [Pod("pod1"),
+            Pod("pod2"),
+            Pod("pod3")] << ReplicaSet("rs") << Deployment("dp") << HPA("hpa")
+```
+
+<p align="center">
+  <img src="https://diagrams.mingrammer.com/img/exposed_pod_with_3_replicas_diagram.png" alt="Exposed Pod with 3 Replicas" width="500"/>
+</p>
+
+
+### Arquitectura con Estado en Kubernetes
+
+Esta arquitectura representa un conjunto de aplicaciones stateful en Kubernetes, mostrando el uso de StatefulSets, almacenamiento persistente y clases de almacenamiento.
+
+```python
+from diagrams import Cluster, Diagram
+from diagrams.k8s.compute import Pod, StatefulSet
+from diagrams.k8s.network import Service
+from diagrams.k8s.storage import PV, PVC, StorageClass
+
+with Diagram("Stateful Architecture", show=False):
+    with Cluster("Apps"):
+        svc = Service("svc")
+        sts = StatefulSet("sts")
+
+        apps = []
+        for _ in range(3):
+            pod = Pod("pod")
+            pvc = PVC("pvc")
+            pod - sts - pvc
+            apps.append(svc >> pod >> pvc)
+
+    apps << PV("pv") << StorageClass("sc")
+```
+
+<p align="center">
+  <img src="https://diagrams.mingrammer.com/img/stateful_architecture_diagram.png" alt="Stateful Architecture" width="500"/>
+</p>
+
+
+
+### Servicio Web Avanzado con Infraestructura On-Premise
+
+Aquí se ilustra un servicio web avanzado que combina infraestructura local (on-premise) con herramientas como Nginx, Redis, PostgreSQL y Kafka para el manejo de servicios, sesiones y base de datos.
+
+```python
+from diagrams import Cluster, Diagram
+from diagrams.onprem.analytics import Spark
+from diagrams.onprem.compute import Server
+from diagrams.onprem.database import PostgreSQL
+from diagrams.onprem.inmemory import Redis
+from diagrams.onprem.aggregator import Fluentd
+from diagrams.onprem.monitoring import Grafana, Prometheus
+from diagrams.onprem.network import Nginx
+from diagrams.onprem.queue import Kafka
+
+with Diagram("Advanced Web Service with On-Premise", show=False):
+    ingress = Nginx("ingress")
+
+    metrics = Prometheus("metric")
+    metrics << Grafana("monitoring")
+
+    with Cluster("Service Cluster"):
+        grpcsvc = [
+            Server("grpc1"),
+            Server("grpc2"),
+            Server("grpc3")]
+
+    with Cluster("Sessions HA"):
+        primary = Redis("session")
+        primary - Redis("replica") << metrics
+        grpcsvc >> primary
+
+
+    with Cluster("Database HA"):
+        primary = PostgreSQL("users")
+        primary - PostgreSQL("replica") << metrics
+        grpcsvc >> primary
+
+    aggregator = Fluentd("logging")
+    aggregator >> Kafka("stream") >> Spark("analytics")
+
+    ingress >> grpcsvc >> aggregator
+```
+
+<p align="center">
+  <img src="https://diagrams.mingrammer.com/img/advanced_web_service_with_on-premise.png" alt="Advanced Web Service with On-Premise" width="500"/>
+</p>
+
+### Servicio Web Avanzado con Infraestructura On-Premise (con colores y etiquetas)
+
+Este diagrama es una versión coloreada del anterior, incluyendo etiquetas y estilos para una mejor comprensión visual.
+
+```python
+from diagrams import Cluster, Diagram, Edge
+from diagrams.onprem.analytics import Spark
+from diagrams.onprem.compute import Server
+from diagrams.onprem.database import PostgreSQL
+from diagrams.onprem.inmemory import Redis
+from diagrams.onprem.aggregator import Fluentd
+from diagrams.onprem.monitoring import Grafana, Prometheus
+from diagrams.onprem.network import Nginx
+from diagrams.onprem.queue import Kafka
+
+with Diagram(name="Advanced Web Service with On-Premise (colored)", show=False):
+    ingress = Nginx("ingress")
+
+    metrics = Prometheus("metric")
+    metrics << Edge(color="firebrick", style="dashed") << Grafana("monitoring")
+
+    with Cluster("Service Cluster"):
+        grpcsvc = [
+            Server("grpc1"),
+            Server("grpc2"),
+            Server("grpc3")]
+
+    with Cluster("Sessions HA"):
+        primary = Redis("session")
+        primary - Edge(color="brown", style="dashed") - Redis("replica") << Edge(label="collect") << metrics
+        grpcsvc >> Edge(color="brown") >> primary
+
+    with Cluster("Database HA"):
+        primary = PostgreSQL
+
+("users")
+        primary - Edge(color="brown", style="dotted") - PostgreSQL("replica") << Edge(label="collect") << metrics
+        grpcsvc >> Edge(color="black") >> primary
+
+    aggregator = Fluentd("logging")
+    aggregator >> Edge(label="parse") >> Kafka("stream") >> Edge(color="black", style="bold") >> Spark("analytics")
+
+    ingress >> Edge(color="darkgreen") << grpcsvc >> Edge(color="darkorange") >> aggregator
+```
+
+<p align="center">
+  <img src="https://diagrams.mingrammer.com/img/advanced_web_service_with_on-premise_colored.png" alt="Advanced Web Service with On-Premise (colored)" width="500"/>
+</p>
+
+
+### Consumidores RabbitMQ con Nodos Personalizados
+
+Este ejemplo demuestra cómo incluir nodos personalizados en los diagramas, usando RabbitMQ y Aurora como base de datos de destino.
+
+```python
+from urllib.request import urlretrieve
+
+from diagrams import Cluster, Diagram
+from diagrams.aws.database import Aurora
+from diagrams.custom import Custom
+from diagrams.k8s.compute import Pod
+
+# Descargar una imagen para usarla en un nodo personalizado
+rabbitmq_url = "https://jpadilla.github.io/rabbitmqapp/assets/img/icon.png"
+rabbitmq_icon = "rabbitmq.png"
+urlretrieve(rabbitmq_url, rabbitmq_icon)
+
+with Diagram("Broker Consumers", show=False):
+    with Cluster("Consumers"):
+        consumers = [
+            Pod("worker"),
+            Pod("worker"),
+            Pod("worker")]
+
+    queue = Custom("Message queue", rabbitmq_icon)
+
+    queue >> consumers >> Aurora("Database")
+```
+
+<p align="center">
+  <img src="https://diagrams.mingrammer.com/img/rabbitmq_consumers_diagram.png" alt="Broker Consumers" width="500"/>
+</p>
+
+
+
+---
+
+Cada diagrama fue generado utilizando la librería `diagrams`, que permite crear representaciones visuales de infraestructuras y arquitecturas tecnológicas de manera programática. Para más información, visita la [documentación oficial de diagrams](https://diagrams.mingrammer.com/).
+
+

