feat(outbox-store): Use executeBatch() after processing entries instead of single executeUpdate() for each entry

README.md

@@ -76,7 +76,7 @@ springOutboxPublisher.publish(
 )
 ```
 
-**Using MySQL instead of PostgreSQL?** Replace `okapi-postgres` with `okapi-mysql` in your dependencies — no code changes needed.
+**Using MySQL instead of PostgreSQL?** Replace `okapi-postgres` with `okapi-mysql` in your dependencies — no code changes needed. Add `rewriteBatchedStatements=true` to your JDBC URL — see [Performance](#performance) for why.
 
 > **Note:** Spring and Kafka versions are not forced by okapi — you control them.
 > Okapi uses plain JDBC internally — it works with any `PlatformTransactionManager` (JPA, JDBC, jOOQ, Exposed, etc.).
@@ -306,7 +306,28 @@ Throughput on a single instance (MacBook M3 Max, JDK 21 LTS, May 2026):
 
 Kafka throughput jumped 16-45× over the original sync-sequential baseline thanks to the `deliverBatch` fire-flush-await pattern. HTTP parallel `sendAsync` is next; multi-threaded scheduler scaling is in the roadmap.
 
-Full methodology, raw JMH results, before/after per change: [`benchmarks/`](benchmarks/).
+### MySQL: `rewriteBatchedStatements`
+
+`OutboxStore.updateAfterProcessingBatch()` writes back a whole processed batch via one JDBC
+`executeBatch()` call. On Postgres, PgJDBC pipelines batched statements over the wire natively, so
+this already collapses N roundtrips into ~1. **MySQL Connector/J does not** — by default it sends
+one roundtrip per statement in the batch regardless of `addBatch()`/`executeBatch()`, silently
+negating the optimization. Add `rewriteBatchedStatements=true` to the JDBC URL
+(`jdbc:mysql://host:3306/db?rewriteBatchedStatements=true`) so Connector/J rewrites the batch into
+a single multi-statement round trip. This is a driver/connection setting — okapi cannot set it for
+you since it doesn't own your `DataSource`.
+
+We verified the rewrite genuinely happens (Connector/J's own query profiler confirms a batch of
+1000 `UPDATE`s becomes one multi-statement round trip), but couldn't measure a net speedup from it
+in our benchmark — see
+[`results-mysql-rewrite-batched-statements.md`](benchmarks/results-mysql-rewrite-batched-statements.md)
+for why (a client-side response-decoding cost that scales with batch size). We still recommend
+enabling it — the round-trip savings are real and matter most against a network-hosted MySQL — but
+unlike Postgres's measured 10.2×, we don't have a MySQL multiplier to back it with.
+
+Full methodology, raw JMH results, before/after per change: [`benchmarks/`](benchmarks/), including
+[`results-postopt-KOJAK-75.md`](benchmarks/results-postopt-KOJAK-75.md) for the batch-UPDATE
+numbers above.
 
 ## Build
 

benchmarks/README.md

@@ -103,3 +103,8 @@ investigate variability sources (background processes, thermal throttling, GC).
 
 - [`results-baseline-2026-04.md`](results-baseline-2026-04.md) — pre-optimization baseline
   (sync sequential delivery, single-threaded scheduler)
+- [`results-postopt-KOJAK-75.md`](results-postopt-KOJAK-75.md) — batch UPDATE via JDBC
+  `executeBatch()`, Postgres: 10.2×
+- [`results-mysql-rewrite-batched-statements.md`](results-mysql-rewrite-batched-statements.md) —
+  same optimization on MySQL: mechanism verified correct, but no net speedup measured at this
+  batch size/hardware — read before assuming the Postgres multiplier transfers

benchmarks/mysql-outbox-store-update-batch.json

@@ -0,0 +1,248 @@
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