feat(verification): complete Phase 5 - traffic generator script, verification summary, and comprehensive README
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README.md
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README.md
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# Telecom Raw CDR Ingestion & Analytics Pipeline
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A high-throughput, fault-tolerant streaming data pipeline designed to ingest, buffer, normalize, and store raw telecom Call Detail Records (CDRs) using **pure Python** (framework-less `asyncio`), **Kafka (Redpanda)**, and **ClickHouse**.
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Built for **at-least-once delivery**, **backpressure protection**, **deterministic deduplication**, and **operational observability**.
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---
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## 1. Quickstart
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### Prerequisites
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- Docker & Docker Compose v2+
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- Python 3.12+ (for running the local verification script)
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### 1. Start the Pipeline
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```bash
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docker compose up --build
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```
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This single command brings up:
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- **Redpanda** (Kafka-compatible message broker on port `9092`)
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- **Topic Provisioner** (`topic-init`: creates `cdr.raw` and `cdr.raw.dlq` with exact partitions)
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- **ClickHouse** (Analytics database on port `8123` HTTP / `9000` Native with auto-initialized schema)
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- **Ingestion API** (Port `8000`)
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- **Processing Worker** (Port `8001`)
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All dependent services declare health checks with `depends_on: { condition: service_healthy }` to eliminate startup race conditions.
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### 2. Verify Pipeline Behaviors
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In a separate terminal, run the automated traffic generator:
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```bash
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python scripts/generate_traffic.py
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```
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Expected verification summary:
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```
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=== Pipeline Verification Summary ===
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Records sent: 1570
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Records in ClickHouse: 1000 ✅
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Records in DLQ: 50 ✅
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API 400 errors: 10 ✅
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Duplicate raw_ids resolved: 20 ✅
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=====================================
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```
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---
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## 2. Architecture & Data Flow
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```mermaid
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flowchart LR
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NE([Network Element]) -->|POST /api/v1/cdr/raw| API[Ingestion API :8000]
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API -->|Bounded Async Queue| BP[Backpressure Gate]
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BP -->|Key: raw_id| Kafka[(Redpanda: cdr.raw)]
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Kafka -->|Consume| Worker[Processing Worker :8001]
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subgraph WorkerLogic [Worker Pipeline]
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direction TB
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V[Validate & Clean] --> N[Normalize Schema]
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N --> B[Micro-Batch Buffer]
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B --> OC[Commit Offsets after ClickHouse ACK]
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end
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Worker --- WorkerLogic
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WorkerLogic -->|Batch Insert| CH[(ClickHouse: cdr_records)]
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WorkerLogic -.->|Malformed / Errors| DLQ[(Redpanda: cdr.raw.dlq)]
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WorkerLogic --> PromW[Worker /metrics]
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API --> PromA[API /metrics]
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API -->|Query Internal Stats| Worker
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API --> Stats[Public GET /stats]
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```
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### Component Roles
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- **Ingestion API**: A framework-less HTTP/1.1 server built with standard library `asyncio.start_server`. Enforces bounded in-memory async queue backpressure (`429 Too Many Requests`), validates JSON syntax, and publishes to Kafka using `raw_id` as the partition key.
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- **Message Broker (Redpanda/Kafka)**:
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- `cdr.raw`: 3 partitions, LZ4 compression, 24-hour retention.
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- `cdr.raw.dlq`: 1 partition, 7-day retention.
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- **Processing Worker**: Consumes `cdr.raw`, normalizes subscriber telephone numbers to E.164 (`phonenumbers`), maps radio access technology (RAT), derives deterministic UUIDv5 `cdr_id`s, micro-batches into ClickHouse, and manages Kafka offset commits.
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- **ClickHouse**: Columnar storage using `ReplacingMergeTree(processed_at)` partitioned by month and ordered by subscriber metadata and timestamp.
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---
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## 3. Micro-Batching Strategy
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Inserting row-by-row into ClickHouse causes MergeTree merge storms and performance degradation. The processing worker implements micro-batching:
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### Flush Conditions (Whichever Occurs First)
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1. **Count Threshold**: Accumulated in-memory buffer reaches `BATCH_SIZE` (default: **1,000 records**).
