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When an application writes to PostgreSQL through a path that does not know Redis is caching the data, application-level cache invalidation can be missed. In an article by Panth Patel, who says he leads Oizom’s software team, the proposed remedy is to let PostgreSQL record that data changed and have application-side listeners invalidate Redis. Patel also describes why a simple notification approach caused duplicate work across multiple backend instances, and how his team moved toward a table-backed listener design. This is his account, not an independently audited production case study.
Why application-level invalidation misses writes
In a cache-aside design, an application checks Redis first and reads from PostgreSQL on a cache miss; it may then store the result in Redis for later requests. Patel’s example gives a Redis entry a 24-hour time-to-live. That is an illustrative TTL, not a measured staleness window or an Oizom production metric.
The risk appears when cached data changes in PostgreSQL but the code that makes the change does not invalidate the matching Redis key. An application’s update or delete function can handle invalidation explicitly, but other write paths—such as another backend, an admin tool, a background job, or a direct database edit—may bypass that function. The database has changed while the cache still holds the earlier value.
How Patel’s design moves invalidation closer to the write
Patel describes an evolution from application-managed cache wrappers toward database-driven change signals. The key distinction is that PostgreSQL does not directly delete Redis entries: the database signals a change, and application-side code responds by invalidating the relevant cache keys.
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Explicit wrappers and centralized callbacks
One approach is to wrap cache reads and writes in application helpers and call invalidation from the relevant update or delete paths. Patel also describes centralizing callbacks for related cached views, so one change can trigger invalidation for more than one representation of the data. These approaches can make application-owned paths easier to manage, but they still depend on every relevant writer invoking the right code.
Trigger-driven notifications
A PostgreSQL trigger can run when a table receives an INSERT, UPDATE, or DELETE, at either row or statement level. Patel describes using triggers to notify application listeners about database changes. An AFTER trigger can inspect the final row value, which can help identify what changed.
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Trigger work is part of the transaction that caused it. PostgreSQL documents that if the statement or trigger errors, the effects of both are rolled back. Consequently, recording or sending a change signal in a trigger can make the database write depend on that trigger work succeeding. This is a reliability benefit when the change record must not be lost independently of the write, but it also couples the write to the trigger’s behavior and failure modes. PostgreSQL documentation on trigger behavior.
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Patel reports that PostgreSQL NOTIFY woke every backend instance listening on the channel. If several application instances each react by invalidating the same key, they all do the work. Repeated deletion of a cache key is often harmless, but duplicate callbacks that send messages, update state, or perform other side effects are not automatically safe.
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Persistent change records and listener observations
Patel’s later design stores changes in a database table, records which listeners have observed each change, and uses a notification as a wake-up signal. The table provides a persistent place for change information rather than relying only on an in-memory reaction to a notification. Listener observation records help track consumption, but Patel’s description does not establish an exactly-once guarantee; consumers still need to account for retries and duplicate handling.
Trigger details that affect correctness
Triggers are not a single uniform event hook. PostgreSQL supports row-level and statement-level triggers, and the operation mix of an application can affect which trigger events occur. A design should be checked against the actual SQL patterns and tables in use, not only a basic update path.
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- Moving a row between partitions can produce row-level DELETE and INSERT trigger events.
INSERT ... ON CONFLICTandMERGEcan invoke combinations of trigger events.- Cascaded SQL operations can fire triggers recursively.
These behaviors matter when a trigger writes change records or derives cache keys from affected rows. An implementation that handles ordinary updates may need additional logic for conflict handling, partition movement, merges, or cascades. PostgreSQL’s trigger documentation describes these cases.
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How the options differ
Direct notifications, a durable change table, and change-data capture (CDC) solve related but distinct delivery problems. The comparison below describes their general trade-offs based on the documented behavior; it is not a benchmark of Oizom’s system.
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| Approach | Change capture and transaction | Durability and recovery | Duplicates and operations | Invalidation delay |
|---|---|---|---|---|
| Application-managed invalidation | Runs in application code; it is not inherently part of the database transaction. | Recovery depends on the application’s write and retry design. | Requires all relevant write paths to call the invalidation logic; missed paths can leave stale cache entries. | Usually close to the application write if the call succeeds; no general delivery guarantee is established here. |
| Trigger plus notification | Trigger work occurs in the write transaction. | A notification can wake listeners, but Patel’s account identifies multi-listener fan-out rather than a durable replay mechanism. | Every listening backend receives the notification; duplicate reactions are possible. | Designed to prompt listeners promptly, but no measured Oizom latency is reported. |
| Trigger plus change table and listener records | The trigger can record the change within the write transaction. | Persisted records give listeners a place to observe pending changes; the account does not prove exactly-once processing or specify all recovery behavior. | Requires operating the table and listener-tracking logic; duplicate-safe handling remains important. | Depends on listener polling or wake-up and processing; no measured Oizom latency is reported. |
| CDC pipeline | A collector captures source changes and a processor applies them downstream. | Redis Data Integration documents streams and checkpoints that permit reprocessing, with at-least-once delivery. | Processing must tolerate repeated events; the pipeline also documents backpressure if events arrive faster than they are processed. | Depends on pipeline throughput and backlog; no Oizom measurement is available. |
Redis describes its Redis Data Integration (RDI) CDC pipeline as capturing source changes, placing events in Redis streams, transforming and writing them with a processor, and using checkpoints to support reprocessing. PostgreSQL is listed as a source, with supported versions varying by deployment. RDI is a comparison point, not evidence that Oizom uses it. Redis Data Integration documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing a design for a cache
The right choice depends on what failure the system must withstand. A direct trigger notification can be attractive when listeners need a prompt signal and duplicate invalidation is safe. A table-backed approach is more appropriate when change records should remain available for listeners to observe, while still requiring explicit decisions about retries, cleanup, and consumer progress. CDC can fit a broader replication pipeline where checkpoints and replay are useful, but it adds components to operate and requires tolerance for at-least-once delivery.
- Transaction coupling: Decide whether a database write should fail if change capture fails. Trigger-based recording couples the two within the transaction.
- Recovery: Establish whether consumers can resume after downtime and whether missed work is persisted or replayable.
- Idempotency: Make repeated invalidations safe, and separately assess any callbacks with side effects.
- Operational cost: Account for listener instances, change-table growth and cleanup, or CDC collectors and processors.
- Latency: Set a target for how quickly a write must become visible through reads, then validate that the selected design meets it under its real workload.
Patel’s article names small libraries in the JSR ecosystem for pub/sub, function wrappers, caching, and a Redis adapter. Those are implementation choices in his account, not independently evaluated recommendations; their current maintenance status and availability are not established here.
What the Oizom account establishes—and what it does not
Patel’s article describes a real engineering problem: cached values can outlive the database state when writes bypass application code that knows about the cache. It also explains the trade-off in using PostgreSQL signals: transaction-aware change capture can address missed write paths, while notification fan-out can duplicate listener work and trigger-based recording couples database writes to the trigger’s success.
The account does not establish exact PostgreSQL or Redis versions, current production usage, invalidation latency, throughput, a reliability rate, or a measured performance improvement. It should be read as an engineering description of the approach and its motivation, not as proof that triggers eliminate stale reads or that a table-backed listener is universally best.
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