Metrics Monitoring and Alerting System - Problem Statement

Overview

Design a comprehensive monitoring and alerting system similar to Prometheus, DataDog, or New Relic that can collect, store, and analyze metrics from distributed systems. The system should provide real-time monitoring, historical analysis, alerting capabilities, and visualization dashboards while handling millions of metrics per second.

Functional Requirements

Core Monitoring Features

Metric Collection: Pull and push-based metric collection from services
Time-series Storage: Efficient storage of time-series metric data
Query Language: Powerful query language for metric analysis (PromQL-like)
Alerting Rules: Define alert conditions based on metric thresholds
Notification Channels: Multi-channel alerting (email, Slack, PagerDuty, webhooks)
Dashboards: Customizable dashboards with various visualization types

Metric Types

Counters: Monotonically increasing values (requests, errors)
Gauges: Point-in-time values (CPU usage, memory, queue depth)
Histograms: Distribution of values (latency percentiles)
Summaries: Pre-calculated percentiles and counts
Rates: Derived metrics (requests per second)

Data Collection Methods

Pull Model: Scrape metrics from HTTP endpoints (Prometheus-style)
Push Model: Services push metrics to collectors (StatsD-style)
Agent-based: Deploy agents on hosts for system metrics
Service Discovery: Automatic discovery of monitoring targets
Custom Instrumentation: SDKs for application metrics

Alerting System

Alert Rules: Define conditions using query language
Alert States: Pending, firing, resolved
Alert Grouping: Group related alerts to reduce noise
Alert Routing: Route alerts based on labels and severity
Silencing: Temporarily suppress alerts during maintenance
Alert History: Track alert occurrences and resolutions

Visualization

Time-series Graphs: Line charts, area charts, stacked graphs
Single Stats: Current value displays with thresholds
Heatmaps: Distribution visualization over time
Tables: Tabular metric data with sorting and filtering
Custom Dashboards: Drag-and-drop dashboard builder
Dashboard Templates: Pre-built dashboards for common services

Service Discovery

Static Configuration: Manually configured targets
DNS-based Discovery: Discover targets via DNS SRV records
Kubernetes Discovery: Auto-discover pods and services
Consul/Etcd Integration: Service registry integration
Cloud Provider APIs: AWS, GCP, Azure auto-discovery
File-based Discovery: Dynamic target lists from files

Non-Functional Requirements

Performance Requirements

Ingestion Rate: Handle 10 million metrics per second
Query Latency: <1 second for simple queries, <5 seconds for complex
Scrape Interval: Support 1-60 second scrape intervals
Alert Evaluation: Evaluate alert rules within 30 seconds
Dashboard Refresh: Update dashboards every 5-30 seconds
Data Retention: Store high-resolution data for 15 days, downsampled for 1 year

Scalability Requirements

Monitored Services: Support 100,000+ monitored targets
Unique Metrics: Handle 100 million unique time series
Concurrent Queries: Support 10,000 concurrent queries
Alert Rules: Evaluate 100,000+ alert rules
Dashboard Users: Support 10,000 concurrent dashboard viewers
Data Points: Store 1 trillion data points per day

Reliability Requirements

System Uptime: 99.95% availability
Data Durability: 99.999% durability for metric data
Alert Delivery: 99.9% successful alert delivery
Scrape Success Rate: 99% successful metric scrapes
Query Success Rate: 99.9% successful queries
No Single Point of Failure: Fully distributed architecture

Data Accuracy

Metric Precision: Maintain precision for all metric types
Timestamp Accuracy: Millisecond-level timestamp precision
Aggregation Accuracy: Correct aggregations across time windows
Downsampling: Preserve statistical properties when downsampling
Gap Handling: Properly handle missing data points
Clock Skew: Handle clock differences across systems

Scale Constraints

Metric Volume

Monitored Services: 100,000 services
Metrics per Service: 100 metrics average
Scrape Interval: 15 seconds
Data Points per Second: 100K × 100 / 15 = 666,666 points/sec
Daily Data Points: 57.6 billion points/day
Storage per Point: 16 bytes (timestamp + value + metadata)
Daily Storage: 922 GB/day raw data