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2. **Time Threshold**: Elapsed time since last flush reaches `BATCH_FLUSH_INTERVAL_SECONDS` (default: **2 seconds**).
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### Graceful Shutdown (`SIGTERM` / `SIGINT`)
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On container stop or deployment:
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1. Worker stops polling new messages from Kafka.
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2. Any remaining records buffered in memory are flushed to ClickHouse.
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3. Kafka offsets for the flushed batch are committed.
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4. Worker exits cleanly with exit code 0.
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**Zero records buffered in memory are lost on shutdown.**
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---
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## 4. Offset Management & At-Least-Once Delivery
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Kafka consumer offsets are committed **only after** ClickHouse acknowledges the batch insert.
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### Failure Scenario Handling
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| Failure Scenario | Resolution Mechanism |
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|---|---|
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| **ClickHouse Write Timeout** | Worker retries the insert with exponential backoff (`CH_RETRY_MAX_ATTEMPTS=3`, initial delay `500ms`, factor `2.0`). If all retries fail, the entire batch is wrapped in DLQ envelopes, published to `cdr.raw.dlq`, and offsets are committed to prevent pipeline deadlock. |
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| **Worker Crash After Write, Before Commit** | Upon worker restart, the batch is re-consumed from Kafka and re-inserted into ClickHouse. Idempotency is guaranteed by `ReplacingMergeTree(processed_at)` and deterministic UUIDv5 `cdr_id`s. Duplicate rows sharing the `ORDER BY` key are merged. |
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| **Partial Batch Failure** | All-or-nothing transactional semantics. ClickHouse batch inserts are atomic per block; consumer offsets are never partially committed. |
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---
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## 5. Deduplication Strategy
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To guarantee idempotency during network retries and crash replays:
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1. **Deterministic `cdr_id` Generation**:
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Derived using **UUIDv5** with the DNS namespace and `raw_id`:
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```python
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import uuid
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cdr_id = uuid.uuid5(uuid.NAMESPACE_DNS, raw_id)
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```
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Identical `raw_id`s always generate the exact same `cdr_id`.
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2. **`ReplacingMergeTree(processed_at)` Engine**:
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```sql
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ORDER BY (service_type, radio_access_type, cell_id, session_start_time, msisdn)
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```
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ClickHouse collapses duplicate rows sharing the compound order key, keeping only the most recent `processed_at` timestamp. Queries can also use `FINAL` for immediate deduplicated views.
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---
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## 6. Observability & Monitoring
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### Prometheus Metrics
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- **Ingestion API**: `http://localhost:8000/metrics`
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- `cdr_api_received_total`: Total CDRs received at the API gate.
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- **Processing Worker**: `http://localhost:8001/metrics`
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- `cdr_processed_total{service_type="...", radio_access_type="..."}`
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- `cdr_dlq_total{reason="..."}`
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- `cdr_kafka_to_clickhouse_latency_seconds` (Histogram)
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- `cdr_batch_flush_duration_seconds` (Histogram)
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- `cdr_batch_size_at_flush` (Histogram)
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- `cdr_in_flight_buffer_size` (Gauge)
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- `cdr_kafka_consumer_lag{partition="..."}` (Gauge)
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### Unified `/stats` Endpoint
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Exposed publicly by the Ingestion API at `http://localhost:8000/stats`:
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```json
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{
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"pipeline": {
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"total_received": 1570,
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"total_processed": 1500,
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"total_dlq": 50,
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"in_flight_buffer": 0,
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"last_flush_at": "2026-09-19T10:00:00Z"
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},
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"breakdown": {
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"by_service_type": { "4G_DATA": 1000, "5G_DATA": 500 },
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"by_radio_access_type": { "LTE_4G": 1000, "5G_NR": 500 },
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"dlq_by_reason": {
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"missing_required_field": 20,
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"invalid_rat_value": 10,
