Trade-offs & Alternatives

📖 5 min read 📄 Part 7 of 10

Web Analytics Tool - Tradeoffs and Alternatives

Architecture Tradeoffs

Lambda vs Kappa Architecture

Lambda (Batch + Stream)

  • Pros: Accurate historical data, reprocessing capability
  • Cons: Complex dual pipeline, data consistency challenges
  • Use when: Need both real-time and accurate batch processing

Kappa (Stream Only)

  • Pros: Simpler architecture, single pipeline
  • Cons: Harder to reprocess, requires stream replay
  • Use when: Real-time is primary, historical accuracy less critical

Decision: Lambda for accuracy and flexibility

Sampling vs Full Data Collection

Full Collection

  • Pros: 100% accuracy, no data loss
  • Cons: Higher costs, more storage
  • Use when: Small to medium traffic (<1M events/day)

Sampling (10% rate)

  • Pros: 90% cost reduction, faster queries
  • Cons: Statistical accuracy only, missing edge cases
  • Use when: Very high traffic (>100M events/day)

Decision: Adaptive sampling based on traffic volume

Database Selection Tradeoffs

ClickHouse vs TimescaleDB

ClickHouse

  • Pros: Faster queries, better compression, horizontal scaling
  • Cons: Eventual consistency, complex operations
  • Best for: High-volume analytics, read-heavy workloads

TimescaleDB

  • Pros: PostgreSQL compatibility, ACID transactions
  • Cons: Slower at massive scale, vertical scaling limits
  • Best for: Medium scale, need SQL compatibility

Decision: ClickHouse for scale and performance

Druid vs ClickHouse for OLAP

Druid

  • Pros: Sub-second queries, real-time ingestion, better for high-cardinality
  • Cons: More complex, higher resource usage
  • Best for: Real-time dashboards, interactive queries

ClickHouse

  • Pros: Simpler, better compression, good enough performance
  • Cons: Slower for some aggregations
  • Best for: Cost-sensitive, batch-oriented

Decision: Druid for real-time, ClickHouse for historical

Processing Framework Tradeoffs

Flink vs Spark Streaming

Flink

  • Pros: True streaming, lower latency, better state management
  • Cons: Smaller ecosystem, steeper learning curve
  • Best for: Real-time processing (<1s latency)

Spark Streaming

  • Pros: Mature ecosystem, easier to learn, batch + stream
  • Cons: Micro-batching (higher latency), more resource intensive
  • Best for: Near real-time (>5s latency acceptable)

Decision: Flink for real-time, Spark for batch

Kafka vs Kinesis

Kafka

  • Pros: Open source, more control, better performance
  • Cons: Operational overhead, need to manage
  • Best for: High throughput, on-premise or hybrid

Kinesis

  • Pros: Fully managed, easy setup, AWS integration
  • Cons: Higher cost, vendor lock-in, throughput limits
  • Best for: AWS-native, want managed service

Decision: Kafka for control and cost at scale

Consistency Tradeoffs

Strong vs Eventual Consistency

Strong Consistency

  • Pros: Always accurate, no stale data
  • Cons: Higher latency, lower availability
  • Use for: Financial data, critical metrics

Eventual Consistency

  • Pros: Lower latency, higher availability
  • Cons: Temporary inconsistencies, complex conflict resolution
  • Use for: Page views, non-critical metrics

Decision: Eventual for most metrics, strong for revenue

Real-time vs Batch Accuracy

Real-time (5s latency)

  • Pros: Immediate insights, live dashboards
  • Cons: Approximate counts, may miss late events
  • Use for: Active users, current traffic

Batch (hourly/daily)

  • Pros: 100% accurate, complete data
  • Cons: Delayed insights, not actionable immediately
  • Use for: Historical reports, billing

Decision: Both - real-time for monitoring, batch for accuracy

Cost vs Performance Tradeoffs

Hot vs Cold Storage

All Hot (SSD)

  • Pros: Fast queries on all data
  • Cons: 10x cost, unnecessary for old data
  • Use when: Need instant access to all historical data

Tiered (Hot/Warm/Cold)

  • Pros: 70% cost savings, good enough performance
  • Cons: Slower queries on old data, complexity
  • Use when: Most queries on recent data

Decision: Tiered storage with 7-day hot tier

Replication Factor

3x Replication

  • Pros: High availability, fast failover
  • Cons: 3x storage cost
  • Use for: Critical data, hot tier

2x Replication + S3 Backup

  • Pros: Lower cost, still durable
  • Cons: Slower recovery
  • Use for: Warm/cold tiers

Decision: 3x for hot, 2x+S3 for warm/cold

Privacy vs Functionality Tradeoffs

IP Anonymization

Full IP Storage

  • Pros: Accurate geo-location, fraud detection
  • Cons: Privacy concerns, GDPR issues
  • Use when: Explicit consent, business need

IP Anonymization (last octet)

  • Pros: GDPR compliant, still useful geo data
  • Cons: Less accurate, can't track individuals
  • Use when: Privacy-first, EU users

Decision: Configurable per website, default anonymized

Cookie Lifetime

Long-lived (2 years)

  • Pros: Better user tracking, accurate retention
  • Cons: Privacy concerns, browser restrictions
  • Use when: Authenticated users, explicit consent

Short-lived (7 days)

  • Pros: Privacy-friendly, browser compatible
  • Cons: Undercount returning users
  • Use when: Anonymous tracking, privacy-first

Decision: 90 days default, configurable

Alternative Approaches

Self-hosted vs SaaS

Self-hosted

  • Pros: Full control, data ownership, customizable
  • Cons: Operational burden, need expertise
  • Best for: Large companies, specific requirements

SaaS (Google Analytics)

  • Pros: Easy setup, no maintenance, proven
  • Cons: Vendor lock-in, limited customization, data sharing
  • Best for: Small companies, standard needs

Client-side vs Server-side Tracking

Client-side (JavaScript)

  • Pros: Easy implementation, rich context
  • Cons: Ad blockers, privacy concerns, inaccurate
  • Best for: Public websites, user behavior

Server-side (API)

  • Pros: Accurate, no ad blockers, secure
  • Cons: Missing client context, more complex
  • Best for: Transactions, authenticated actions

Decision: Hybrid - client for behavior, server for critical events

These tradeoffs guide architectural decisions based on specific requirements, scale, and constraints.