Add diagrams test
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15
catch-all/07_diagrams_as_code/01_workers_aws.py
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catch-all/07_diagrams_as_code/01_workers_aws.py
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#! /usr/bin/env python
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from diagrams import Diagram
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from diagrams.aws.compute import EC2
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from diagrams.aws.database import RDS
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from diagrams.aws.network import ELB
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with Diagram("Grouped Workers", show=False, direction="TB"):
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ELB("lb") >> [
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EC2("worker1"),
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EC2("worker2"),
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EC2("worker3"),
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EC2("worker4"),
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EC2("worker5")
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] >> RDS("events")
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28
catch-all/07_diagrams_as_code/02_cluster_web.py
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catch-all/07_diagrams_as_code/02_cluster_web.py
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#! /usr/bin/env python
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from diagrams import Cluster, Diagram
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from diagrams.aws.compute import ECS
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from diagrams.aws.database import ElastiCache, RDS
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from diagrams.aws.network import ELB
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from diagrams.aws.network import Route53
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with Diagram("Clustered Web Services", show=False):
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dns = Route53("dns")
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lb = ELB("lb")
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with Cluster("Services"):
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svc_group = [
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ECS("web1"),
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ECS("web2"),
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ECS("web3")
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]
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with Cluster("DB Cluster"):
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db_primary = RDS("userdb")
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db_primary - [RDS("userdb ro")]
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memcached = ElastiCache("memcached")
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dns >> lb >> svc_group
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svc_group >> db_primary
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svc_group >> memcached
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32
catch-all/07_diagrams_as_code/03_event_aws.py
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catch-all/07_diagrams_as_code/03_event_aws.py
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#! /usr/bin/env python
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from diagrams import Cluster, Diagram
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from diagrams.aws.compute import ECS, EKS, Lambda
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from diagrams.aws.database import Redshift
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from diagrams.aws.integration import SQS
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from diagrams.aws.storage import S3
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with Diagram("Event Processing", show=False):
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source = EKS("k8s source")
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with Cluster("Event Flows"):
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with Cluster("Event Workers"):
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workers = [
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ECS("worker1"),
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ECS("worker2"),
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ECS("worker3")
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]
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queue = SQS("event queue")
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with Cluster("Processing"):
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handlers = [Lambda("proc1"),
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Lambda("proc2"),
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Lambda("proc3")]
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store = S3("events store")
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dw = Redshift("analytics")
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source >> workers >> queue >> handlers
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handlers >> store
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handlers >> dw
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37
catch-all/07_diagrams_as_code/04_message_gcp.py
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catch-all/07_diagrams_as_code/04_message_gcp.py
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#! /usr/bin/env python
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from diagrams import Cluster, Diagram
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from diagrams.gcp.analytics import BigQuery, Dataflow, PubSub
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from diagrams.gcp.compute import AppEngine, Functions
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from diagrams.gcp.database import BigTable
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from diagrams.gcp.iot import IotCore
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from diagrams.gcp.storage import GCS
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with Diagram("Message Collecting", show=False):
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pubsub = PubSub("pubsub")
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with Cluster("Source of Data"):
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[
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IotCore("core1"),
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IotCore("core2"),
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IotCore("core3")
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] >> pubsub
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with Cluster("Targets"):
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with Cluster("Data Flow"):
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flow = Dataflow("data flow")
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with Cluster("Data Lake"):
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flow >> [
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BigQuery("bq"),
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GCS("storage")
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]
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with Cluster("Event Driven"):
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with Cluster("Processing"):
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flow >> AppEngine("engine") >> BigTable("bigtable")
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with Cluster("Serverless"):
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flow >> Functions("func") >> AppEngine("appengine")
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pubsub >> flow
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12
catch-all/07_diagrams_as_code/05_exposed_pod_k8s.py
