## 1. Project Setup & Common Utilities

- [x] 1.1 Create project structure, `requirements.txt` (with `confluent-kafka`, `clickhouse-connect`, `phonenumbers`), and `.env.example` defining all environment variables and verify file presence.
- [x] 1.2 Implement standard library structured JSON logging module emitting `timestamp`, `level`, `service`, `raw_id`, and `event`, and verify output formatting with unit test.
- [x] 1.3 Create idempotent ClickHouse initialization script (`init.sql`) creating database `telecom` and `ReplacingMergeTree` table `cdr_records` with TTL and indexing settings, and verify schema correctness.

## 2. Framework-less Async Ingestion API

- [x] 2.1 Implement lightweight HTTP/1.1 server using standard library `asyncio.start_server` with route dispatching for `POST /api/v1/cdr/raw`, `GET /metrics`, and `GET /stats`.
- [x] 2.2 Implement bounded async queue (`asyncio.Queue`) backpressure: verify `429 Too Many Requests` when queue is saturated and `202 Accepted` when enqueued.
- [x] 2.3 Implement incoming payload validation: verify `400 Bad Request` with structured JSON error `{ "error": "...", "detail": "..." }` on malformed JSON or missing required fields.
- [x] 2.4 Integrate `confluent-kafka` producer publishing to `cdr.raw` using `raw_id` as message key, and return `503 Service Unavailable` if Kafka is unreachable.
- [x] 2.5 Implement Prometheus metrics endpoint (`cdr_api_received_total`) and public `GET /stats` endpoint that fetches worker runtime stats via internal HTTP and returns unified JSON.

## 3. Processing Worker Implementation

- [x] 3.1 Implement Kafka consumer loop with `confluent-kafka` consuming from `cdr.raw` and DLQ producer publishing to `cdr.raw.dlq`.
- [x] 3.2 Implement safe parser routing malformed records to DLQ with structured envelope and reason taxonomy (`missing_required_field`, `invalid_rat_value`, `invalid_epoch`, `malformed_json`, `normalization_error`).
- [x] 3.3 Implement schema normalizer: E.164 phone conversion via `phonenumbers`, RAT mapping (`1`→`3G`, `6`→`LTE_4G`, `9`→`5G_NR`), `cell_id` derivation (`lac_ci`), and deterministic UUIDv5 `cdr_id` generation.
- [x] 3.4 Implement micro-batching buffer flushing on 1,000 records or 2 seconds elapsed, batch-inserting into ClickHouse via `clickhouse-connect`, and committing offsets only after ClickHouse ACK.
- [x] 3.5 Implement exponential backoff retry for ClickHouse write timeouts (`CH_RETRY_MAX_ATTEMPTS`, `CH_RETRY_INITIAL_DELAY_MS`, `CH_RETRY_BACKOFF_FACTOR`) with fallback to DLQ.
- [x] 3.6 Implement worker internal HTTP stats endpoint (`GET /internal/stats`) and worker Prometheus metrics (`cdr_processed_total`, `cdr_dlq_total`, histograms, gauges).
- [x] 3.7 Implement `SIGTERM` and `SIGINT` signal handlers to stop consuming, flush remaining in-memory buffer to ClickHouse, commit offsets, and exit cleanly without data loss.

## 4. Containerization & Orchestration

- [ ] 4.1 Create lightweight Dockerfiles for Ingestion API and Processing Worker without framework dependencies.
- [ ] 4.2 Create `docker-compose.yml` defining Kafka/Redpanda, ClickHouse, Ingestion API, and Processing Worker with healthcheck blocks and `depends_on: { condition: service_healthy }`.

## 5. Verification & Test Traffic Generator

- [ ] 5.1 Implement traffic generator script covering the 7 mandatory scenarios (valid records, missing fields, malformed JSON, bad RAT, invalid epoch, duplicates, burst load).
- [ ] 5.2 Implement automated verification summary reporting verifying ClickHouse counts, DLQ counts, API 400 errors, and duplicate resolution.
- [ ] 5.3 Write comprehensive `README.md` covering quickstart, architecture, micro-batching, offset management, deduplication, monitoring, and configuration reference.