Diagrams
Diagram as Code.
Diagrams lets you draw the cloud system architecture in Python code. It was born for prototyping a new system architecture design without any design tools. You can also describe or visualize the existing system architecture as well. Diagrams currently supports main major providers including:
AWS
,
Azure
,
GCP
,
Kubernetes
,
Alibaba Cloud
,
Oracle Cloud
etc... It also supports
On-Premise
nodes,
SaaS
and major
Programming
frameworks and languages.
Diagram as Code also allows you to track the architecture diagram changes in any version control system.
NOTE: It does not control any actual cloud resources nor does it generate cloud formation or terraform code. It is just for drawing the cloud system architecture diagrams.
Getting Started
It requires Python 3.6 or higher, check your Python version first.
It uses Graphviz to render the diagram, so you need to install Graphviz to use diagrams. After installing graphviz (or already have it), install the diagrams.
Graphviz下載下傳位址: https://graphviz.gitlab.io/download/
macOS users can download the Graphviz via brew install graphviz
$brew install graphviz 複制
然後，配置目錄 /usr/local/Cellar/graphviz/5.0.0/bin 到PATH環境變量。
$/usr/local/Cellar/graphviz/5.0.0/bin
acyclic circo dijkstra dot_builtins gc gv2gml gvgen gvpack gxl2gv nop prune tred
bcomps cluster dot edgepaint gml2gv gv2gxl gvmap gvpr mm2gv osage sccmap twopi
ccomps diffimg dot2gxl fdp graphml2gv gvcolor gvmap.sh gxl2dot neato patchwork sfdp unflatten 複制
# using pip (pip3)
$ pip3 install diagrams 複制
Or, using pipenv/poetry install:
# using pipenv
$ pipenv install diagrams
# using poetry
$ poetry add diagrams 複制
You can start with quick start. Check out guides for more details, and you can find all available nodes list in here.
Quick Start
Example 1
# diagram1.py
from diagrams import Diagram
from diagrams.aws.compute import EC2
from diagrams.aws.database import RDS
from diagrams.aws.network import ELB
with Diagram("diagram1", show=False):
ELB("lb") >> EC2("web") >> RDS("userdb") 複制
This code generates below diagram.
$ python diagram1.py 複制
It will be saved as
diagram1.png
on your working directory.
Example 2
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 複制
Example 3
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 複制
Example 4
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 複制
Contributing
To contribute to diagram, check out contribution guidelines.
Let me know if you are using diagrams! I'll add you in showcase page. (I'm working on it!) :)
Who uses it?
GitPitch is the perfect slide deck solution for Tech Conferences, Training, Developer Advocates, and Educators. Diagrams is now available as a dedicated Cloud Diagram Markdown Widget so you can use Diagrams directly on any slide for conferences, meetups, and training.
Cloudiscovery helps you to analyze resources in your cloud (AWS/GCP/Azure/Alibaba/IBM) account. It allows you to create a diagram of analyzed cloud resource map based on this Diagrams library, so you can draw your existing cloud infrastructure with Cloudiscovery.
Airflow Diagrams is an Airflow plugin that aims to easily visualise your Airflow DAGs on service level from providers like AWS, GCP, Azure, etc. via diagrams.
Other languages
- If you are familiar to Go, you can use go-diagrams as well.
License
MIT