Dry Run

A dry run means letting a scheduled task walk through its full execution logic — reading data, making judgments, deciding what actions to take externally — but replacing every step that would produce a real consequence (sending an email, writing to a database, calling an external API) with 'log it, don't actually do it,' ending with a list showing what would have happened had this been a real run. The only difference from an actual run is whether it genuinely touches the outside world. This is also what puts it at a different level than retry policy and trigger condition — those two handle what a task that's already live in production should do when it fails or when deciding whether to fire, while a dry run handles the period before a task goes live at all, confirming the whole logic behaves as expected without taking on any real risk.

This is needed because once a scheduled task goes live, the cost of a mistake is often far higher than a manual operation — send one bad email by hand and you can still stop yourself before hitting send, but a scheduled task with flawed logic might repeat the same mistake for several days straight, to hundreds of recipients, before anyone notices, by which point the damage is done and hard to walk back. A dry run exists to move the moment 'is this logic actually correct' gets verified from 'discovered after going live' to 'seeing the full result before it ever goes live,' letting you preview exactly what would happen if this logic ran, without taking on any real consequence, and catching obvious mistakes before they ever reach production.

In practice a complete dry run needs to confirm three things. First, confirm every branch of the logic actually gets exercised, not just the smoothest path — if the task has a branch for 'special handling when the amount exceeds a threshold,' the dry run needs data that genuinely triggers that branch, not just a single normal-case run called good enough. Second, confirm every step that would produce a side effect is correctly intercepted and logged instead of executed — verify the send-email step genuinely didn't send anything, verify the write step genuinely made no database change. This step needs its own verification too, because if the interception mechanism itself has a hole, a dry run gives you a false sense of 'this has been tested' while some steps quietly ran for real. Third, confirm the output list is specific enough that you can judge whether the logic is correct just by reading it — the list should say 'will send to 47 recipients, subject line such-and-such,' not just 'will send an email,' since the latter doesn't carry enough information to verify it's actually the result you wanted.

For you, the real value of a dry run is moving the cost of debugging 'is this logic correct' from after it's already touched real data or real recipients to before it's caused any consequence at all. Without a dry run, a logic error in a scheduled task either gets caught after the fact when someone notices something odd and traces it back — with the damage already done — or relies on you personally staring at the code and mentally walking through every possible scenario before it goes live, which easily misses edge cases, especially combinations you never thought of. With a dry run, you can actually see what would be sent, what would be written, if this logic ran, checking it with your eyes rather than reasoning it out in your head. Worth noting: a dry run verifies whether the logic itself is correct, not whether that logic will hold up against situations it's never encountered — after going live, data shape and volume can differ from what was tested, and a dry run can't substitute for mechanisms like retry policy or fallback instruction that only matter once something's actually in production. The three address different stages of risk, and doing just one isn't the whole picture.