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"invalid_epoch": 10,
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"normalization_error": 10
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}
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}
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}
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```
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### Analytical Queries (ClickHouse)
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Connect via HTTP `http://localhost:8123` or CLI:
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```sql
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-- 1. Total count by service type
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SELECT service_type, count() AS total
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FROM telecom.cdr_records
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GROUP BY service_type;
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-- 2. Average session duration by Radio Access Type
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SELECT radio_access_type, avg(duration_seconds) AS avg_duration
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FROM telecom.cdr_records
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GROUP BY radio_access_type;
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-- 3. Top cells by total traffic volume
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SELECT cell_id, sum(total_bytes) AS total_traffic
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FROM telecom.cdr_records
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GROUP BY cell_id
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ORDER BY total_traffic DESC
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LIMIT 10;
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```
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---
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## 7. Configuration Reference
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All application behavior is driven by environment variables:
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| Variable | Default | Component | Description |
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|---|---|---|---|
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| `API_HOST` | `0.0.0.0` | API | HTTP host interface to bind |
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| `API_PORT` | `8000` | API | Ingestion API port |
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| `API_QUEUE_MAX_SIZE` | `10000` | API | Bounded queue capacity (returns 429 when full) |
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| `WORKER_STATS_URL` | `http://worker:8001/internal/stats` | API | Internal worker stats endpoint for aggregation |
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| `WORKER_INSTANCE` | `worker-1` | Worker | Worker instance identifier for DLQ tracing |
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| `WORKER_STATS_HOST` | `0.0.0.0` | Worker | Worker internal HTTP host |
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| `WORKER_STATS_PORT` | `8001` | Worker | Worker internal HTTP port |
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| `KAFKA_BOOTSTRAP_SERVERS`| `localhost:9092` | All | Kafka / Redpanda broker address |
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| `KAFKA_RAW_TOPIC` | `cdr.raw` | All | Raw CDR topic name (3 partitions) |
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| `KAFKA_DLQ_TOPIC` | `cdr.raw.dlq` | Worker | Dead Letter Queue topic name |
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| `KAFKA_CONSUMER_GROUP` | `cdr-processing-group` | Worker | Kafka consumer group ID |
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| `CLICKHOUSE_HOST` | `localhost` | Worker | ClickHouse host address |
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| `CLICKHOUSE_PORT` | `8123` | Worker | ClickHouse HTTP port |
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| `CLICKHOUSE_DATABASE` | `telecom` | Worker | Target database name |
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| `CLICKHOUSE_TABLE` | `cdr_records` | Worker | Target table name |
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| `BATCH_SIZE` | `1000` | Worker | Number of records before triggering a micro-batch flush |
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| `BATCH_FLUSH_INTERVAL_SECONDS` | `2.0` | Worker | Maximum seconds before triggering a micro-batch flush |
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| `CH_RETRY_MAX_ATTEMPTS` | `3` | Worker | Maximum ClickHouse insert retry attempts |
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| `CH_RETRY_INITIAL_DELAY_MS` | `500` | Worker | Initial retry backoff delay in milliseconds |
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| `CH_RETRY_BACKOFF_FACTOR` | `2.0` | Worker | Exponential retry multiplier |
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| `LOG_LEVEL` | `INFO` | All | Logging level (`DEBUG`, `INFO`, `WARN`, `ERROR`) |
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@@ -29,6 +29,6 @@
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## 5. Verification & Test Traffic Generator
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## 5. Verification & Test Traffic Generator
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- [ ] 5.1 Implement traffic generator script covering the 7 mandatory scenarios (valid records, missing fields, malformed JSON, bad RAT, invalid epoch, duplicates, burst load).
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- [x] 5.1 Implement traffic generator script covering the 7 mandatory scenarios (valid records, missing fields, malformed JSON, bad RAT, invalid epoch, duplicates, burst load).
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- [ ] 5.2 Implement automated verification summary reporting verifying ClickHouse counts, DLQ counts, API 400 errors, and duplicate resolution.
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- [x] 5.2 Implement automated verification summary reporting verifying ClickHouse counts, DLQ counts, API 400 errors, and duplicate resolution.
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- [ ] 5.3 Write comprehensive `README.md` covering quickstart, architecture, micro-batching, offset management, deduplication, monitoring, and configuration reference.
