cache-service

Microservice that acts as the cache memory for very frequent REST calls and therefore increase the performance.

The cache-service utilizes as memory cache the Redis database. Others REST calls in the future will be added in order to increase performance. This is a common good microservice common pattern.

note: this microservice depends with the Kafka and audit-service.

Introduction

This is a Event Driven application, the cache service receives "very frequent" REST calls. The first time this microservice forwards the call to the actual microservice and stores the result in memory using Redis database, subsequent calls are read from Redis database to increase performance.

To keep in sync the Redis database with the server repo, the cache-ervice listens events from the microservice that is reads data; these events 'invalidates' Redis memory database so when a next Rest call is received the data is refreshed from the database and then stored again in the cache memory Redis database.

The application listen to events from the microservice it caches without the postfix -audit (i.e., after the audit-service has been logged.

This cache-service utilizes kafka event machine microservice created previously in the docker directory or in kubernetes (and zookeeper for previous Kafka versions).

This microservice is just the initial concept for cache performance. It just caches de paramDB and just some tables (not all). Future version, and depending on the Customer needs more database and tables will be added in the Redis database.

How to use it

Send events

Send a Event via Kafka

Event Request

The Event Request is a JSON with the follow properties:

username: The name of the user that generates the event.
correlationId: This id helps to a listener application to follow a track of events. This id is generated by the Zuul API Gateway and keep the same until all micro services involved in the transaction finished. It is important this trasaction for the Zipkin and Graylog montior servers.
eventType: This event type tell if our event will be stored or not, its a enum that accepts these values:
- DB_STORE: Store only into a data base.
- FILE_STORE: Store only into a .TXT file.
- FULL_STORE: Store into a data base and .TXT file.
- NON_STORE: No store the event.
eventName: This is the name of the event for example: saveUser.
applicationName: Is the name of the micro service or front end application that generates the event.
coreName: Is the group which it belongs the application name. In other words is the topic that the listener will suscribe.
body: The body is the action of the event, if we create a user the body is the result of create a new user.

Example:

{
    "username":"user1",
    "correlationId": "saveUser_1",
    "eventType":"FULL_STORE",
    "eventName":"saveUser",
    "applicationName":"userServices",
    "eventBody":{
        "notificaFacultad": "NOTIFICA_IAM",
        "datos": {
          "interno": false, 
          "activo": true, 
          "administrador": false, 
          "id": 55, 
          "idUsuario": 501, 
          "nombreUsuario": "adACME", 
          "nombre": "Mickey", 
          "apellido": "Mouse", 
          "telefono": "5591495040", 
          "mail": "[email protected]",
          "fechaIngreso":"2022-04-18", 
          "zonaHoraria":"America/Mexico", 
          "usuarioModificacion":"adminACME", 
          "fechaModificacion":2022-04-22T17:27:14.899735009, 
          "grupos": [], 
          "companias":[],
          "areas":[], 
          "name":"adACME", 
          "attributes": {}, 
          "fullName":"Mickey Mouse", 
          "zoneInfo":"America/Mexico", 
          "authorities":[], 
          "givenName"="Mickey", 
          "familyName"="Mouse", 
          "phoneNumber":"5591495040", 
          "preferredUsername":"adACME", 
          "updatedAt":2022-04-22T17:27:14.899735009Z, 
          "claims": {}, 
          "[email protected]", 
          "address": {}
        }
    }
}

Kafka

Produce

This microservice does NOT produce event, it is just a listener

Dependencies

We need add the spring-cloud stream and kafka dependencies to our pom.xml:

	implementation("org.springframework.cloud:spring-cloud-stream")
    implementation("org.springframework.cloud:spring-cloud-stream-binder-kafka")

Redis database

This microservice utilizes redis to store cache in memory.

For more information see chapter 10 Event-driven architecture with Spring Cloud Stream in Spring Microservices in Action book (2nd edition)

Create the image manually

./gradlew bootBuildImage

Publish the image to GitHub manually

./gradlew bootBuildImage \
   --imageName ghcr.io/rlegorreta/cache-service \
   --publishImage \
   -PregistryUrl=ghcr.io \
   -PregistryUsername=rlegorreta \
   -PregistryToken=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

Publish the image to GitHub from the IntelliJ

To publish the image to GitHub from the IDE IntelliJ a file inside the directory .github/workflows/commit-stage.yml was created.

To validate the manifest file for kubernetes run the following command:

kubeval --strict -d k8s

This file compiles de project, test it (for this project is disabled for some bug), test vulnerabilities running skype, commits the code, sends a report of vulnerabilities, creates the image and lastly push the container image.

For detail information see .github/workflows/commit-stage.yml file.

Run the image inside the Docker desktop

docker run \
    --net ailegorretaNet \
    -p 8351:8351 \
    -e SPRING_PROFILES_ACTIVE=local \
    cache-service

Or a better method use the docker-compose tool. Go to the directory ailegorreta-deployment/docker-platform and run the command:

docker-compose up

Run inside Kubernetes

Manually

If we do not use the Tilttool nd want to do it manually, first we need to create the image:

Fist step:

./gradlew bootBuildImage

Second step:

Then we have to load the image inside the minikube executing the command:

image load ailegorreta/cache-service --profile ailegorreta

To verify that the image has been loaded we can execute the command that lists all minikube images:

kubectl get pods --all-namespaces -o jsonpath="{..image}" | tr -s '[[:space:]]' '\n' | sort | uniq -c\n

Third step:

Then execute the deployment defined in the file k8s/deployment.yml with the command:

kubectl apply -f k8s/deployment.yml

And after the deployment can be deleted executing:

kubectl apply -f k8s/deployment.yml

Fourth step:

For service discovery we need to create a service applying with the file: k8s/service.yml executing the command:

kubectl apply -f k8s/service.yml

And after the process we can delete the service executing:

kubectl deltete -f k8s/service.yml

Fifth step:

If we want to use the project outside kubernetes we have to forward the port as follows:

kubectl port-forward service/config-service 8351:80

Appendix:

If we want to see the logs for this pod we can execute the following command:

kubectl logs deployment/cache-service

Using Tilt tool

To avoid all these boilerplate steps is much better and faster to use the Tilt tool as follows: first create see the file located in the root directory of the proyect called TiltFile. This file has the content:

# Tilt file for config-service
# Build
custom_build(
    # Name of the container image
    ref = 'cache-service',
    # Command to build the container image
    command = './gradlew bootBuildImage --imageName $EXPECTED_REF',
    # Files to watch that trigger a new build
    deps = ['build.gradle', 'src']
)

# Deploy
k8s_yaml(['k8s/deployment.yml', 'k8s/service.yml'])

# Manage
k8s_resource('config-service', port_forwards=['8351'])

To execute all five steps manually we just need to execute the command:

tilt up

In order to see the log of the deployment process please visit the following URL:

http://localhost:10350

Or execute outside Tilt the command:

kubectl logs deployment/cache-service

In order to undeploy everything just execute the command:

tilt down

To run inside a docker desktop the microservice need to use http://cach-service:8351 path

Reference Documentation

This microservice uses the recent Spring Gateway :

Links to Springboot 3 Observability

https://tanzu.vmware.com/developer/guides/observability-reactive-spring-boot-3/

Baeldung:

https://www.baeldung.com/spring-boot-3-observability

Contact AI Legorreta

Feel free to reach out to AI Legorreta on web page.

rlegorreta / cache-service Goto Github PK

cache-service's Introduction