Query Load

Dashboard Users: 10,000 concurrent
Queries per Dashboard: 10 queries
Refresh Interval: 30 seconds
Query Rate: 10K × 10 / 30 = 3,333 queries/sec
Peak Query Rate: 10,000 queries/sec

Alert Evaluation

Alert Rules: 100,000 rules
Evaluation Interval: 30 seconds
Evaluations per Second: 100K / 30 = 3,333 evaluations/sec
Firing Alerts: 1,000 concurrent
Notifications per Hour: 10,000 notifications

Technical Constraints

Storage Requirements

High Cardinality: Support millions of unique label combinations
Compression: Achieve 10:1 compression ratio for time-series data
Fast Writes: Optimize for high write throughput
Fast Reads: Optimize for time-range queries
Retention Policies: Automatic data lifecycle management
Backup and Recovery: Regular backups with point-in-time recovery

Query Performance

Time-range Queries: Efficient queries across time ranges
Aggregations: Fast sum, avg, min, max, percentile calculations
Filtering: Quick filtering by labels and values
Joins: Support metric correlation across services
Downsampling: Automatic downsampling for long time ranges
Caching: Query result caching for frequently accessed data

Network Efficiency

Compression: Compress metric data in transit
Batching: Batch metric collection and storage
Efficient Protocols: Use efficient serialization (Protocol Buffers)
Connection Pooling: Reuse connections for scraping
Rate Limiting: Prevent overwhelming monitored services
Backpressure: Handle slow consumers gracefully

Edge Cases and Constraints

High Cardinality Issues

Label Explosion: Too many unique label combinations
Memory Pressure: High cardinality causes memory issues
Query Performance: Slow queries on high-cardinality metrics
Storage Growth: Exponential storage growth
Mitigation: Label limits, cardinality monitoring, metric relabeling

Missing Data

Scrape Failures: Target unavailable or timing out
Network Issues: Packet loss or network partitions
Service Restarts: Metrics reset during restarts
Clock Skew: Timestamps out of order
Handling: Gap detection, interpolation, staleness markers

Alert Fatigue

Too Many Alerts: Alert storms during incidents
False Positives: Alerts firing incorrectly
Duplicate Alerts: Same issue triggering multiple alerts
Mitigation: Alert grouping, rate limiting, smart routing

Resource Constraints

Memory Limits: Limited memory for in-memory data
Disk I/O: Storage bottlenecks during high load
CPU Usage: Query processing consuming too much CPU
Network Bandwidth: Metric collection saturating network
Mitigation: Resource limits, throttling, horizontal scaling

Success Metrics

System Performance

Ingestion Latency: p95 <100ms, p99 <500ms
Query Latency: p95 <1s, p99 <5s
Scrape Success Rate: >99%
Alert Evaluation Latency: p95 <30s
Dashboard Load Time: p95 <2s
System Uptime: >99.95%

Data Quality

Metric Accuracy: >99.99%
Data Loss Rate: <0.01%
Alert Accuracy: >99% (low false positive rate)
Timestamp Precision: ±100ms
Compression Ratio: >10:1
Storage Efficiency: <1KB per metric per day

User Engagement

Active Users: 10,000 daily active users
Dashboards Created: 50,000 custom dashboards
Alert Rules: 100,000 active alert rules
Query Volume: 1 billion queries per day
Alert Notifications: 1 million per day
API Usage: 100 million API calls per day

Business Metrics

Mean Time to Detection (MTTD): <5 minutes
Mean Time to Resolution (MTTR): <30 minutes
False Positive Rate: <5%
Alert Actionability: >90% of alerts lead to action
Cost per Metric: <$0.001 per metric per month
Infrastructure Cost: <$0.10 per monitored service per month

This problem statement establishes the foundation for designing a comprehensive monitoring and alerting system capable of handling massive scale while providing actionable insights.