DATA OPS

Postgres breaking-change sentinel to PagerDuty

Polls a production Postgres table's schema and pages on-call via PagerDuty only for breaking changes — dropped or retyped columns — while logging additive changes quietly.

CategoryData Ops
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • Trigger15-minute schedule polls the table
  • ActionRead column shape from Postgres information_schemaPostgreSQLPostgres
  • LogicClassify deltas as additive vs. breaking
  • ActionPage on-call via PagerDuty for breaking changesPagerDutyPagerDuty
  • ActionUpdate baseline schemaPostgreSQLPostgres

What it does

Separates harmless schema growth from genuinely dangerous changes. New nullable columns are logged and ignored; dropped columns or narrowed types page the on-call engineer through PagerDuty so a pipeline break never goes unnoticed overnight.

When to use it

Use it on the few Postgres tables whose contract truly matters — billing, identity, the core fact table — where a removed or retyped column means immediate downstream failure and is worth waking someone for.

How it works

  1. 1A schedule polls the table on a tight interval (every 15 minutes).
  2. 2The flow reads column names, types, and nullability from `information_schema.columns` in Postgres.
  3. 3A logic step classifies each delta: additive (new column) versus breaking (drop or type change).
  4. 4If only additive changes exist, it records them to a log table and exits without alerting.
  5. 5If any breaking change is found, it triggers a PagerDuty incident with severity based on the column's criticality and attaches the diff.
  6. 6The baseline schema is updated for the next poll.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect PagerDutyIncidents, on-call, escalations.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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