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catch-all/07_diagrams_as_code/05_exposed_pod_k8s.py
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#! /usr/bin/env python
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from diagrams import Diagram
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from diagrams.k8s.clusterconfig import HPA
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from diagrams.k8s.compute import Deployment, Pod, ReplicaSet
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from diagrams.k8s.network import Ingress, Service
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with Diagram("Exposed Pod with 3 Replicas", show=False):
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net = Ingress("domain.com") >> Service("svc")
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net >> [Pod("pod1"),
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Pod("pod2"),
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Pod("pod3")] << ReplicaSet("rs") << Deployment("dp") << HPA("hpa")
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20
catch-all/07_diagrams_as_code/06_stateful_k8s.py
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catch-all/07_diagrams_as_code/06_stateful_k8s.py
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#! /usr/bin/env python
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from diagrams import Cluster, Diagram
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from diagrams.k8s.compute import Pod, StatefulSet
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from diagrams.k8s.network import Service
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from diagrams.k8s.storage import PV, PVC, StorageClass
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with Diagram("Stateful Architecture", show=False):
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with Cluster("Apps"):
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svc = Service("svc")
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sts = StatefulSet("sts")
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apps = []
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for _ in range(3):
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pod = Pod("pod")
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pvc = PVC("pvc")
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pod - sts - pvc
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apps.append(svc >> pod >> pvc)
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apps << PV("pv") << StorageClass("sc")
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39
catch-all/07_diagrams_as_code/07_web_onpremise.py
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catch-all/07_diagrams_as_code/07_web_onpremise.py
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#! /usr/bin/env python
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from diagrams import Cluster, Diagram
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from diagrams.onprem.analytics import Spark
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from diagrams.onprem.compute import Server
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from diagrams.onprem.database import PostgreSQL
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from diagrams.onprem.inmemory import Redis
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from diagrams.onprem.aggregator import Fluentd
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from diagrams.onprem.monitoring import Grafana, Prometheus
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from diagrams.onprem.network import Nginx
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from diagrams.onprem.queue import Kafka
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with Diagram("Advanced Web Service with On-Premise", show=False):
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ingress = Nginx("ingress")
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metrics = Prometheus("metric")
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metrics << Grafana("monitoring")
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with Cluster("Service Cluster"):
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grpcsvc = [
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Server("grpc1"),
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Server("grpc2"),
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Server("grpc3")
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]
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with Cluster("Sessions HA"):
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primary = Redis("session")
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primary - Redis("replica") << metrics
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grpcsvc >> primary
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with Cluster("Database HA"):
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primary = PostgreSQL("users")
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primary - PostgreSQL("replica") << metrics
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grpcsvc >> primary
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aggregator = Fluentd("logging")
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aggregator >> Kafka("stream") >> Spark("analytics")
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ingress >> grpcsvc >> aggregator
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43
catch-all/07_diagrams_as_code/08_web_onpremise_color.py
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catch-all/07_diagrams_as_code/08_web_onpremise_color.py
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#! /usr/bin/env python
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from diagrams import Cluster, Diagram, Edge
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from diagrams.onprem.analytics import Spark
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from diagrams.onprem.compute import Server
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from diagrams.onprem.database import PostgreSQL
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from diagrams.onprem.inmemory import Redis
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from diagrams.onprem.aggregator import Fluentd
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from diagrams.onprem.monitoring import Grafana, Prometheus
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from diagrams.onprem.network import Nginx
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from diagrams.onprem.queue import Kafka
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with Diagram(name="Advanced Web Service with On-Premise (colored)", show=False):
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ingress = Nginx("ingress")
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metrics = Prometheus("metric")
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metrics << Edge(color="firebrick", style="dashed") << Grafana("monitoring")
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with Cluster("Service Cluster"):
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grpcsvc = [
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Server("grpc1"),
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Server("grpc2"),
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Server("grpc3")
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]
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with Cluster("Sessions HA"):
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primary = Redis("session")
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primary - Edge(color="brown", style="dashed") - \
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Redis("replica") << Edge(label="collect") << metrics
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grpcsvc >> Edge(color="brown") >> primary
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with Cluster("Database HA"):
