Tech-Reader.blog

  AWS Lambda: Phantom Retries — When Logs Multiply but Events Don’t Exist # aws # lambda # devops # cloud Phantom retries can make a stable system look unstable Problem Your logs show the same Lambda event twice. The same  RequestId  appears again. CloudWatch metrics show two invocations where you expected one. You check your SQS queue or Kinesis stream — and there’s no duplicate message. No second trigger. Nothing out of the ordinary. Welcome to  Phantom Retries  — the haunting illusion of multiple Lambda executions that never actually happened. In this scenario, Lambda appears to “retry” work that it never re-received, leaving engineers chasing ghosts in their logs. Clarifying the Issue Phantom retries occur when signals, not actions, repeat. Lambda’s event model is  at-least-once delivery , which means retries can happen — but what often looks like a retry is instead the result of lag, visibility timeout overlaps, or duplicate reporting at the monitoring...

  AWS S3: Event Notifications Gone Silent — Why Your S3 Triggers Don’t Fire # aws # s3 # devops # cloud A silent event is worse than an error — because it looks like success Problem Your pipeline is humming along — S3 uploads are landing perfectly — but the next step never runs. Your Lambda function doesn’t fire. Your SQS queue is empty. Your SNS topic stays quiet. You double-check the code and the IAM roles. Everything looks fine. But somewhere between your S3 bucket and your downstream service,  the event never made the trip. Imagine you have an automated image processing pipeline. A user uploads an image to your S3 bucket, but the Lambda function that resizes it never runs. The upload completes successfully, yet the workflow stops cold. This is the classic symptom of a silent event failure. Clarifying the Issue S3 event notifications are reliable — but not foolproof. They depend on  configuration, permissions, and object-level filters  all working in harmony. When...

  The Functional Toolkit: Map, Filter, and Reduce # python # coding # programming # softwaredevelopment Timothy had mastered lambda functions and was using them everywhere—in sorting, filtering, and transforming data. But his code still felt repetitive. He found himself writing the same  for  loop patterns: iterate through a collection, transform each item, collect results. Or iterate through a collection, test each item, keep some. Or iterate through a collection, accumulate a single value. Margaret found him writing yet another loop. "You keep rebuilding the same machinery," she observed, leading him to a section labeled "The Functional Toolkit"—a workshop of standardized tools for common data transformations. "Python provides three powerful functions that handle most iteration patterns:  map ,  filter , and  reduce ." The Repetitive Loop Problem Timothy's code followed predictable patterns: # Note: Examples use placeholder data structures # In pract...