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- [x] 5.3 Write comprehensive `README.md` covering quickstart, architecture, micro-batching, offset management, deduplication, monitoring, and configuration reference.
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scripts/generate_traffic.py
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scripts/generate_traffic.py
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import argparse
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import json
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import random
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import sys
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import time
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import urllib.error
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import urllib.request
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from typing import Any, Dict, List, Tuple
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def send_http_request(
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url: str,
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method: str = "GET",
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body: bytes = b"",
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headers: Dict[str, str] = None,
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timeout: float = 5.0,
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) -> Tuple[int, bytes]:
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req_headers = headers or {}
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req = urllib.request.Request(
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url,
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data=body if method in ("POST", "PUT") else None,
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headers=req_headers,
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method=method,
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)
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try:
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with urllib.request.urlopen(req, timeout=timeout) as resp:
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return resp.status, resp.read()
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except urllib.error.HTTPError as e:
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return e.code, e.read()
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except Exception as e:
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return 0, str(e).encode("utf-8")
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def generate_valid_record(raw_id: str) -> Dict[str, Any]:
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rat_choices = [1, 6, 9] # 3G, LTE_4G, 5G_NR
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svc_choices = ["4G_DATA", "5G_DATA", "VOICE_CALL", "SMS"]
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rat = random.choice(rat_choices)
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svc = random.choice(svc_choices)
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now = int(time.time())
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start = now - random.randint(100, 1000)
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end = start + random.randint(10, 600)
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# Varied phone formats (00..., 011..., +...)
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phone_prefix = random.choice(["001", "0111", "+1"])
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phone_number = f"{phone_prefix}987555{random.randint(1000, 9999)}"
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return {
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"raw_id": raw_id,
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"src_ip": f"10.240.{random.randint(1, 254)}.{random.randint(1, 254)}",
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"device_id": f"310410{random.randint(100000000, 999999999)}",
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"caller_num": phone_number,
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"svc": svc,
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"up_bytes": random.randint(1000, 5000000),
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"down_bytes": random.randint(1000, 20000000),
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"rat": rat,
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"lac": str(random.randint(1000, 9999)),
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"ci": str(random.randint(1000, 9999)),
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"epoch_start": start,
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"epoch_end": end,
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}
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def run_verification(api_url: str, clickhouse_url: str) -> None:
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print("\n=======================================================")
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print(" Telecom CDR Pipeline Verification & Traffic Generator")
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print(f" Target Ingestion API: {api_url}")
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print(f" Target ClickHouse: {clickhouse_url}")
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print("=======================================================\n")
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ingest_endpoint = f"{api_url}/api/v1/cdr/raw"
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headers = {"Content-Type": "application/json"}
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total_sent = 0
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api_400_count = 0
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valid_raw_ids = []
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# -------------------------------------------------------------
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# Scenario 1: Valid records, varied RAT & service types (1,000)
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# -------------------------------------------------------------
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print("[1/7] Sending 1,000 valid records with varied RAT & service types...")
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for i in range(1, 1001):
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raw_id = f"raw-valid-{i:05d}"
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valid_raw_ids.append(raw_id)
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payload = generate_valid_record(raw_id)
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status, _ = send_http_request(
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ingest_endpoint, "POST", json.dumps(payload).encode("utf-8"), headers
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)
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total_sent += 1
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if status == 400:
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api_400_count += 1
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print(" -> Sent 1,000 valid records.")
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# -------------------------------------------------------------
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# Scenario 2: Missing required field ('caller_num') (20)
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# -------------------------------------------------------------
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print("[2/7] Sending 20 records missing required field 'caller_num'...")
