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A Demo Twitter Streaming and Sentiment Analysis App to showcase RHT AMQ Streams (Kafka), MongoDB served through Python backend API and Javascript Frontend . This app runs on OpenShift and enjoys persistency using OpenShift Container Storage (rook-ceph)

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ksingh7/twitter_streaming_app_on_openshift_OCS

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Demo App : Running on OpenShift with persistent storage from OpenShift Container Storage

Follow this demo hands-on-exercise to deploy a Twitter Streaming and Sentiment Analysis App running on OpenShift consuming persistent storage from OpenShift Container Storage

Once you follow the demo the output should look like this

Instructions

Pre-Pre-requisite

  1. Register for a developer account on twitter and get your API keys. This will be required for this demo
  2. Register for Aylien developer account and grab your API keys. This will be required for this demo

Pre-requisite

  1. Verify OCS cluster status from dashboard
  2. verify OCS cluster status from CLI
oc project openshift-storage
TOOLS_POD=$(oc get pods -n openshift-storage -l app=rook-ceph-tools -o name)
oc rsh -n openshift-storage $TOOLS_POD
ceph -s
ceph osd tree
  1. change default storage class
oc get sc

oc patch storageclass gp2 -p '{"metadata":{"annotations":{"storageclass.kubernetes.io/is-default-class":"false"}}}'

oc patch storageclass ocs-storagecluster-ceph-rbd -p '{"metadata":{"annotations":{"storageclass.kubernetes.io/is-default-class":"true"}}}'

oc get sc

Deploy Distributed Messaging Service (Kafka)

  1. Create a new project
oc new-project amq-streams
  1. Install AMQ Streams Operator from console
watch oc get all,csv
  1. Git clone application repository
git clone https://github.com/ksingh7/twitter_streaming_app_on_openshift_OCS.git
cd twitter_streaming_app_on_openshift_OCS
  1. Verify there are no PV,PVC exist on OCS
oc get pv,pvc
  1. Create Kafka Cluster (before running verify the domain name)
oc apply -f 01-kafka-cluster.yaml
watch oc get all
  1. Deploy Prometheus and Grafana
oc apply -f 02-prometheus.yaml
oc apply -f 03-grafana.yaml
watch oc get all
  1. Verify PV,PVC provisioned by OCS
oc get pvc -n amq-streams

oc get pv -o json | jq -r '.items | sort_by(.spec.capacity.storage)[]| select(.spec.claimRef.namespace=="amq-streams") | [.spec.claimRef.name,.spec.capacity.storage] | @tsv'
  1. Add prometheus as grafana's data source and Kafka/Zookeeper dashboards
sh 04-grafana-datasource.sh
  1. Grab Grafana URL
oc get route grafana-route --no-headers | awk '{print $2}'

Deploy Database Service (Mongodb)

  1. Create MongoDB template
oc create -f 05-ocs-mongodb-persistent-template.yaml -n openshift
oc -n openshift get template mongodb-persistent-ocs
  1. Create MongoDB app
oc new-app -n amq-streams --name=mongodb --template=mongodb-persistent-ocs \
    -e MONGODB_USER=demo \
    -e MONGODB_PASSWORD=demo \
    -e MONGODB_DATABASE=twitter_stream \
    -e MONGODB_ADMIN_PASSWORD=admin
  1. Exec into MongoDB POD
oc rsh $(oc get  po --selector app=mongodb --no-headers | awk '{print $1}')
  1. Connect to MongoDB and add some recods
mongo -u demo -p demo twitter_stream

db.redhat.insert({name:'Red Hat Enterprise Linux',product_name:'RHEL',type:'linux-x86_64',release_date:'05/08/2019',version:8})
db.redhat.find().pretty()