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primary = PostgreSQL("users")
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primary - Edge(color="brown", style="dotted") - \
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PostgreSQL("replica") << Edge(label="collect") << metrics
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grpcsvc >> Edge(color="black") >> primary
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aggregator = Fluentd("logging")
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aggregator >> Edge(label="parse") >> Kafka("stream") >> Edge(
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color="black", style="bold") >> Spark("analytics")
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ingress >> Edge(color="darkgreen") << grpcsvc >> Edge(
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color="darkorange") >> aggregator
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25
catch-all/07_diagrams_as_code/09_consumers_rabbitmq.py
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catch-all/07_diagrams_as_code/09_consumers_rabbitmq.py
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#! /usr/bin/env python
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from urllib.request import urlretrieve
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from diagrams import Cluster, Diagram
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from diagrams.aws.database import Aurora
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from diagrams.custom import Custom
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from diagrams.k8s.compute import Pod
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# Download an image to be used into a Custom Node class
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rabbitmq_url = "https://jpadilla.github.io/rabbitmqapp/assets/img/icon.png"
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rabbitmq_icon = "rabbitmq.png"
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urlretrieve(rabbitmq_url, rabbitmq_icon)
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with Diagram("Broker Consumers", show=False):
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with Cluster("Consumers"):
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consumers = [
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Pod("worker"),
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Pod("worker"),
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Pod("worker")
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]
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queue = Custom("Message queue", rabbitmq_icon)
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queue >> consumers >> Aurora("Database")
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359
catch-all/07_diagrams_as_code/README.md
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catch-all/07_diagrams_as_code/README.md
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# Ejemplos de Diagramas como Código
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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.
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## Instalación
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Crear un entorno virtual y activarlo:
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```bash
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python3 -m venv venv
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source venv/bin/activate
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```
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Instalar la biblioteca `diagrams`, puedes utilizar `pip`:
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```bash
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pip install diagrams
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```
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Al terminal se puede desactivar el entorno virtual con el comando:
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```bash
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deactivate
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```
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## Ejemplos
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### Trabajadores Agrupados en AWS
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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.
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```python
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from diagrams import Diagram
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from diagrams.aws.compute import EC2
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from diagrams.aws.database import RDS
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from diagrams.aws.network import ELB
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with Diagram("Grouped Workers", show=False, direction="TB"):
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ELB("lb") >> [EC2("worker1"),
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EC2("worker2"),
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EC2("worker3"),
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EC2("worker4"),
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EC2("worker5")] >> RDS("events")
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```
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<p align="center">
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<img src="https://diagrams.mingrammer.com/img/grouped_workers_diagram.png" alt="Grouped Workers" width="500"/>
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</p>
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### Servicios Web en Clúster
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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.
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```python
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from diagrams import Cluster, Diagram
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from diagrams.aws.compute import ECS
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from diagrams.aws.database import ElastiCache, RDS
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from diagrams.aws.network import ELB
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from diagrams.aws.network import Route53
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with Diagram("Clustered Web Services", show=False):
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dns = Route53("dns")
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lb = ELB("lb")
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with Cluster("Services"):
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svc_group = [ECS("web1"),
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ECS("web2"),
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ECS("web3")]
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with Cluster("DB Cluster"):
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db_primary = RDS("userdb")
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db_primary - [RDS("userdb ro")]
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memcached = ElastiCache("memcached")
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dns >> lb >> svc_group
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svc_group >> db_primary
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svc_group >> memcached
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```
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<p align="center">
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<img src="https://diagrams.mingrammer.com/img/clustered_web_services_diagram.png" alt="Clustered Web Services" width="500"/>
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</p>
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### Procesamiento de Eventos en AWS
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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.
