The newest rows are often the first missing from a replica because their changes have had the least time to arrive and be applied. In asynchronous replication, a successful commit on the source does not mean every replica can already return that write. A replica query can show an older but internally consistent state while it catches up.
Why a successful write may be missing from a replica
There are distinct stages between a write and its visibility on a replica: the transaction commits on the source, its change records reach the replica, and the replica applies those records so queries can see the transaction. With asynchronous replication, those stages do not happen as one synchronized event. A read routed to the replica in the interval can therefore omit a row that the source has already committed.
Recent changes are especially likely to be absent: they entered the replication stream most recently and have had the least time to travel through it. This is a statement about replica freshness, not a special property of newly created rows. It does not mean every read will be stale, or that a replica’s applied data is necessarily inconsistent with itself.
What replication lag means in PostgreSQL and MySQL
PostgreSQL: WAL replay
In PostgreSQL physical streaming replication, the primary emits write-ahead log (WAL) records and a standby replays them. The standby’s replay_lsn identifies the last WAL location it has replayed. PostgreSQL 17 describes replay_lag on an asynchronous standby as an approximation of how long recent transactions take to become visible to queries, not a per-row freshness guarantee. See the PostgreSQL 17 statistics documentation.
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MySQL: binary logs and relay logs
In MySQL replication, the source records changes in its binary log. A replica requests those events, stores them in its relay log, and applies them independently. Replicas can progress at their own pace, so a replica may serve a view of the changes it has applied even while it trails the source. The MySQL replication guide describes this flow.
The exact mechanics vary by database engine, replication mode, and read routing. These PostgreSQL and MySQL examples explain the general timing issue; they should not be treated as guarantees about every database service.
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How to assess lag without misreading the metric
On PostgreSQL, compare the standby’s replay position and the replication view’s timing fields. The reported write_lag, flush_lag, and replay_lag describe recent progress through WAL. PostgreSQL cautions that these intervals are not predictions of how long the standby will take to catch up. When a standby has caught up and there is no new WAL activity, the lag fields can later become NULL; that does not by itself mean the replica is unhealthy. Consult the PostgreSQL monitoring reference when interpreting them.
A lag value is not the same as an application-level test that a particular write is visible. For a real incident, establish which engine and replication mode are in use, where the read was routed, and whether the relevant transaction has been applied.
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Ways to preserve read-your-writes behavior
Choose a mitigation according to the freshness guarantee the application needs and the latency or coordination cost it can accept.
| Approach | Freshness behavior | Trade-off or condition |
|---|---|---|
| Read from the source | The read uses the node that accepted the write. | Routes selected reads to the writer instead of a replica; consider this for immediate read-your-writes behavior. |
| Wait for a known commit position | The read waits until the target replica has replayed a position that includes the write. | Requires retaining the write’s position and adds a wait; availability and syntax depend on the database version. |
| Read from a replica without waiting | The read may return an older state while changes are in flight or being applied. | Avoid when the user must immediately see their own write. |
PostgreSQL position-based waiting
PostgreSQL 19 documentation describes WAIT ... standby_replay for waiting until a target LSN has been replayed. The target must be at or after the transaction’s COMMIT record, so the client or pooler needs to retain the write’s LSN. This is a version-specific example: verify that the feature and syntax are supported by the PostgreSQL version you operate before relying on it. See the PostgreSQL 19 WAIT reference.
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Why failover can also produce stale reads
Promotion does not always mean a node has already applied every change in its backlog. MySQL Group Replication documents that a promoted primary can accept reads while applying backlog, temporarily returning stale data. Its consistency options let deployments coordinate reads or writes differently, with corresponding coordination trade-offs. The relevant behavior is specific to Group Replication and its configuration; see MySQL Group Replication transaction consistency guarantees.
More generally, evaluate a mitigation against the guarantee it provides, its read latency, its scope (a session, selected writes, or a broader group of reads), and what clients can see during failover. Do not assume a setting for one engine or replication mode transfers unchanged to another.
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What the documentation does—and does not—establish
The MySQL 8.4 replication FAQ says a replica is not guaranteed to be in synchrony with its source at any given time without special measures. The official PostgreSQL and MySQL references explain lag signals and consistency mechanisms, but they do not provide a general statistic for how often replication lag causes a user-visible stale read. A frequency estimate would depend on the workload, replication setup, and routing behavior, so it should not be inferred from these manuals. See the MySQL 8.4 Replication FAQ.
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