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Monitoring & Alerting

Continuous monitoring is essential for understanding system health, identifying performance bottlenecks, diagnosing errors, and ensuring data quality at scale.

Log Aggregation

  • Purpose: Centralize logs from all distributed components (Dispatcher, Workers, Parsers, Django App, Databases) into a single, searchable system.
  • Benefits: Enables comprehensive debugging, tracing requests across services, analyzing error patterns, and understanding application behavior.
  • Tools: ELK Stack (Elasticsearch, Logstash, Kibana), Grafana Loki, Splunk, Datadog Logs, AWS CloudWatch Logs, Google Cloud Logging.
  • Implementation: Configure all services and applications (including Celery and potentially detailed Scrapy stats/logs if applicable) to ship logs (preferably in a structured format like JSON) to the chosen aggregation platform.

Metrics Collection

  • Purpose: Collect time-series numerical data about system performance and behavior.
  • Benefits: Provides quantitative insights into system health, resource utilization, throughput, and error rates over time. Essential for dashboarding, alerting, and capacity planning.
  • Tools: Prometheus + Grafana, InfluxDB, Datadog, StatsD, AWS CloudWatch Metrics, Google Cloud Monitoring.
  • Key Metrics to Track:
    • Message Queue:
      • queue_depth (messages ready/unacknowledged)
      • message_age (oldest message)
      • publish_rate / consume_rate
    • Celery Workers:
      • worker_count (running instances)
      • cpu_utilization / memory_utilization (per worker/node)
      • task_queued_duration / task_execution_duration
      • task_success_rate / task_failure_rate / task_retry_rate (per task type)
    • Scraping Specific (Custom Metrics / Logs):
      • items_scraped_count (per site/total)
      • pages_fetched_count (per site/status code)
      • error_rate_per_domain
      • proxy_success_rate / proxy_failure_rate
      • captcha_detected_count
    • Databases (Config & Structured):
      • connection_count
      • query_latency
      • cpu/memory/disk_utilization
      • replication_lag (if applicable)
    • Business Metrics:
      • new_jobs_added_per_hour/day
      • data_freshness (e.g., max time since last update for active sources)

Visualization & Dashboarding

  • Purpose: Create visual representations of collected metrics and log data to provide an at-a-glance overview of system health and performance trends.
  • Tools: Grafana, Kibana, Datadog Dashboards, Google Cloud Monitoring Dashboards, AWS CloudWatch Dashboards.
  • Implementation: Build dashboards displaying key metrics (queue depths, worker status, error rates, scrape rates per site, DB performance). Allow filtering by time range, service, site, etc.

Alerting

  • Purpose: Proactively notify operators about critical issues or potential problems based on predefined thresholds or patterns in metrics and logs.
  • Tools: Alertmanager (with Prometheus), Grafana Alerting, Datadog Monitors, Cloud provider native alerting (CloudWatch Alarms, Google Cloud Alerting). Integrations with PagerDuty, Slack, etc.
  • Example Alert Conditions:
    • High message queue depth (above threshold for X minutes).
    • High Celery task failure rate (above Y% over Z minutes).
    • Persistently high error rate for a specific target website.
    • Dead-letter queue depth increasing.
    • Low scrape success rate for critical sources.
    • High CPU/Memory utilization on workers or database nodes.
    • Critical errors detected in aggregated logs.