catch-all/README.md

@@ -8,9 +8,10 @@ Aquí iré dejando scripts y ejercicios que se me ocurran, con lo que no hay un
 
 |                                                   Nombre | Descripción                                              |   Nivel    |
 | -------------------------------------------------------: | :------------------------------------------------------- | :--------: |
-|        [Words Linux](./01_scripts_words_linux/README.md) | Script con el fichero: `/usr/share/dict/words`           | intermedio |
+|        [Words Linux](./01_scripts_words_linux/README.md) | Script con el fichero: `/usr/share/dict/words`           |   fácil    |
-| [Descifrador wargame](./02_scripts_descifrador_wargame/) | Script descifrador de código al estilo wargame           | intermedio |
+| [Descifrador wargame](./02_scripts_descifrador_wargame/) | Script descifrador de código al estilo wargame           |   fácil    |
 |                              [Clima España](./03_clima/) | Script conectado a API AEMET                             | intermedio |
-|              [acortador de enlaces](./04_acortador_url/) | Script para acortar enlaces y redirigirlos con app Flask | intermedio |
+|              [acortador de enlaces](./04_acortador_url/) | Script para acortar enlaces y redirigirlos con app Flask |   fácil    |
 | [Pruebas de infraestructuras](./05_infra_test/README.md) | Redis, RabbitMQ, Kafka, Prometheus, etc                  | intermedio |
 |            [Bots Telegram](./06_bots_telegram/README.md) | Bots de Telegram con Python                              |  avanzado  |
+|       [Diagram as code](./07_diagrams_as_code/README.md) | Diagramas de infraestructuras con Python                 |   fácil    |