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PostgreSQL Performance & Query Optimization · Lesson

Monitoring Replication Lag and Slot Bloat

Track WAL retention on replication slots to prevent runaway disk usage on the primary.

Monitoring Replication Lag and Slot Bloat is a free PostgreSQL Performance & Query Optimization lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the PostgreSQL Performance & Query Optimization learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Why Slots Can Eat Your Disk

Logical replication slots make subscribers durable: the primary will retain WAL until every active slot has confirmed it consumed the changes. That guarantee is the same mechanism that can fill your data disk.

  • A slot that is inactive (consumer down, network split, slow apply) pins WAL forever.
  • WAL accumulates in pg_wal/, the partition fills, and the primary can stop accepting writes.

This lesson is about observing that retention early, before it becomes an outage. The core question is always: how far behind is each slot, in bytes?

The Authoritative View: pg_replication_slots

Every slot is visible in pg_replication_slots. The columns that matter for bloat are active, restart_lsn, and on PostgreSQL 13+ the retention bookkeeping columns wal_status and safe_wal_size.

  • restart_lsn — the oldest LSN the slot still needs; WAL before it can be recycled.
  • active — whether a consumer is currently connected.
  • wal_statusreserved, extended, unreserved, or lost.
SELECT slot_name,
       slot_type,
       active,
       restart_lsn,
       wal_status,
       safe_wal_size
FROM pg_replication_slots
ORDER BY active, slot_name;

Measuring Retention in Bytes

The single most useful number is the gap between the current WAL write position and each slot's restart_lsn. That difference is the WAL the primary is forced to keep just for that slot.

LSNs are pg_lsn values; subtracting two of them yields bytes. Wrap it with pg_size_pretty() for human-readable output.

SELECT slot_name,
       active,
       pg_size_pretty(
         pg_wal_lsn_diff(pg_current_wal_lsn(), restart_lsn)
       ) AS retained_wal
FROM pg_replication_slots
ORDER BY pg_wal_lsn_diff(pg_current_wal_lsn(), restart_lsn) DESC;

Distinguishing Flush Lag from Slot Bloat

Two different lags are often confused:

  • Replication lag — how far behind the consumer is right now. Visible on the primary in pg_stat_replication as the gap between sent_lsn, flush_lsn, and replay_lsn.
  • Slot bloat — how much WAL is physically retained because of a slot, whether or not a consumer is connected.

A healthy, connected replica can have near-zero bloat but transient lag. A disconnected slot has zero live lag (no row in pg_stat_replication) yet unbounded bloat. Always check both views.

Live Lag from pg_stat_replication

For currently connected standbys and logical subscribers, pg_stat_replication exposes per-connection lag in bytes. Compute the write/flush/replay distances against the sender's current WAL position.

The state column (streaming, catchup) and the time-based write_lag/flush_lag/replay_lag intervals tell you whether the consumer is keeping up.

SELECT application_name,
       client_addr,
       state,
       pg_size_pretty(pg_wal_lsn_diff(pg_current_wal_lsn(), sent_lsn))   AS pending_send,
       pg_size_pretty(pg_wal_lsn_diff(sent_lsn, replay_lsn))            AS apply_backlog,
       write_lag,
       flush_lag,
       replay_lag
FROM pg_stat_replication
ORDER BY apply_backlog DESC;

Reading wal_status: reserved, extended, lost

Since PostgreSQL 13, max_slot_wal_keep_size caps how much WAL a slot may force the primary to retain. The wal_status column reports where each slot sits relative to that cap:

  • reserved — within max_wal_size, safe.
  • extended — exceeding max_wal_size but still under max_slot_wal_keep_size.
  • unreserved — past the cap; WAL is being removed and the slot may soon be invalidated.
  • lost — required WAL was already removed; the slot is permanently broken and its consumer must reseed.

safe_wal_size tells you how many bytes can still be written before the slot risks becoming lost. A small or negative value is an urgent alert.

Capping Retention with max_slot_wal_keep_size

The defensive trade-off in logical replication architectures: do you protect the subscriber's durability or the primary's uptime? Setting max_slot_wal_keep_size chooses primary uptime — a stuck slot gets invalidated instead of filling the disk.

  • Default -1 means unlimited retention (the dangerous default).
  • A concrete value (e.g. 10GB) bounds worst-case bloat per slot.

If a slot is invalidated, its subscriber loses its place and must be recreated and resynced — acceptable for ephemeral pipelines, not for a critical replica.

ALTER SYSTEM SET max_slot_wal_keep_size = '10GB';
SELECT pg_reload_conf();

SHOW max_slot_wal_keep_size;

Confirmed Flush vs Restart LSN

Logical slots carry two LSNs worth distinguishing:

  • restart_lsn — the point from which decoding would have to restart; this is what pins WAL retention.
  • confirmed_flush_lsn — the LSN the subscriber has acknowledged as durably applied.

