Lambda runs your code in response to events without you managing servers. You supply a function, AWS supplies the compute, and you pay for the time it runs rather than for an instance sitting idle.\n\nThe model is genuinely different from a server, and most Lambda problems come from carrying server assumptions into it.\n\n\u003cAwsDiagram type="lambda-flow" /\u003e\n\n## The execution model\n\nAn execution environment is a small, isolated sandbox that runs one invocation at a time. Ten simultaneous requests mean ten environments. There is no in-process concurrency to share.\n\nWhat matters operationally is that AWS reuses environments. After an invocation finishes, the environment is kept warm for a while and reused for the next request. That has two consequences:\n\nCode outside the handler runs once per environment, not once per invocation. This is the single most useful optimisation available:\n\n```python\nimport boto3\n\n# Runs once per environment. Reused across invocations\ns3 = boto3.client("s3")\n\ndef handler(event, context):\n # Runs every invocation\n return s3.get_object(Bucket="acme", Key=event["key\
Create a DynamoDB table with the AWS CLI. Choose the key schema you cannot change later, enable deletion protection and PITR, and take on-demand backups.
Learn DynamoDB Query vs Scan, why filter expressions do not cut cost, how GSIs differ from LSIs, and how to paginate results and use sparse indexes.
Run ElastiCache well: pick Redis, Valkey or Memcached, start serverless, set eviction policies, implement cache-aside and handle cluster mode and failover.