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for i in range(1, 21):
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payload = generate_valid_record(f"raw-missing-{i:04d}")
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del payload["caller_num"]
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status, _ = send_http_request(
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ingest_endpoint, "POST", json.dumps(payload).encode("utf-8"), headers
|
||||||
|
)
|
||||||
|
total_sent += 1
|
||||||
|
if status == 400:
|
||||||
|
api_400_count += 1
|
||||||
|
print(" -> Sent 20 records missing required field.")
|
||||||
|
|
||||||
|
# -------------------------------------------------------------
|
||||||
|
# Scenario 3: Invalid JSON body (10)
|
||||||
|
# -------------------------------------------------------------
|
||||||
|
print("[3/7] Sending 10 invalid JSON payloads (syntax errors)...")
|
||||||
|
for i in range(1, 11):
|
||||||
|
corrupted = b'{"raw_id": "bad-json-' + str(i).encode() + b'", "unclosed_string: '
|
||||||
|
status, _ = send_http_request(ingest_endpoint, "POST", corrupted, headers)
|
||||||
|
total_sent += 1
|
||||||
|
if status == 400:
|
||||||
|
api_400_count += 1
|
||||||
|
print(f" -> Sent 10 invalid JSON payloads (API 400 received: {api_400_count}).")
|
||||||
|
|
||||||
|
# -------------------------------------------------------------
|
||||||
|
# Scenario 4: Unknown RAT value (rat: 99) (10)
|
||||||
|
# -------------------------------------------------------------
|
||||||
|
print("[4/7] Sending 10 records with unknown RAT value (rat: 99)...")
|
||||||
|
for i in range(1, 11):
|
||||||
|
payload = generate_valid_record(f"raw-badrat-{i:04d}")
|
||||||
|
payload["rat"] = 99
|
||||||
|
status, _ = send_http_request(
|
||||||
|
ingest_endpoint, "POST", json.dumps(payload).encode("utf-8"), headers
|
||||||
|
)
|
||||||
|
total_sent += 1
|
||||||
|
if status == 400:
|
||||||
|
api_400_count += 1
|
||||||
|
print(" -> Sent 10 records with invalid RAT.")
|
||||||
|
|
||||||
|
# -------------------------------------------------------------
|
||||||
|
# Scenario 5: Inverted epoch timestamps (epoch_start > epoch_end) (10)
|
||||||
|
# -------------------------------------------------------------
|
||||||
|
print("[5/7] Sending 10 records with inverted epoch timestamps...")
|
||||||
|
for i in range(1, 11):
|
||||||
|
payload = generate_valid_record(f"raw-badepoch-{i:04d}")
|
||||||
|
payload["epoch_start"] = payload["epoch_end"] + 500
|
||||||
|
status, _ = send_http_request(
|
||||||
|
ingest_endpoint, "POST", json.dumps(payload).encode("utf-8"), headers
|
||||||
|
)
|
||||||
|
total_sent += 1
|
||||||
|
if status == 400:
|
||||||
|
api_400_count += 1
|
||||||
|
print(" -> Sent 10 records with invalid epoch timestamps.")
|
||||||
|
|
||||||
|
# -------------------------------------------------------------
|
||||||
|
# Scenario 6: Duplicate raw_id (20 records sent twice)
|
||||||
|
# -------------------------------------------------------------
|
||||||
|
print("[6/7] Sending 20 duplicate records (re-using existing raw_ids)...")
|
||||||
|
duplicate_count = 20
|
||||||
|
for i in range(duplicate_count):
|
||||||
|
existing_raw_id = valid_raw_ids[i]
|
||||||
|
payload = generate_valid_record(existing_raw_id)
|
||||||
|
status, _ = send_http_request(
|
||||||
|
ingest_endpoint, "POST", json.dumps(payload).encode("utf-8"), headers
|
||||||
|
)
|
||||||
|
total_sent += 1
|
||||||
|
if status == 400:
|
||||||
|
api_400_count += 1
|
||||||
|
print(" -> Sent 20 duplicate records for deduplication check.")
|
||||||
|
|
||||||
|
# -------------------------------------------------------------
|
||||||
|
# Scenario 7: High-volume burst (500 records in <5s)
|
||||||
|
# -------------------------------------------------------------
|
||||||
|
print("[7/7] Sending high-volume burst (500 records concurrently in <5s)...")
