Earlier this year, I wrapped up a six-month migration of seven Python data-ingestion Lambdas into services. Most of the code moved unchanged into an environment where concurrent deliveries shared a filesystem, memory space, and process. I’ll use five cases to show what changed in practice. Duplicate deliveries collided in /tmp. Six SQLAlchemy engines created per request eventually filled a 4 GiB process. Lambda’s async invocation retries disappeared when the same work moved behind RabbitMQ. A blocking database call caused liveness probes to fail. A DLQ replay kept taking its pod down. In each case, application code depended on behavior supplied by the runtime. Filesystem isolation, process lifetime, delivery semantics, network topology, and resource boundaries formed the runtime contract around our code. The services had a runtime contract of their own. We had to identify which guarantees carried over and which responsibilities now belonged in the application. I’ll finish with a checklist engineers can use to find those dependencies before moving a workload between managed runtimes.
Denys Zhak is a software engineer with nine years of experience in backend engineering and distributed systems, focused on production reliability. At Imubit, he builds cloud and on-prem software for industrial machine-learning deployments. He led the six-month migration of seven Python ingestion Lambdas into services described in this talk. Outside work, he contributes to open source. Much of that work has been on Astral’s Ruff and ty. He writes about production engineering at denyszhak.com.
