Metrics, logs, traces and dashboards that answer real questions.
6 items at intermediate level · all topics
How do you get visibility into what's happening on an EC2 fleet, and who changed what, without SSHing into boxes to find out?
These are two different questions wearing one sentence. CloudWatch answers "what is the fleet doing right now," metrics, logs, alarms. CloudTrail answers "who did what to it," an audit log of every API call. Confusing the two is the most common way this question goes wrong.
Your deployment system needs to know exactly which Git commit is running in production. How do you design that?
Stamp the commit SHA into the artifact at build time and expose it at runtime. Build once per commit, tag and deploy by digest, and serve a /version endpoint, so the answer to "what's live?" comes from the running process itself, not from a pipeline log someone has to go dig up.
A web application runs on Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer. Users report being logged out at random. Investigation shows that session data is held in memory on each instance, so a request routed to a different instance loses the session. Which solution fixes this while keeping the web tier horizontally scalable?
Session state held on an instance makes that instance special, which is what breaks scaling and termination. Moving sessions to a shared store such as ElastiCache or DynamoDB makes every instance interchangeable, which is the property the rest of the architecture assumes.
A product catalogue application backed by Amazon RDS shows rising read latency. Analysis shows that a small number of identical queries account for most of the load, and the underlying data changes only a few times per day. The team wants to reduce latency to single-digit milliseconds with minimal application rework. What should a solutions architect recommend?
Repeated identical reads over rarely changing data is the textbook caching case. Amazon ElastiCache serves those queries from memory in well under a millisecond, and because the data changes a few times a day, staleness is cheap to manage.
An application stores orders in an Amazon DynamoDB table with a partition key of orderId. A new reporting feature must list all orders for a given customerId, sorted by order date. Running the report currently scans the entire table and is slow and expensive. What should a solutions architect recommend?
A scan means the access pattern has no index behind it. A global secondary index with customerId as its partition key and the order date as its sort key turns that scan into a query. Only a global secondary index can introduce a new partition key on an existing table.
A HorizontalPodAutoscaler targeting 70% average CPU never scales, and kubectl describe hpa shows unknown for the current metric. Which TWO conditions would cause this?
CPU-based autoscaling needs a metrics source serving the Metrics API, normally metrics-server, and it needs CPU requests on the Pods' containers, since utilisation is a percentage of the request.