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
with Diagram("Multi-Cloud", show=False):
terraform = Custom("Terraform", terraform_icon)
client = Client("Client Notebook")
with Cluster("AWS"):
with Cluster("Kubernetes"):
dask1 = Custom("\nDask", dask_icon)
worker = RS("Dask Worker")
worker >> Edge(color="orange") << dask1
s3 = S3("LENS")
terraform >> worker
worker >> s3
with Cluster("GCP"):
with Cluster("Kubernetes"):
dask2 = Custom("\nDask", dask_icon)
worker2 = RS("Dask Worker")
worker2 >> Edge(color="orange") << dask2
gcs = GCS("ERA5")
terraform >> worker2
worker2 >> gcs
client >> dask1
client >> dask2
from diagrams import Cluster, Diagram, Edge
from diagrams.gcp.compute import GKE
from diagrams.gcp.storage import GCS
from diagrams.k8s.compute import Pod
from diagrams.k8s.others import CRD
from diagrams.onprem.analytics import Spark
from diagrams.onprem.monitoring import Grafana, Prometheus
with Diagram(show=False, outformat="png",
filename="diagrams/tbd",
direction="LR",
node_attr ={"fontsize": "14"},
edge_attr ={"labelfontsize": "14"}):
with Cluster("Google Cloud") as cloud:
storage = GCS("Google Cloud Storage")
with Cluster("Kubernetes cluster"):
with Cluster("Monitoring") as monitoring:
metrics = Prometheus("Prometheus")
metrics_push = Prometheus("Prometheus Gateway")
metrics_push << Edge(label="pull metrics") << metrics
service_monitor = CRD("ServiceMonitor")
service_monitor << Edge(label="watch") << metrics
metrics << Edge(label="query metrics") << Grafana("Grafana")
with Cluster("Apache Spark application") as spark:
Spark()
driver = Pod("driver")
driver << service_monitor
exec_1 = Pod("executor 1")
exec_2 = Pod("executor 2")
exec_3 = Pod("executor 3")
driver << exec_1
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("Media Monitoring Storage Architecture", show=False) as med_diag:
pubsub = PubSub("pubsub")
flow = Dataflow("DataFlow")
with Cluster("Data Collection"):
[
Functions("RSS Feed Webhook"),
Functions("Twitter Webhook"),
Functions("Press Release")
] >> pubsub >> flow
with Cluster("Storage"):
with Cluster("Data Lake"):
flow >> [BigQuery("BigQuery"), GCS("Storage")]
with Cluster("Event Driven"):
with Cluster("Processing"):
flow >> AppEngine("GAE") >> BigTable("BigTable")
with Cluster("Serverless"):
flow >> Functions("Function") >> AppEngine("AppEngine")
pubsub >> flow
med_diag
from diagrams import Diagram, Cluster
from diagrams.gcp.database import Firestore
from diagrams.gcp.network import CDN, DNS, LoadBalancing
from diagrams.gcp.storage import GCS
from diagrams.gcp.compute import GKE
from diagrams.onprem.compute import Server
from diagrams.oci.network import Internetgateway
with Diagram("Gran - GCP Design", show=False):
server = Server("Client (Nuxt.js)")
with Cluster("Google Cloud Platform"):
firestore = Firestore("Firebase Firestore")
cdn = CDN("Cloud CDN")
dns = DNS("Cloud DNS")
lb = LoadBalancing("Cloud Load Balancing")
gcs = GCS("Cloud Storage")
gke = GKE("Kubernetes Engine")
net = Internetgateway("Gateway")
dns >> cdn >> gcs
dns >> lb >> gke >> firestore
server >> net
net >> dns
with Cluster("GCP account (sysdig)", graph_attr={"bgcolor": "lightblue"}):
sds = Custom("Sysdig Secure", "../../resources/diag-sysdig-icon.png")
bench = General("Cloud Bench")
sds >> Edge(label="schedule on rand rand * * *") >> bench
with Cluster("GCP organization project", graph_attr={"bgcolor": "pink"}):
ccProjectSink = Custom("\nLog Router \n Sink",
"../../resources/sink.png")
orgBenchRole = Iam("Cloud Bench Role")
with Cluster("Secure for Cloud (children project)"):
ccBenchRole = Iam("Cloud Bench Role")
ccPubSub = PubSub("CC PubSub Topic")
ccEventarc = Code("CloudRun\nEventarc Trigger")
ccCloudRun = Run("Cloud Connector")
bucket = GCS("Bucket\nCC Config")
keys = KMS("Sysdig \n Secure Keys")
ccCloudRun << Edge(style="dashed") << keys
bucket << Edge(style="dashed",
label="Get CC \n config file") << ccCloudRun
ccEventarc >> ccCloudRun
ccEventarc << ccPubSub
ccProjectSink >> ccPubSub
gcrPubSub = PubSub("GCR PubSub Topic\n(gcr named)")
gcrSubscription = Code("GCR PubSub\nSubscription")
csCloudBuild = Build("Triggered\n Cloud Builds")
gcr = GCR("Google \n Cloud Registry")
gcrSubscription >> ccCloudRun
disk_2 = PersistentDisk('OS Disk 2')
