Check Results

Two functions convert Rocky execution results into Dagster events that appear in the Dagster UI.

emit_materializations(result, translator?) -> list[AssetMaterialization]

Converts Rocky materializations into Dagster AssetMaterialization events.

Each materialization includes metadata:

• strategy — the materialization strategy used (e.g., incremental, full_refresh)
• duration_ms — how long the materialization took
• rows_copied — number of rows copied
• watermark — the new high watermark value

An optional translator can be passed to control asset key mapping, ensuring consistency with how assets were discovered via load_rocky_assets().

emit_check_results(result) -> list[AssetCheckResult]

Converts Rocky check results into Dagster AssetCheckResult events.

Handles every Rocky check type, both pipeline-level and model-level:

Pipeline-level checks:

• row_count — validates source and target row counts match
• column_match — validates columns exist in both source and target
• freshness — validates data is within a staleness threshold
• null_rate — validates null rates are below a threshold
• anomaly — row count deviation from historical baselines
• custom — any user-defined SQL check

Model-level assertions (DQX parity): not_null, unique, accepted_values, relationships, expression, row_count_range, in_range, regex_match, aggregate, composite, not_in_future, older_than_n_days. Each carries a severity (error / warning) and an optional filter predicate; row-level assertions also surface quarantined = true/false when [quarantine] is configured on the pipeline.

Severity maps to Dagster’s AssetCheckSeverity (ERROR / WARN). Warnings never fail the asset check, even when the underlying assertion returned passed = false. All check metadata — SQL fingerprint, failing row count, quarantine state, filter used — is propagated to the Dagster event for full observability.

Example

from dagster_rocky import RockyResource, emit_check_results, emit_materializations
import dagster as dg

@dg.asset
def replicate(context, rocky: RockyResource):
    result = rocky.run(filter="tenant=acme")

    for mat in emit_materializations(result):
        context.log_event(mat)

    for check in emit_check_results(result):
        context.log_event(check)

    return result.tables_copied

Both functions return lists, so you can also inspect or filter events before logging them:

mats = emit_materializations(result)
context.log.info(f"Emitting {len(mats)} materializations")
for mat in mats:
    context.log_event(mat)