Configuring Idempotent Writes with Delta Lake MERGE in foreachBatch

Configuring Idempotent Writes with Delta Lake MERGE in foreachBatch

The goal is to use Structured Streaming’s foreachBatch to write each micro‑batch to a Delta Lake table via a MERGE statement, relying on the merge’s upsert behavior to obtain exactly‑once semantics even when Spark retries a batch.

Delta Lake’s MERGE requires a deterministic match condition that uniquely identifies existing rows, typically based on a primary‑key column or a partitioned column. It is unclear which expressions Spark treats as deterministic inside foreachBatch—whether a simple column reference suffices, whether a deterministic function like coalesce or a constant is acceptable, and whether incorporating a non‑deterministic element (e.g., current_timestamp() or rand()) would invalidate the idempotency guarantee.

• What constitutes a deterministic match condition for Delta Lake MERGE inside foreachBatch?
• Can a match condition that includes a non‑deterministic function still guarantee that retries do not produce duplicate rows?
• How does Spark ensure that the same batch is not applied twice if the match condition evaluates differently across retry attempts?