Recipe: Handle Schema and Type Drift

If you do not provide a schema, BlazeRules infers rule-referenced field types from the first evaluated batch. After that, the engine treats the schema as fixed.

Example:

{"account_age_days": 42}

If a later record sends:

{"account_age_days": "forty-two"}

the value is a type mismatch. The configured type_mismatch_mode decides what happens:

Mode	Behavior
`NULL_ON_TYPE_ERROR`	Treat the field as null for that row; normal comparisons return false.
`COERCE`	Try lightweight coercion such as `"42"` to an integer.
`HARD_FAIL_TYPE`	Throw a structured type error for the batch.

Production default:

cfg = blazerules.EngineConfig()
cfg.ingest_error_mode = blazerules.IngestErrorMode.SKIP_AND_COUNT
cfg.type_mismatch_mode = blazerules.TypeMismatchMode.NULL_ON_TYPE_ERROR

CLI equivalent: run the same policy in a shell script

python - <<'PY'
import sys
import blazerules

cfg = blazerules.EngineConfig()
cfg.ingest_error_mode = blazerules.IngestErrorMode.SKIP_AND_COUNT
cfg.type_mismatch_mode = blazerules.TypeMismatchMode.NULL_ON_TYPE_ERROR

engine = blazerules.RuleEngine(cfg)
engine.load_rules("rules.yaml")
result = engine.evaluate_ndjson(sys.stdin.buffer.read())
print(result.messages_processed, result.messages_skipped, result.error_counts)
PY

Watch:

result = engine.evaluate_ndjson(payload)
print(result.error_counts)
print(result.error_samples)

CLI equivalent: print drift diagnostics from stdin

cat events.ndjson | python drift_check.py

drift_check.py is the Python snippet above with payload = sys.stdin.buffer.read().

For important fields, pin the type in fields: so inference cannot guess differently across environments:

fields:
  account_age_days: {type: int32, nullable: false}
  amount: {type: float32, nullable: false}
  event_ts_ms: {type: timestamp_ms, nullable: false}