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When a save fails because a client-management record changed after it was read, don’t replay the old update blindly. Fetch the latest record and version token, check whether the pending edit still makes sense, then retry, merge under explicit rules, or ask the user to resolve the difference.
What a compare-and-swap conflict means
Imagine two staff members open the same client record. One changes the phone number and saves. The other, still looking at the earlier copy, changes the account owner and tries to save. Without concurrency protection, the second save might replace the first person’s update with stale data. Compare-and-swap (CAS) prevents that by allowing a write only if the record is still at the version the editor read.
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A CAS token represents a version or state observed by the client; it is not a universal token format. Couchbase, for example, changes a document’s CAS value when the document is modified and lets a mutation be conditioned on the previously observed value (Couchbase: Concurrent Document Mutations). HTTP ETags, database version columns, and Couchbase CAS serve related purposes, but their formats and APIs are not interchangeable.
How to prevent a stale save
Use a read-token-write cycle: obtain the record and its version, retain them together, and submit the update with a condition that the version has not changed. With HTTP, a server may return an ETag on a read, and the client can send it in an If-Match header with an update or delete. RFC 9110 describes If-Match as a common way to prevent accidental overwrites when user agents act in parallel; the condition is checked before the method is performed, using strong entity-tag comparison (RFC 9110, HTTP Semantics).
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- Read: Request the client record and capture its ETag, CAS value, version column, or the equivalent token defined by the system.
- Edit: Keep the token associated with the exact record version shown to the user while preparing the intended changes.
- Write conditionally: Send the update with the token using the API’s required mechanism, such as
If-Matchor a database-specific CAS check. - Handle rejection: If the condition fails, treat the update as a concurrency conflict and start recovery rather than silently dropping the condition.
In SAP’s NetWeaver 750 documentation, a stale ETag can produce 412 Precondition Failed, while a missing required If-Match can produce 428 Precondition Required (SAP Help Portal: Conditional Handling). Those are documented behaviors for that API context, not a guarantee that every client-management product uses the same status codes, headers, or contract. Follow the actual API documentation for the system you integrate with.
What to do when the update conflicts
- Fetch the current record and its current token. Don’t use the stale response or token for a new attempt. Azure Cosmos DB and PlayFab document rereading current state or obtaining an updated version before retrying (Azure Cosmos DB: Optimistic Concurrency Control; PlayFab: ETags and Concurrency Control).
- Reassess the intended change against the current values. Determine what changed since the original read and whether the pending edit still reflects the user’s intent. Don’t assume a patch based on stale state remains valid simply because it can be resubmitted.
- Retry only if the operation is still valid. Build the retry from the current state, use its fresh token, and preserve the original intended change only where it remains correct. Make retries bounded by application policy; no single retry count fits every API or workflow.
- Otherwise, merge deliberately or ask for a decision. Apply defined field-level rules only when they preserve the meaning of the record. If the edits cannot be reconciled safely, show the current and pending values and let the user choose or revise the update. EF Core documents requerying, merging, or asking the user to resolve concurrency conflicts (Microsoft Learn: Handling Concurrency Conflicts).
Choose a recovery approach by the edits involved
The right response depends on whether the operation can safely be repeated, whether edits overlap, and whether the application can preserve the business meaning without asking the user.
| Situation | Safer response | Decision to make |
|---|---|---|
| The edit remains valid against the latest record and can be safely repeated. | Rebuild the update from current state and retry with the fresh token. | Confirm the action is still appropriate; do not replay the stale whole-record payload blindly. |
| Changes affect separate fields with independent meanings. | Merge by applying explicit field-level rules, then condition the write on the fresh version. | Check whether the fields are truly independent in the client-management workflow. |
| Changes overlap or affect related fields. | Show the current and pending values and ask the user to resolve them. | Let the user decide which value or combination reflects the intended business outcome. |
| A field has an automated merge rule. | Apply that rule only if it is appropriate for that field’s semantics. | Verify the rule handles duplicates, ordering, and related fields as intended. |
A merge policy can be technically consistent yet wrong for the business data. AWS AppSync documents Automerge behavior in which the existing server value wins for scalar conflicts, while list values are concatenated and duplicates retained (AWS AppSync: Conflict Detection and Resolution). That policy illustrates why a generic “merge” is not automatically safe: concatenating a list of client contacts, for example, may create duplicates rather than resolve which entry should be primary.
Quick Recap
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Implementation checks that prevent avoidable data loss
- Keep each token paired with its record snapshot. A version token from one read must not be attached to a different record version or another record.
- Separate conflict handling from other failures. A concurrency rejection calls for refresh and reevaluation; validation, authorization, network, and server failures need their own handling.
- Make retries bounded and observable. Avoid unbounded retry loops, and provide a path to user resolution if the record keeps changing.
- Test rules for related fields as well as isolated fields. A merge that works for two independent values may break a relationship such as account owner plus ownership date.
- Preserve user visibility. If automatic recovery cannot safely preserve intent, report that the record changed and present enough current context for a deliberate choice.
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