Advantages of Using the Diagrams Python Tool

Define as code, not as a drawing

As engineers, we are prone to not be very savvy about design tools and having to learn one just to create a simple infrastructure diagram for documentation can be taxing and time consuming.

Code can be commited

Most likely you’re already using some sort of version control system (git, svn, mercurial, etc) and you would like to keep using it for eveything else, commiting diagrams in this way is as simple as adding new python files to your project.

Can consume data to create diagrams

You can easily plug this into any other python scripts you may have to easily and automatically create architecture diagrams that change with the actual architecture.

How to Use the Diagrams Python Tool

Run the script

python diagrams.py

Basic example

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")

A more advanced example

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

Working with GCP

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

Kubernetes example

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")

Thank you for your attention!

Any questions?