Deploying Python Backend API Service

  1. Allow container to run as root
oc adm policy add-scc-to-user anyuid -z default

  1. Deploy backend API APP
oc new-app --name=backend --docker-image=karansingh/kafka-demo-backend-service --env IS_KAFKA_SSL='False' --env MONGODB_ENDPOINT='mongodb:27017' --env KAFKA_BOOTSTRAP_ENDPOINT='cluster-kafka-bootstrap:9092' --env 'KAFKA_TOPIC=topic1' --env AYLIEN_APP_ID='YOUR_KEY_HERE' --env AYLIEN_APP_KEY='YOUR_KEY_HERE' --env TWTR_CONSUMER_KEY='YOUR_KEY_HERE' --env TWTR_CONSUMER_SECRET='YOUR_KEY_HERE' --env TWTR_ACCESS_TOKEN='YOUR_KEY_HERE' --env TWTR_ACCESS_TOKEN_SECRET='YOUR_KEY_HERE' --env MONGODB_HOST='mongodb' --env MONGODB_PORT=27017 --env MONGODB_USER='demo' --env MONGODB_PASSWORD='demo' --env MONGODB_DB_NAME='twitter_stream' -o yaml > 06-backend.yaml
oc apply -f 06-backend.yaml ; oc expose svc/backend

  1. In a new shell, tail logs of backend
oc logs -f $(oc get po --selector app=backend --no-headers | awk '{print $1}')

Deploy Frontend Service

  1. Grab the backend route
oc get route backend --no-headers | awk '{print $2}'
  1. Edit results.html, Line-62 var url and update route

  2. Build Frontend Docker image

cd frontend

docker build -t kafka-demo-frontend-service:latest .
docker tag kafka-demo-frontend-service:latest karansingh/kafka-demo-frontend-service:latest
docker push karansingh/kafka-demo-frontend-service

oc new-app --name=frontend --docker-image=karansingh/kafka-demo-frontend-service:latest ; oc expose svc/frontend ; oc get route frontend

Miscellaneous Command Set (notes to myself)

  • Deleting App
oc delete all --selector app=backend
oc delete all --selector app=frontend
oc delete all --selector app=mongodb ; oc delete svc/mongodb ; oc delete secret/mongodb ; oc delete pvc/mongodb
oc adm policy remove-scc-from-user anyuid system:serviceaccount:amq-streams:default
  • Local Docker setup
docker run -p 27017:27017 -d --mount type=bind,source=$PWD/data/bin,destination=/data/bin --name mongo mongo
docker exec -it mongo /bin/bash
mongo
  • Create a User with Password (demo:demo)
  • Create a db and add document in a collection (sampledb)
docker run -d --rm  --name backend --link mongo -p 8080:8080 --env IS_KAFKA_SSL='True' --env MONGODB_ENDPOINT='mongo:27017' --env KAFKA_BOOTSTRAP_ENDPOINT='cluster-kafka-bootstrap-twitter-demo.apps.ocp42.ceph-s3.com:443' --env 'KAFKA_TOPIC=topic1' --env AYLIEN_APP_ID='SECRET' --env AYLIEN_APP_KEY='SECRET' --env TWTR_CONSUMER_KEY='SECRET' --env TWTR_CONSUMER_SECRET='SECRETa' --env TWTR_ACCESS_TOKEN='SECRET' --env TWTR_ACCESS_TOKEN_SECRET='SECRET' --env MONGODB_HOST='mongo' --env MONGODB_PORT=27017 --env MONGODB_USER='demo' --env MONGODB_PASSWORD='demo' --env MONGODB_DB_NAME='sampledb' karansingh/kafka-demo-backend-service
docker run -p 80:80 -d --rm --name frontend  --link backend  karansingh/kafka-demo-frontend-service

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A Demo Twitter Streaming and Sentiment Analysis App to showcase RHT AMQ Streams (Kafka), MongoDB served through Python backend API and Javascript Frontend . This app runs on OpenShift and enjoys persistency using OpenShift Container Storage (rook-ceph)

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