Estimate Unapplied Data Behind a Lagging Replica

How to Use This Tool

Translate write throughput and replay delay into an unapplied-data estimate. A lag value in seconds does not show how much data may be missing from reads or exposed during an emergency promotion.

Why Replication Lag Risk needs more than a raw total

PostgreSQL lag fields describe recent write, flush or replay delay and are not forecasts of catch-up duration. For this page, the useful comparison is estimated unapplied data, not whichever input happens to be largest. The Replication Lag Risk result answers the decision in the heading and should not be reused as a score for a different workflow.

The exact Replication Lag Risk formula

Estimated unapplied MB equals primary write MB per second × replay lag seconds × burst multiplier. The visible fields are Primary write throughput, Observed replica replay lag and Incident burst multiplier. For Replication Lag Risk, read each printed unit before entry and make the values describe one transaction, cohort or reporting window. If those scopes differ, the displayed estimated unapplied data may be arithmetically valid but operationally meaningless.

Interpreting estimated unapplied data

Pair time lag with byte-position metrics and validate recovery procedures before treating a replica as a zero-loss failover target. The ten-percent comparison is deliberately narrow: it tests the influence of primary write throughput and is neither a forecast nor a confidence interval. Preserve the values used, their dates and the resulting decision so a later reviewer can reproduce why Replication Lag Risk supported the choice.

What this Replication Lag Risk model leaves out

Synchronous commit, WAL overhead, compression, transaction boundaries, network buffers and catch-up rate are excluded. That is where Replication Lag Risk stops being trustworthy. If an excluded factor could reverse estimated unapplied data, extend the model explicitly or use the authoritative account system instead of hiding the factor inside an unexplained adjustment.

Evidence and independent verification

The reference reviewed for Replication Lag Risk is PostgreSQL — Cumulative statistics system. PostgreSQL — Cumulative statistics system supports the named definition or rule but does not supply private values for estimated unapplied data. Before acting on the result, reconcile the worked example with the relevant dashboard, invoice, export or measurement.

Private, reproducible calculation

Replication Lag Risk runs its arithmetic in the current browser tab and requests no login or API key. That keeps the Replication Lag Risk inputs away from the site's calculation server, while leaving the user responsible for detecting stale data or a changed platform rule. When an assumption changes, reopen PostgreSQL — Cumulative statistics system and rerun the saved Replication Lag Risk scenario.