Database replication keeps copies of data on multiple servers, but those copies may not be equally current. With asynchronous replication, a replica can lag behind its source and return older data; conflicts and failover behavior depend on the database, replication mode, and configuration.
What does database replication copy?
Replication maintains data copies on separate database servers, commonly to support availability, redundancy, or read workloads. The mechanism and scope vary. Physical replication copies changes at a lower storage or database level, while logical replication publishes selected data changes for subscribers; the exact objects and guarantees depend on the engine and setup.
| Example | Documented replication behavior | What to keep in mind |
|---|---|---|
| MongoDB replica set | Secondaries replicate the primary’s oplog and apply operations asynchronously. | A secondary may not yet have applied a recent primary write. MongoDB’s Manual describes this behavior in its current “Replication” documentation. |
| PostgreSQL logical replication | A subscriber starts with a snapshot, then receives ongoing changes from publications and applies them in publisher order for transactional consistency within a single subscription. | This is logical publisher/subscriber replication, not a universal description of PostgreSQL physical replication or every extension. See PostgreSQL 18 “Logical Replication.” |
| MySQL GTID replication | MySQL’s consistency statement is conditional: consistency is guaranteed when all transactions committed on the source have been applied to the replica. | The condition matters; a replica that has not applied all source-committed transactions does not meet it. See MySQL Reference Manual section 26.7, “Replication.” |
These examples are product-specific, not interchangeable guarantees. Check the documentation for the deployed engine version and replication mode before relying on a particular behavior.
What is replication lag, and why is a replica behind?
Replication lag is the delay between a change on the source and its application on the replica. MongoDB’s official Database Manual describes lag as the delay between an operation on the primary and its application from the oplog to a secondary. Lag is a measurable condition, not a diagnosis by itself, and it has no single universal cause or maximum duration.
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Common causes to investigate
- Network problems: latency or packet loss can delay the transfer of changes.
- Replica resource contention: CPU, storage, or other workload pressure can slow application of incoming changes.
- Slow operations or a heavy workload: the replica may be unable to apply changes as quickly as the source generates them.
- Insufficient replication history: a replica that has been down too long may need to resynchronize if the source no longer retains the changes it needs.
MongoDB Manual 8.0’s “Troubleshoot Replication Lag” recommends an oplog window long enough to cover the longest expected secondary downtime. It states a minimum of 24 hours and notes that many users prefer 72 hours or a week. Those are MongoDB documentation recommendations, not general requirements for other databases.
Can a lagging replica return stale reads?
Yes. If a source has applied a write but an asynchronous replica has not, a read served by that replica can return data from before the write. MongoDB’s lag troubleshooting documentation says increased lag raises the possibility of inconsistent distributed reads. For MySQL GTID replication, the documented consistency condition applies only after all source-committed transactions have been applied to the replica.
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Decide which application reads must reflect the latest committed write, then verify that the engine’s read routing, write acknowledgment policy, and replication configuration provide the behavior you need. There is no single cross-database read-after-write mechanism or guarantee established by these product examples.
How do you diagnose and address replication lag?
- Measure the delay using the engine’s tools. For MongoDB, run
rs.printSecondaryReplicationInfo()to inspect each secondary’s lag relative to the primary. Use the monitoring and replication-status tools documented for your deployed database and version. - Correlate lag with workload and resource signals. Check network latency and packet loss, resource contention on the replica, and slow operations. MongoDB notes there is no single error code or immediate method that identifies the cause; investigate the surrounding signals rather than treating the lag number as a diagnosis.
- Check whether the replica can catch up. For MongoDB, compare expected secondary downtime and sync time with the oplog window. If the needed history is no longer available, a resynchronization may be necessary; follow the procedure for the deployed version.
- Review primary-side pressure and flow control. MongoDB documents that significant lag can create cache pressure on the primary. Its flow control limits primary write application with the goal of keeping majority-commit lag below a configurable target, and the current Manual says flow control is enabled by default. Verify the setting and behavior against your version and configuration before changing it.
- Reassess the workload or topology if lag recurs. A remedy should address the observed bottleneck. Changing replication or acknowledgment settings without understanding their effect can alter freshness, write latency, or recovery behavior.
What happens when database replication conflicts?
Conflict behavior is database- and mode-specific. PostgreSQL 16’s “Logical Replication Conflicts” documentation describes how conflicts are handled for logical replication; it should not be generalized to every PostgreSQL extension or another vendor’s multi-writer system.
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PostgreSQL logical replication cases
- An incoming replicated change can update subscriber data even if that data was changed locally.
- A constraint violation is a conflict. If a conflict causes an error, replication stops and requires operator action.
- If a replicated
UPDATEorDELETEfinds no matching row, PostgreSQL skips the operation; the missing row alone does not produce a conflict. - Documented recovery options include changing subscriber data or permissions so the change can apply, or skipping the conflicting transaction.
Skipping a transaction is an explicit data-integrity decision: it can leave the subscriber different from the publisher. For a single PostgreSQL subscription, keeping the subscriber read-only for application writes avoids conflicts caused by those local writes. Other local writes or multiple subscribers can introduce conflicts.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should you verify before relying on replication for consistency or failover?
Replication alone does not establish a universal freshness, recovery-point, or failover guarantee. Confirm the specific engine version, replication mode, acknowledgment policy, and topology before promising what a read or failover will preserve. For MySQL GTID replication, for example, the cited consistency guarantee is conditional on all source-committed transactions having been applied to the replica.
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- Replication scope: determine whether the setup is physical or logical and which data it copies.
- Write acknowledgment: understand whether writes are acknowledged synchronously or asynchronously, and how the chosen policy affects write latency and freshness.
- Topology and conflicts: identify whether there is one writer or multiple writers, and how the specific product handles competing changes.
- Lag measurement: know which native metric or command shows delay, and what lag your application can tolerate.
- Failover eligibility and recovery: establish which replicas may be promoted and what data can be missing under the configured acknowledgment and replication behavior.
- Version compatibility and support: verify behavior against documentation for the deployed versions and supported configuration.
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