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diagrams/docs/getting-started/examples.md

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---
id: examples
title: Examples
---
Here are some more examples.
## Grouped Workers on AWS
```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")
```
![grouped workers diagram](/img/grouped_workers_diagram.png)
## Clustered Web Services
```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
```
![clustered web services diagram](/img/clustered_web_services_diagram.png)
## Event Processing on AWS
```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
```
![event processing diagram](/img/event_processing_diagram.png)
## Message Collecting System on 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
```
![message collecting diagram](/img/message_collecting_diagram.png)
## Exposed Pod with 3 Replicas on Kubernetes
```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")
```
![exposed pod with 3 replicas diagram](/img/exposed_pod_with_3_replicas_diagram.png)
## Stateful Architecture on Kubernetes
```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")
```
![stateful architecture diagram](/img/stateful_architecture_diagram.png)
## Advanced Web Service with On-Premise
```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
```
![advanced web service with on-premise diagram](/img/advanced_web_service_with_on-premise.png)
## Advanced Web Service with On-Premise (with colors and labels)
```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
```
![advanced web service with on-premise diagram colored](/img/advanced_web_service_with_on-premise_colored.png)
## RabbitMQ Consumers with Custom Nodes
```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")
```
![rabbitmq consumers diagram](/img/rabbitmq_consumers_diagram.png)