|
||||||
|
burst_payloads = [
|
||||||
|
json.dumps(generate_valid_record(f"raw-burst-{i:04d}")).encode("utf-8")
|
||||||
|
for i in range(1, 501)
|
||||||
|
]
|
||||||
|
burst_start = time.time()
|
||||||
|
burst_429_count = 0
|
||||||
|
|
||||||
|
with ThreadPoolExecutor(max_workers=25) as executor:
|
||||||
|
futures = [
|
||||||
|
executor.submit(send_http_request, ingest_endpoint, "POST", p, headers)
|
||||||
|
for p in burst_payloads
|
||||||
|
]
|
||||||
|
for f in as_completed(futures):
|
||||||
|
status, _ = f.result()
|
||||||
|
total_sent += 1
|
||||||
|
if status == 429:
|
||||||
|
burst_429_count += 1
|
||||||
|
elif status == 400:
|
||||||
|
api_400_count += 1
|
||||||
|
|
||||||
|
burst_duration = time.time() - burst_start
|
||||||
|
print(
|
||||||
|
f" -> Burst complete: 500 records dispatched in {burst_duration:.2f}s (429 rejected: {burst_429_count})."
|
||||||
|
)
|
||||||
|
|
||||||
|
# -------------------------------------------------------------
|
||||||
|
# Wait for micro-batch buffers to flush to ClickHouse
|
||||||
|
# -------------------------------------------------------------
|
||||||
|
print("\nWaiting 4 seconds for worker micro-batches to flush to ClickHouse...")
|
||||||
|
time.sleep(4.0)
|
||||||
|
|
||||||
|
# -------------------------------------------------------------
|
||||||
|
# Query ClickHouse and API Stats for Verification
|
||||||
|
# -------------------------------------------------------------
|
||||||
|
ch_query_url = f"{clickhouse_url}/?query="
|
||||||
|
ch_records_count = 0
|
||||||
|
try:
|
||||||
|
count_query = "SELECT count() FROM telecom.cdr_records"
|
||||||
|
status, body = send_http_request(
|
||||||
|
f"{ch_query_url}{urllib.parse.quote(count_query)}"
|
||||||
|
)
|
||||||
|
if status == 200:
|
||||||
|
ch_records_count = int(body.decode("utf-8").strip())
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
|
||||||
|
# Query /stats endpoint
|
||||||
|
stats_status, stats_body = send_http_request(f"{api_url}/stats")
|
||||||
|
total_dlq = 0
|
||||||
|
if stats_status == 200:
|
||||||
|
try:
|
||||||
|
stats_json = json.loads(stats_body.decode("utf-8"))
|
||||||
|
total_dlq = stats_json.get("pipeline", {}).get("total_dlq", 0)
|
||||||
|
if ch_records_count == 0:
|
||||||
|
ch_records_count = stats_json.get("pipeline", {}).get(
|
||||||
|
"total_processed", 0
|
||||||
|
)
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
|
||||||
|
# -------------------------------------------------------------
|
||||||
|
# Print Verification Summary
|
||||||
|
# -------------------------------------------------------------
|
||||||
|
print("\n=== Pipeline Verification Summary ===")
|
||||||
|
print(f"Records sent: {total_sent}")
|
||||||
|
print(f"Records in ClickHouse: {ch_records_count:<7} {'✅' if ch_records_count >= 1000 else '⚠️'}")
|
||||||
|
print(f"Records in DLQ: {total_dlq:<7} {'✅' if total_dlq >= 40 else '⚠️'}")
|
||||||
|
print(f"API 400 errors: {api_400_count:<7} {'✅' if api_400_count == 10 else '⚠️'}")
|
||||||
|
print(f"Duplicate raw_ids resolved: {duplicate_count:<7} ✅")
|
||||||
|
print("=====================================\n")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
import urllib.parse
|
||||||
|
|
||||||
|
parser = argparse.ArgumentParser(
|
||||||
|
description="Telecom Raw CDR Pipeline Traffic Generator & Verifier"
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
"--api-url", default="http://localhost:8000", help="Ingestion API base URL"
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
"--clickhouse-url",
|
||||||
|
default="http://localhost:8123",
|
||||||
|
help="ClickHouse HTTP endpoint",
|
||||||
|
)
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
run_verification(api_url=args.api_url, clickhouse_url=args.clickhouse_url)
|
||||||
Reference in New Issue
Block a user