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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteDistributed databases stay available by keeping copies of data on multiple servers and propagating each change between them. A server records a change in a log; other servers receive that record and apply it. If one server fails, another copy may keep serving requests—but whether it has the latest acknowledged change depends on the replication setup.
What is database replication?
Replication is the process of maintaining copies of database data on multiple servers and sending changes between them. A common design has a primary server, which accepts writes, and one or more replicas, which receive and apply the primary’s change records. Names and mechanics differ by product, but the broad idea is similar.
For example, PostgreSQL streams write-ahead log (WAL) records to standby servers; MySQL replicates binary-log events from a source to replicas; and MongoDB secondaries copy and apply operations recorded in a primary’s oplog. These are product-specific implementations, not interchangeable protocols.
Replication can improve availability, support recovery, place data closer to users, and spread read traffic. It does not by itself guarantee that every read is current or that an acknowledged write can never be lost. Those outcomes depend on the acknowledgement rule, read policy, failure, and configuration.
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How does replication keep data moving?
1. The primary records a change
A client sends a write to the primary. The database records the operation in a change log or equivalent stream. That record gives replicas information they can use to reproduce the change.
2. Replicas receive and apply changes
Replicas fetch or receive the records and apply them to their own copies. Until a replica catches up, it may be behind. A replica that serves reads can therefore return an older value, depending on the product and read-consistency settings.
3. The database acknowledges the write
The acknowledgement policy determines when the client is told that a write succeeded. An acknowledgement might mean the primary recorded it, a replica received it, or a configured number of replicas persisted or applied it. Those are distinct milestones; the word “committed” does not have one universal cross-database meaning.
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What is the difference between asynchronous and synchronous replication?
| Approach | What acknowledgement generally waits for | Main trade-off |
|---|---|---|
| Asynchronous | The primary can acknowledge without waiting for replicas to receive or apply the change. | Lower write wait in many configurations, but replicas may lag. If the primary fails before a change reaches the replica later promoted, that change may be absent. |
| Synchronous or acknowledgement-based | The primary waits for the configured replica acknowledgement before confirming the write. The precise milestone and number of replicas depend on the product and configuration. | Can strengthen the guarantee for acknowledged copies, but adds network-dependent latency and can leave writes waiting or unavailable if required replicas cannot respond. |
“Synchronous” is not a promise that every replica has applied every change. For example, MySQL 8.4 semisynchronous replication waits for at least one replica to receive and log transaction events; that acknowledgement is not equivalent to all replicas having applied the transaction. PostgreSQL synchronous standby settings can wait for acknowledgements from a configured number of standbys, including quorum-style configurations. PostgreSQL’s documentation cautions that synchronous replication can increase response times and contention, and commits can remain incomplete if the required synchronous standbys fail.
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There is no universally safer or faster choice. The right configuration depends on how much risk of losing a recent write is acceptable, how much write delay the application can tolerate, and what should happen when a replica or network link is unavailable.
What happens when a database replica fails?
If a secondary fails while the primary remains healthy, writes may continue on the primary, but the system has fewer current copies and less redundancy. Depending on configuration, reads routed to the failed replica may fail or need to be redirected. After recovery, the replica may need to catch up from the change stream.
If the primary fails, an eligible replica may be promoted or elected as the new primary. Clients then need to discover the new role and reconnect or retry requests safely. During the transition, some requests may fail or pause. If replication was asynchronous, a write acknowledged by the old primary may not exist on the promoted replica if it had not reached that replica before failure.
Promotion restores a writable role; it does not reconstruct data that never reached a surviving copy. Applications and operators need a plan for role changes, client reconnection, retries that do not duplicate effects, and recovery of unavailable members. The exact election and failover behavior is database- and configuration-specific.
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Yes, replicas can be used for read-only workloads, analytics, geographically closer access, or recovery planning. Sending reads to replicas can reduce work on a primary, but a replica that is behind can return stale data. Applications that need to read their own recent writes or see a current value must choose a read policy that meets that requirement rather than assuming every replica is current.
Replication and backups address different risks. Replication copies changes, including unwanted changes such as an accidental deletion or corruption. A separate backup and tested recovery plan are still important. MySQL’s documentation describes replica use for backup and analytics, but that does not make replication a substitute for a recoverable backup.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How replication differs across database products
| Database documentation | Documented mechanism or behavior | Important qualification |
|---|---|---|
| PostgreSQL 16 and 18 | WAL streaming to standby servers; synchronous standby acknowledgement can be configured, including a requested number of standbys. | Configuration determines what acknowledgement means. Synchronous replication can increase response times and contention, and required standbys affect whether commits can complete. PostgreSQL 16 high availability documentation; PostgreSQL 18 standby documentation. |
| MySQL 8.4 | Source binary-log replication to replicas; replication is asynchronous by default. The manual also describes GTIDs, read scaling, backup and analytics uses, and semisynchronous mode. | Semisynchronous mode waits for at least one replica to receive and log events, not for all replicas to apply them. The manual distinguishes statement-based, row-based, and mixed binary-log formats. MySQL 8.4 replication reference. |
| MongoDB current manual | A replica set has a primary and secondary members. The primary records changes in an oplog; secondaries replicate and apply operations asynchronously. Elections can occur when the primary is unavailable. | Secondary reads can return data that does not reflect the primary. Election timing depends on configuration; it should not be generalized into a failover-time promise for all deployments. MongoDB replication manual. |
PostgreSQL’s high-availability documentation calls the challenge of synchronizing changes across servers “the fundamental difficulty for servers working together.” In practice, each database exposes its own controls for acknowledgement, reads, and failover; operators need to understand those controls rather than infer guarantees from the label “replication.”
What should you check before relying on replication?
- Write acknowledgement: Identify whether success means the primary recorded a change, a replica received it, or replicas persisted or applied it—and how many acknowledgements are required.
- Read freshness: Decide whether reads may come from replicas and how much lag the application can tolerate.
- Failure behavior: Determine what happens when the primary, a replica, or the network between them becomes unavailable.
- Client handling: Plan how clients find a promoted primary, reconnect, and retry without unintentionally repeating a write.
- Recovery: Keep separate backups and a recovery procedure, since replicated mistakes can propagate too.
- Operational trade-offs: Account for network delay, write latency, contention, the level at which data is replicated, and the complexity of monitoring and restoring members.
For a deeper technical treatment of distributed databases and consistency models, Alex Petrov’s Database Internals: A Deep Dive into How Distributed Data Systems Work is an intermediate-to-advanced reference listed by O’Reilly Media.
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