Upsert

An upsert is primarily needed to prevent duplicate data, simplify code, and improve performance by eliminating the need to manually check if a record exists before writing it.

It plays a massive role in idempotency, offers critical benefits in multithreaded environments, and shares a conceptual relationship with—but is not identical to—Compare-And-Swap (CAS).

Idempotency

Yes, absolutely. Upsert is a core tool for achieving idempotency (the property where executing an operation multiple times yields the same result as executing it once).

• The Problem: If a network glitch occurs after a client sends an INSERT command, the client might retry the request. A standard INSERT will either create a duplicate row or fail with a primary key violation.
• The Upsert Solution: If the retry occurs, the upsert detects that the record already exists and safely overwrites it with the exact same data. The state of the database remains perfectly consistent no matter how many times the operation is retried.

Multithreading

t completely resolves race conditions caused by concurrent operations.

• Without Upsert (Check-then-Act): A program checks if a user exists (SELECT). If not, it runs an INSERT. In a multithreaded app, Thread A and Thread B might both run the SELECT at the same exact millisecond, see that the user doesn’t exist, and both try to INSERT. One will crash with an error or cause a duplicate.
• With Upsert (Atomic): The database handles the check and write as a single, atomic operation at the engine level. Threads don’t step on each other’s toes because the database manages the lock or conflict resolution internally.

Is it the same as CAS (Compare-And-Swap)?

No, they are different mechanisms, though they share the same goal of managing concurrent data safely.

Feature	Upsert	CAS (Compare-And-Swap)
Core Concept	”Insert this record, but if it already exists, update it instead."	"Update this record, but only if its current value matches my expected value.”
Missing Data	Automatically creates a new record if it doesn’t exist.	Fails if the underlying data isn’t exactly what it expects.
Primary Use	Data synchronization, API retries, and merging data streams.	Optimistic concurrency control (preventing threads from overwriting each other’s changes).