Problem Statement

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Log Collection and Analysis System - Problem Statement

Overview

Design a centralized logging system similar to ELK Stack (Elasticsearch, Logstash, Kibana) or Splunk that can collect, process, store, and analyze logs from distributed applications. The system should handle billions of log entries daily, provide real-time search capabilities, and enable troubleshooting and security analysis.

Functional Requirements

Core Logging Features

  • Log Collection: Collect logs from applications, servers, containers, cloud services
  • Log Parsing: Parse structured and unstructured logs
  • Log Enrichment: Add metadata (timestamps, hostnames, tags)
  • Log Storage: Store logs efficiently for search and analysis
  • Full-text Search: Search across all log fields with filters
  • Real-time Streaming: View logs in real-time as they arrive

Log Sources

  • Application Logs: Application-generated logs (JSON, plain text)
  • System Logs: OS logs (syslog, Windows Event Log)
  • Container Logs: Docker, Kubernetes logs
  • Web Server Logs: Apache, Nginx access/error logs
  • Database Logs: MySQL, PostgreSQL, MongoDB logs
  • Cloud Service Logs: AWS CloudWatch, Azure Monitor, GCP Logging

Search and Query

  • Full-text Search: Search log messages and fields
  • Field Filtering: Filter by specific fields (level, service, host)
  • Time Range Queries: Query logs within time ranges
  • Aggregations: Count, sum, average, percentiles
  • Pattern Matching: Regex and wildcard searches
  • Saved Searches: Save and share common queries

Analysis and Visualization

  • Log Analytics: Identify patterns, trends, anomalies
  • Dashboards: Create custom dashboards with visualizations
  • Charts: Time-series, bar charts, pie charts, tables
  • Alerting: Alert on log patterns and thresholds
  • Correlation: Correlate logs across services
  • Anomaly Detection: ML-based anomaly detection

Log Management

  • Retention Policies: Configurable retention periods
  • Archival: Archive old logs to cold storage
  • Data Lifecycle: Automatic tier transitions
  • Index Management: Create, delete, optimize indexes
  • Access Control: Role-based access to logs
  • Audit Logging: Track who accessed what logs

Non-Functional Requirements

Performance Requirements

  • Ingestion Rate: Handle 1 million log entries per second
  • Search Latency: <1 second for simple searches, <5 seconds for complex
  • Indexing Latency: Logs searchable within 10 seconds of ingestion
  • Dashboard Load: <3 seconds for dashboard rendering
  • Real-time Tail: <1 second latency for log streaming
  • Aggregation Queries: <10 seconds for complex aggregations

Scalability Requirements

  • Daily Log Volume: 100 billion log entries per day
  • Storage: 500 TB of searchable logs
  • Concurrent Users: 10,000 concurrent users searching logs
  • Log Sources: 100,000+ log sources
  • Indexes: 10,000+ indexes
  • Retention: 90 days hot, 1 year warm, 7 years cold

Reliability Requirements

  • System Uptime: 99.9% availability
  • Data Durability: 99.999% durability
  • No Data Loss: Guaranteed log delivery
  • Disaster Recovery: <4 hours RTO, <1 hour RPO
  • Replication: 3x replication for hot data
  • Backup: Daily backups to object storage

Data Quality

  • Log Parsing Accuracy: 99%+ successful parsing
  • Timestamp Accuracy: Millisecond precision
  • Deduplication: Detect and handle duplicate logs
  • Schema Validation: Validate log structure
  • Data Completeness: Track missing logs
  • Error Handling: Graceful handling of malformed logs

Scale Constraints

Log Volume

Log Sources: 100,000 sources
Logs per Source: 1,000 logs/second average
Total Ingestion: 100 million logs/second peak
Daily Volume: 100 billion logs/day
Average Log Size: 500 bytes
Daily Data: 50 TB/day raw
Compressed: 10 TB/day (5:1 compression)

Storage Requirements

Hot Storage (90 days): 900 TB
Warm Storage (1 year): 3.6 PB
Cold Storage (7 years): 25 PB
Total: 29.5 PB

Query Load

Concurrent Users: 10,000
Queries per User: 10 queries/hour
Query Rate: 27,777 queries/hour = 7.7 queries/sec
Peak Query Rate: 100 queries/sec

Success Metrics

Performance

  • Ingestion latency: p95 <100ms
  • Search latency: p95 <2s
  • Index latency: p95 <10s
  • Query success rate: >99.9%
  • System uptime: >99.9%

Data Quality

  • Parsing success rate: >99%
  • Data loss rate: <0.01%
  • Duplicate rate: <0.1%
  • Schema compliance: >98%

User Engagement

  • Daily active users: 5,000
  • Searches per day: 1 million
  • Dashboards created: 10,000
  • Alerts configured: 50,000

This problem statement establishes the foundation for designing a comprehensive log analysis system.