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```python
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from diagrams import Cluster, Diagram
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from diagrams.aws.compute import ECS, EKS, Lambda
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from diagrams.aws.database import Redshift
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from diagrams.aws.integration import SQS
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from diagrams.aws.storage import S3
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with Diagram("Event Processing", show=False):
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source = EKS("k8s source")
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with Cluster("Event Flows"):
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with Cluster("Event Workers"):
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workers = [ECS("worker1"),
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ECS("worker2"),
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ECS("worker3")]
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queue = SQS("event queue")
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with Cluster("Processing"):
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handlers = [Lambda("proc1"),
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Lambda("proc2"),
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Lambda("proc3")]
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store = S3("events store")
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dw = Redshift("analytics")
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source >> workers >> queue >> handlers
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handlers >> store
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handlers >> dw
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```
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<p align="center">
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<img src="https://diagrams.mingrammer.com/img/event_processing_diagram.png" alt="Event Processing" width="500"/>
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</p>
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### Sistema de Recolección de Mensajes en GCP
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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.
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```python
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from diagrams import Cluster, Diagram
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from diagrams.gcp.analytics import BigQuery, Dataflow, PubSub
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from diagrams.gcp.compute import AppEngine, Functions
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from diagrams.gcp.database import BigTable
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from diagrams.gcp.iot import IotCore
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from diagrams.gcp.storage import GCS
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with Diagram("Message Collecting", show=False):
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pubsub = PubSub("pubsub")
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with Cluster("Source of Data"):
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[IotCore("core1"),
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IotCore("core2"),
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IotCore("core3")] >> pubsub
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with Cluster("Targets"):
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with Cluster("Data Flow"):
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flow = Dataflow("data flow")
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with Cluster("Data Lake"):
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flow >> [BigQuery("bq"),
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GCS("storage")]
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with Cluster("Event Driven"):
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with Cluster("Processing"):
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flow >> AppEngine("engine") >> BigTable("bigtable")
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with Cluster("Serverless"):
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flow >> Functions("func") >> AppEngine("appengine")
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pubsub >> flow
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```
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<p align="center">
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<img src="https://diagrams.mingrammer.com/img/message_collecting_diagram.png" alt="Message Collecting" width="500"/>
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</p>
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### Pod Expuesto con 3 Réplicas en Kubernetes
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Este ejemplo muestra un pod expuesto con un servicio de red en Kubernetes, ilustrando el uso de pods y réplicas.
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```python
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from diagrams import Diagram
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from diagrams.k8s.clusterconfig import HPA
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from diagrams.k8s.compute import Deployment, Pod, ReplicaSet
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from diagrams.k8s.network import Ingress, Service
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with Diagram("Exposed Pod with 3 Replicas", show=False):
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net = Ingress("domain.com") >> Service("svc")
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net >> [Pod("pod1"),
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Pod("pod2"),
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Pod("pod3")] << ReplicaSet("rs") << Deployment("dp") << HPA("hpa")
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```
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<p align="center">
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<img src="https://diagrams.mingrammer.com/img/exposed_pod_with_3_replicas_diagram.png" alt="Exposed Pod with 3 Replicas" width="500"/>
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</p>
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### Arquitectura con Estado en Kubernetes
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Esta arquitectura representa un conjunto de aplicaciones stateful en Kubernetes, mostrando el uso de StatefulSets, almacenamiento persistente y clases de almacenamiento.
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```python
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from diagrams import Cluster, Diagram
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from diagrams.k8s.compute import Pod, StatefulSet
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from diagrams.k8s.network import Service
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from diagrams.k8s.storage import PV, PVC, StorageClass
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with Diagram("Stateful Architecture", show=False):
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with Cluster("Apps"):
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svc = Service("svc")
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sts = StatefulSet("sts")
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apps = []
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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/).
|
||||
|
||||
|
||||
@ -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 |
|
||||
| [Descifrador wargame](./02_scripts_descifrador_wargame/) | Script descifrador de código al estilo wargame | 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 | 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 |
|
||||
|
||||
Loading…
Reference in New Issue
Block a user