Dapr Api Wrapper

This is a simple Interface built over Microsoft Dapr APIs for easy transition from libraries like requests and aiohttp to dapr's grpc interface without additional changes in code.

Requirements

1. Kubernetes cluster (eg. Azure Kubernetes)
2. Dapr Setup in Kubernetes

Setup

Step:1

Copy the contents of configuration yml from dapr_api_wrapper > setup > setup.yml

Step:2

Paste them in a new dapr_setup.yml file and add references to passwords (recommended) or passwords directly. Change namespace if required.

Step:3

Apply changes to your kubernetes cluster by running: kubectl apply -f path/to/your/dapr_setup.yml

Step:4

In your kubernetes pod deployment.yml configuration add dapr annotations as referenced here.

apiVersion: apps/v1
kind: Deployment
metadata:
labels:
    app: myawesome-svc
name: myawesome-svc-deployment
spec:
replicas: 1
selector:
    matchLabels:
    app: myawesome-svc

# Add the below lines
# Remove '#' to uncomment any annotation

template:
    metadata:
        annotations:
            dapr.io/app-id: myawesome-svc
            dapr.io/app-port: "<myawesome-svc-port>"
            dapr.io/app-protocol: http # http | grpc
            dapr.io/config: appconfig
            # dapr.io/enable-metrics: "true"
            # dapr.io/enable-profiling: "true" # Setting this paramater to true starts the Dapr profiling server on port 7777
            dapr.io/enabled: "true"
            # dapr.io/http-max-request-size: 64 # Increasing max size of request body http and grpc servers parameter in MB to handle uploading of big files. Default is 4 MB
            # dapr.io/log-as-json: "false"
            dapr.io/log-level: debug # info | warn | error
            # dapr.io/metrics-port: "9090"
            # dapr.io/sidecar-cpu-limit: 1
            # dapr.io/sidecar-memory-limit: 500Mi

        labels:
            app: myawesome-svc
            redis-client: "true"
# Add the above lines
    # Further configuration
    spec:
    containers:
    

Understanding the request

Get the request arguments from the docstring of below mentioned class.

from dapr_api_wrapper.requests import DaprRequest

API Usage

from dapr_api_wrapper import requests as dapr_requests
from datetime import datetime

request_body = {
    "id": "12345",
    "api_key": "1234567",
    "updated_at": str(datetime.now()),
    "num": 1234567,
    "is_auth": True,
}

resp = dapr_requests.post(
    app_id="myawesome-svc",
    url_path="http://myawesome-svc-svc/test",
    data=request_body,
    content_type="application/json",
    headers={"key1": "value1"},
    http_query_params={"querykey1": "queryvalue1"},
)
resp_json = resp.json()

resp_text = resp.text

resp_bytes = resp.bytes()

print (f"\n{resp_json = }\n{resp_text = }\n{resp_bytes = }", flush=True)