Chunk 4.0

In this session, we investigated an apparent data corruption issue discovered during S3 load testing. After adding better error classification to the loadtest tool—distinguishing between actual checksum mismatches and context deadline timeouts—we confirmed that no real corruption was occurring; the earlier "verify errors" were simply timeouts at the end of test runs. This led us to focus on optimizing the YCQL write path to improve throughput and reduce database contention under high concurrency. We implemented a `CQLBatcher` in the `database/cqldb` package that collects individual CQL INSERT calls and flushes them in batches (default 15,000 entries or within 10–30 ms). The batcher uses a worker pool (8 workers) with exponential backoff retries and blocks callers until the batch is committed, preserving read‑after‑write consistency. We integrated it into `ObjectIndexCql.Put()` by adding a `Session()` method to the `Database` interface and exposing the underlying `gocql.Session`. We also fixed a configuration bug that prevented kuri nodes from starting (`RetrievableRepairThreshold > MinimumReplicaCount`) and updated the `gen-config.sh` script accordingly. To eliminate the Docker userland proxy as a bottleneck at high concurrency, we rewrote `docker-compose.yml` to use host networking, removed the unnecessary nginx web‑UI proxy, and adjusted port mappings. Load tests with the batcher showed clean results at 10 workers (~115 MB/s, 0 corruption) and throughput scaling to ~334 MB/s at 100 workers, but connection resets appeared at 100+ workers due to the proxy. The host network change should resolve these issues and allow testing at much higher concurrency. The dominant themes were **performance optimization through batching**, **correctness of read‑after‑write verification**, and **infrastructure tuning for high‑throughput testing**.

The Architecture of a Debugging Breakthrough: From False Corruption to Host Networking in a Distributed S3 Storage System 2684 words

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