database postgres

#docker run --name microservices -p 5435:5432 -e POSTGRES_PASSWORD=s3cr3t -d postgres:12-alpine

kafka

topic creation

docker-compose exec kafka kafka-topics.sh --create --topic prod.orders.placed --partitions 1 --replication-factor 1 --bootstrap-server kafka:9092 docker-compose exec kafka kafka-topics.sh --create --topic prod.orders.shipped --partitions 1 --replication-factor 1 --bootstrap-server kafka:9092

consumer

docker-compose exec kafka kafka-console-consumer.sh --topic prod.orders.shipped --from-beginning --bootstrap-server kafka:9092

producer

docker-compose exec kafka kafka-console-producer.sh --topic prod.orders.shipped --broker-list kafka:9092

argocd

helm repo add argo https://argoproj.github.io/argo-helm helm install argocd argo/argo-cd -n argocd --create-namespace

to get pwd

kubectl -n argocd get secret argocd-initial-admin-secret -o jsonpath="{.data.password}" | base64 -d

kubectl port-forward service/argocd-server -n argocd 8090:443

kubectl apply -f argocd/manifests/repositories.yaml kubectl apply -f argocd/manifests/projects.yaml kubectl apply -f argocd/manifests/applications.yaml

install vault

helm repo add hashicorp https://helm.releases.hashicorp.com helm install vault hashicorp/vault -n vault --create-namespace

install external-secrets

helm repo add external-secrets https://charts.external-secrets.io helm install external-secrets external-secrets/external-secrets --version "${HELM_CHART_VERSION}" --namespace=external-secrets --create-namespace --set installCRDs=true

ClusterSecretStore

kubectl apply -f vault/secret-store.yaml

vault port forward to 8200:8200

configuration of vault, secret store and external secret

namespace vault for all now. kubectl apply -f vault/vault-secret.yaml kubectl apply -f vault/secret-store.yaml kubectl apply -f vault/external-secret.yaml

apisix - api gateway

helm install apisix apisix/apisix --create-namespace --namespace apisix helm install apisix-dashboard apisix/apisix-dashboard --create-namespace --namespace apisix

add route /order and info below is for openid plugin

{ "client_id": "order", "client_secret": "AWMhRNWKJpNxyF7wiJoQlrfcKW3nduVW", "discovery": "http://keycloak.default.svc.cluster.local/realms/apisix/.well-known/openid-configuration", "bearer_only": true, "realm": "apisix", "redirect_uri": "http://localhost:9080/auth/callback", "logout_path": "http://localhost:9080/auth/logout" }

upstream

order.argocd.svc.cluster.local:8080

keycloak - identity management

helm install keycloak oci://registry-1.docker.io/bitnamicharts/keycloak

create a user named order

create a client named order

generate token on keycloak

curl -s -XPOST "http://localhost:8080/realms/apisix/protocol/openid-connect/token"
-d "grant_type=password&username=order&client_id=order&client_secret=AWMhRNWKJpNxyF7wiJoQlrfcKW3nduVW&password=pwd" | jq

test

port forward apisix 9080:9080 curl -v -XGET -H "Authorization: Bearer PUT TOKEN HERE" http://localhost:9080/test | jq

instrumentation & tracing

Micrometer (lib to capture metrics) -> Prometheus (db)

dependencies

spring-boot-starter-actuator -> exposes metrics API to be collected by Prometheus (scraping). micrometer-registry-prometheus -> allows metrics to be exposed in a format that will be understood by Prometheus. spring-boot-starter-aop -> Micrometer times methods need and depends on AspectJ. For using @Observed on class and enabled @ObservedAspect. micrometer-tracing-bridge-otel -> For Tracing Context Propagation with Micrometer Tracing opentelemetry-exporter-zipkin -> For Latency Visualization to send data for traces and spans. loki-logback-appender:1.3.2 -> Sends logs to Loki

config

management.endpoints.web.exposure.include=health,info,prometheus -> in application.properties. (/actuator/prometheus) management.tracing.sampling.probability=1.0 management.metrics.distribution.percentiles-histogram.http.server.requests=true logging.pattern.level="%5p [${spring.application.name:},%X{traceId:-},%X{spanId:-}]"

Prometheus is a time-series database. Polls your application for its latest metrics data – this is known as scraping.

Micrometer is a set of libraries for Java that allow you to capture metrics and expose them to several different tools – including Prometheus. Micrometer acts as a facade – an intermediate layer – between your application and some of the more popular monitoring tools. This makes it easier to publish metrics to Prometheus and other tools like Elastic, Datadog or Dynatrace.

promql

Grafana uses Prometheus as a data source to visualize the data on dashboards.

feign client

https://docs.spring.io/spring-cloud-openfeign/docs/current/reference/html/