instance_3 = ComputeEngine('Server 3')
disk_3 = PersistentDisk('OS Disk 3')
pv_disk = PersistentDisk('SSD Disk 1')
node_1 - instance_1
node_2 - instance_2
node_3 - instance_3
instance_1 - disk_1
instance_2 - disk_2
instance_3 - disk_3
instance_3 - pv_disk
pv_workspace - pv_disk
core - SQL('PostgreSQL')
core - GCS('Object Storage')
lb = LoadBalancing('Load Balancer')
dns = DNS('DNS')
lb >> ing
dns >> lb
[Client('SDKs'), Users('Users')] >> dns
with Diagram('Amazon Web Services resources',
show=False,
filename='aws',
outformat='png'):
with Cluster('Amazon Web Services'):
with Cluster('Virtual Private Cloud'):
kube = EKS('Elastic Kubernetes\nService')
instance_1 = EC2('EC2 Instance 1\n(m5.xlarge)')
from diagrams import Diagram, Cluster
from diagrams.gcp.compute import GCF
from diagrams.gcp.storage import GCS
from diagrams.onprem.iac import Terraform
from diagrams.programming.language import Python
Terraform._height = 0.9
Python._height = 0.9
diagram_kwargs = dict(direction="LR", filename="docs/diagram", show=False)
with Diagram("GCF Data Mining Example", **diagram_kwargs):
with Cluster("DevOps & Source Code") as devops:
source_code = GCS("Source Code Bucket")
python = Python()
state = GCS("Terraform State Bucket")
terraform = Terraform()
with Cluster("Application") as app:
function = GCF("Data Mining Service")
data = GCS("Data Bucket")
source_code >> python >> function
state >> terraform >> function
function >> data
from diagrams.gcp.analytics import PubSub
from diagrams.gcp.compute import Functions
from diagrams.gcp.storage import GCS
from diagrams.gcp.analytics import BigQuery
from diagrams.gcp.database import Firestore
from diagrams.gcp.devtools import Scheduler
with Diagram("Strava Leaderboard Architecture Diagram ", show=True):
source = Functions("generate grid")
with Cluster("Data Pipeline"):
gird_queue = PubSub("grid queue")
credential = Firestore("credentials store")
store = GCS("raw JSON")
with Cluster("Extract-Load"):
with Cluster("scheduler"):
scheduler = Scheduler("scheduler")
schedule_queue = PubSub("schedule queue")
extract_load = Functions("worker")
staging = BigQuery("BigQuery staging dataset")
with Cluster("Transform"):
transform = Functions("transform worker")
analysis = BigQuery("BigQuery analysis dataset")
scheduler >> schedule_queue >> extract_load
}
with Diagram("GCP Architecture",
show=False,
outformat="png",
graph_attr=graph_attr):
with Cluster("Local") as local:
local_machine = BareMetal("Local machine")
with Cluster("Google Cloud") as gcp:
tpu = TPU("Cloud TPU")
with Cluster("Google Cloud Storage") as gcs:
bucket_data = GCS("polyaxon data")
bucket_outputs = GCS("polyaxon outputs")
bucket_logs = GCS("polyaxon logs")
with Cluster("Private VPC") as vpc:
cloud_sql = SQL("CloudSQL\n(Postgres)")
polyaxon_repos = Filestore("Google Filestore\n(polyaxon-repos)")
redis = Memorystore("Cloud Memorystore\n(optional)")
with Cluster("GKE") as gke:
polyaxon = Polyaxon("polyaxon")
local_machine - Edge(label="SSH tunnel", minlen="3.0") - polyaxon
polyaxon - [
cloud_sql, polyaxon_repos, redis, tpu, bucket_data, bucket_logs,
bucket_outputs
from diagrams import Cluster, Diagram, Edge
from diagrams.onprem.client import Users
from diagrams.gcp.analytics import BigQuery, Dataflow, PubSub, DataCatalog
from diagrams.gcp.compute import AppEngine, Functions, GPU, ComputeEngine, GKE
from diagrams.gcp.database import BigTable, Spanner, Firestore, SQL, Datastore
from diagrams.gcp.storage import GCS
with Diagram("Референсная архитектура робота сервисного центра"):
users = Users("Пользователи")
analytics = Users("Аналитики заказчика")
modellers = Users("Модельеры исполнителя")
with Cluster("Корпоративные сиcтемы"):
itsm = GCS("ITSM")
exchange = GCS("Exchange")
teams = GCS("Teams")
msad = GCS("AD")
confluence = GCS("Confluence")
with Cluster("Кластер с компонентами робота"):
with Cluster("Основной бизнес-процесс"):
with Cluster("Обработка входящего трафика"):
emailmod = GKE("Обработка писем")
msgmod = GKE("Обработка сообщений")
with Cluster("Общие компоненты"):
ocr = GKE("OCR")
s2t = GKE("Speech to Text")
t2s = GKE("Text to Speech")
cls = GKE("Text Classifiers")
ceph = GCS("Object Storage")
with Cluster("База знаний"):
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