Merge pull request #14 from SaridakisStamatisChristos/codex/evaluate-production-readiness-and-assign-grade

Add operational runbook documentation

Author: SaridakisStamatisChristos

README.md

@@ -133,6 +133,12 @@ Visualise the outputs via `benchmarks/bench_plots.ipynb`, and read [`docs/benchm
 
 ---
 
+## 🛡️ Operations
+
+For day-2 guidance—monitoring, alerting, capacity planning, and a step-by-step upgrade playbook—see the [Operational Guide](docs/operations.md).
+
+---
+
 ## 🗺️ Roadmap
 
 * Optional NumPy/C hot paths for sort/merge.

docs/operations.md

@@ -0,0 +1,69 @@
+# Operational Guide
+
+This guide summarizes the day-2 operational practices for running the KLL sketch in production services and batch pipelines.
+
+## Monitoring & Observability
+
+### Key service level indicators
+- **Ingestion throughput**: items/second (overall and per tenant). Track moving averages and high-water marks to detect ingestion stalls.
+- **Sketch merge latency**: wall-clock latency and failure rate for merge jobs. Watch for sustained increases (>2× baseline) which indicate undersized capacity.
+- **Quantile query latency**: P50/P95/P99 latency per query type (`quantile`, `quantiles`, `rank`).
+- **Serialized blob size**: mean and max payload size emitted by `to_bytes()`. Sudden jumps usually mean skewed data or capacity misconfiguration.
+- **Compaction counters**: number of compactions per level. Rising compaction frequency implies either a bursty workload or an undersized `capacity`.
+
+### Recommended instrumentation
+- Wrap ingestion entry points (e.g., `add`, `extend`) and query methods with metrics (`Counter`/`Histogram`). Expose via Prometheus or your platform’s native telemetry.
+- Log sketch metadata at debug level: `capacity`, `size`, level configuration, deterministic seed. Mask PII, logging only aggregates.
+- Emit structured events when merges occur, including source shard identifiers and duration.
+- Sample serialized blobs and validate round-trips in background tasks to catch corruption early.
+
+### Alerting policies
+- **Ingestion stalled**: alert when ingestion throughput drops to zero for more than 1 minute while upstream traffic persists.
+- **Merge backlog**: alert when merge queue length exceeds 2× normal baseline for 5 consecutive intervals.
+- **Query SLO breach**: alert when P95 query latency exceeds the agreed SLO (e.g., 50 ms) for 3 consecutive intervals.
+- **Serialization errors**: alert on any deserialization failures or checksum mismatches.
+
+### Dashboards & diagnostics
+- Chart ingestion throughput, query latency, and serialized blob sizes on a single operational dashboard.
+- Maintain a table of per-level buffer sizes and compaction counts to aid debugging.
+- Surface release version, git SHA, and configuration flags in dashboard annotations.
+
+## Capacity & Configuration Management
+- Size `capacity` based on required rank error ε using `ε ≈ 1 / capacity`. Double the capacity if you observe compaction hot spots or serialized blobs exceeding transport limits.
+- For workloads with heavy merges, align shard capacities; a single small shard can dominate error.
+- Document default seeds and level configuration in configuration management. Keep environment-specific overrides in version control.
+
+## Upgrade Playbook
+
+1. **Review release notes**
+   - Read `CHANGELOG.md` for breaking changes, new features, and migration steps.
+   - Check dependency bumps, especially the minimum supported Python version and `setuptools` constraints.
+
+2. **Stage the upgrade**
+   - Pin the target version in your dependency management tool and deploy to a staging environment.
+   - Run the full pytest suite plus representative workload benchmarks (`benchmarks/bench_kll.py`) on staging data.
+   - Validate that serialized blobs created by the previous version deserialize correctly with the new release (forwards compatibility) and vice versa (backwards compatibility when rolling back).
+
+3. **Production rollout**
+   - Perform a canary deployment (5–10% traffic) and monitor ingestion throughput, query latency, and error rates for at least one compaction window.
+   - If metrics remain within SLOs, proceed with progressive rollout to all shards or services.
+
+4. **Rollback procedure**
+   - Maintain the previous version pinned and ready for redeploy.
+   - Because serialization is version-stable, downgrades are safe provided no breaking schema change is noted in the changelog. Always confirm with staged rollback tests.
+   - After rollback, clear metrics annotations and document the incident.
+
+5. **Post-upgrade validation**
+   - Confirm dashboards show the new version identifiers.
+   - Update operational runbooks with any new configuration flags or behaviours introduced in the release.
+
+## Incident Response Checklist
+- Capture failing serialized blobs and store them with timestamps and shard identifiers.
+- Dump per-level buffer states via `KLL.debug_state()` (if enabled) or the equivalent introspection helper for forensic analysis.
+- Reconstruct workloads that triggered failures using recorded input batches and replay them in a sandbox before patching production.
+
+## Documentation & Runbook Hygiene
+- Store this guide alongside other operational runbooks in your organization’s knowledge base.
+- Schedule quarterly reviews to update thresholds, metrics, and playbooks in line with observed production behaviour.
+- When onboarding new services, link this document from their service runbooks to ensure consistent operational standards.
+