Company: Always Right Temp Inc

Dev team:

Engineering Manager: Rafał Jaszczyński

Team lead: Rafał Jaszczyński

Senior dev: Rafał Jaszczyński

Senior dev: Rafał Jaszczyński

Senior dev: Rafał Jaszczyński

Senior QA dev: Rafał Jaszczyński

Product Owner: Rafał Jaszczyński

• detect anomalies in temperature measurements
  • 2 algorithms
• api to answer questions
• scalable
• high throughput

• 20k measurements per sec
• kafka messages
• REST api
• algorithms choose option in properties file
• postgres as persistent database for detected anomalies
• redis to store recent measurements, because of easy and straightforward TTL mechanism (60 sec)
• Command/Query separation
• dead letter topic

No details on how sensors works, but I assume 1 sensor generate 1 measurement per second, this has implications for the application. We got roomId, so sensor could switch rooms.

Note for the Client: To avoid accidental anomaly detections sensor should shoot down when switching rooms.

Use CQRS separation:

• microservice for receiving measurements, detecting anomalies and storing them in database
• microservice for reading

There will probably be need scale heavily command microservice

Interacting with database and running the algorithm should take longer then just receiving and storing the message so there is another option, to add additional microservice:

• microservice for receiving measurements and storing them without processing in database
• microservice to process recent measurements and find anomalies with spring scheduling eg. each 1 second
• microservice for reading

This also changes the behaviour: first version with 2 microservices is checking anomaly for each message and the second one is doing it on scheduled time based.

Because of simplicity and lack of knowledge about real world sensors I would choose option 1.

Frameworks/libraries to be used:

• spring kafka/jpa/redis
• spring webflux for REST api
• testing: jupiter/testcontainers/spring-test
• docker&compose to easily create containers of kafka, redis and postgres
• lombok, mapstruct, liquibase

• More test cases both unit and integration ones
• Fix redis query (redis template)
• Fix query with group by issue
• fix anomaly analyzer lack of uuid support in entity
• add support for list of alghoritms eg. anomaly.detector.strategy: average, limit (to check a range of temperatures eg. 0 to 40, so temp 45 C will )

• Add both services to docker-compose
• separate producer app for testing
• and/or separate e2e test project