The gap between confirmed_flush_lsn and restart_lsn exists because decoding must restart from the beginning of the oldest running transaction. A long-running transaction on the primary holds restart_lsn back even when the subscriber is fully caught up — a classic cause of stubborn bloat with no apparent lag.

SELECT slot_name,
       confirmed_flush_lsn,
       restart_lsn,
       pg_size_pretty(
         pg_wal_lsn_diff(confirmed_flush_lsn, restart_lsn)
       ) AS decode_restart_gap
FROM pg_replication_slots
WHERE slot_type = 'logical'
ORDER BY decode_restart_gap DESC;

A Single Health Query for Alerting

For dashboards and alert rules, combine retention, activity, and WAL status into one row per slot. Flag any slot that is inactive while retaining significant WAL, or whose wal_status has left reserved.

Wire this query's severity output into your monitoring system (Prometheus exporter, cron + alert, etc.).

SELECT slot_name,
       active,
       wal_status,
       pg_size_pretty(pg_wal_lsn_diff(pg_current_wal_lsn(), restart_lsn)) AS retained,
       CASE
         WHEN wal_status IN ('unreserved', 'lost') THEN 'critical'
         WHEN NOT active
              AND pg_wal_lsn_diff(pg_current_wal_lsn(), restart_lsn) > 1073741824
           THEN 'warning'
         ELSE 'ok'
       END AS severity
FROM pg_replication_slots
ORDER BY severity, retained DESC;

Finding the Transaction That Pins the Slot

When bloat persists on a logical slot with a caught-up subscriber, the culprit is usually the oldest in-progress transaction holding the catalog xmin. Inspect catalog_xmin on the slot, then hunt the offending backend.

pg_replication_slots.catalog_xmin is the oldest transaction whose catalog changes must remain decodable. Long-lived transactions (forgotten BEGIN, idle-in-transaction sessions) keep it pinned.

SELECT pid,
       state,
       xact_start,
       now() - xact_start AS xact_age,
       left(query, 80) AS query
FROM pg_stat_activity
WHERE backend_xmin IS NOT NULL
   OR state = 'idle in transaction'
ORDER BY xact_start
LIMIT 10;

Dropping Orphaned Slots Safely

When a subscriber is gone for good (decommissioned pipeline, failed-over replica), its slot must be dropped or it will retain WAL indefinitely. A slot can only be dropped while inactive.

  • Verify active = false first; dropping an active slot errors out.
  • Use pg_drop_replication_slot() on the primary that owns the slot.

This is the fastest way to recover a disk that is filling because of a dead consumer — but it permanently breaks that subscriber, so confirm it is truly abandoned.

SELECT pg_drop_replication_slot(slot_name)
FROM pg_replication_slots
WHERE active = false
  AND slot_name = 'sub_analytics_dead';

Quick Check: Diagnosing the Bloat

A logical replication slot is retaining 40 GB of WAL. The subscriber is connected, pg_stat_replication shows replay_lag near zero, and state is streaming. What is the most likely cause of the retained WAL?

Recap: Keeping Slots Honest

You now have a full monitoring playbook for replication slot bloat:

  • Retained WAL in bytes = pg_wal_lsn_diff(pg_current_wal_lsn(), restart_lsn) per slot — the number to alert on.
  • Two distinct signals: live lag in pg_stat_replication vs physical retention in pg_replication_slots. A dead slot has no live lag but unbounded bloat.
  • wal_status (reservedextendedunreservedlost) and safe_wal_size show how close a slot is to invalidation.
  • max_slot_wal_keep_size bounds worst-case bloat, trading subscriber durability for primary uptime.
  • Stubborn bloat with zero lag usually means a long-running transaction pinning restart_lsn/catalog_xmin — find it in pg_stat_activity.
  • Orphaned slots must be dropped with pg_drop_replication_slot() once confirmed inactive.

Frequently asked questions

Is the “Monitoring Replication Lag and Slot Bloat” lesson free?

Yes — the full text of “Monitoring Replication Lag and Slot Bloat” is free to read here on the web, and the PostgreSQL Performance & Query Optimization course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the PostgreSQL Performance & Query Optimization course, upgrade to CoddyKit PRO.

What will I learn in “Monitoring Replication Lag and Slot Bloat”?

Track WAL retention on replication slots to prevent runaway disk usage on the primary. You practise PostgreSQL Performance & Query Optimization with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start PostgreSQL Performance & Query Optimization?

No prior experience is required. PostgreSQL Performance & Query Optimization on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Monitoring Replication Lag and Slot Bloat” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this PostgreSQL Performance & Query Optimization lesson?

Yes. Every PostgreSQL Performance & Query Optimization lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Publications, Subscriptions, and Replica Identity
  2. Offloading Read and Analytic Workloads
  3. Near-Zero-Downtime Major Version Upgrades
  4. Monitoring Replication Lag and Slot Bloat
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