Designing Data-Intensive Applications - 1. Reliable, Scalable, and Maintainable Applications

Table Of Contents

Key Building Blocks of Data-Intensive Applications

Applications often integrate multiple standard building blocks:

• Databases: Store and retrieve data
• Caches: Remember expensive operation results to speed up reads
• Search Indexes: Enable keyword search and data filtering
• Stream Processing: Send messages to be processed asynchronously
• Batch Processing: Periodically process large accumulated datasets

Fundamental Design Principles

1. Reliability

Definition: The system’s ability to continue working correctly despite adversities.

Key Characteristics:

• Fault-tolerant and resilient
• Continues to perform the correct function at the desired performance level
• Tolerates faults rather than attempting to completely avoid them

Types of Faults:

• Hardware Faults:

  • Physical component failures (e.g., disk failures)
  • Statistically predictable (e.g., in a 10,000-disk cluster, expect ~1 disk failure per day)

• Software Errors:

  • Systemic errors like leap second bugs
  • Correlated failures across multiple nodes
  • Often more dangerous than uncorrelated hardware faults

• Human Errors:

  • Mitigated through:
    • Quick recovery mechanisms
    • Gradual code rollouts
    • Detailed monitoring
    • Clear performance and error rate tracking

2. Scalability

Definition: The system’s capacity to handle increased load effectively.

Key Considerations:

• Load Parameters: Metrics describing system load (varies by system architecture)

  • Example: For Twitter, followers per user determines fan-out load

• Performance Measurement:

  • Impact of load increase on system resources
  • Resources needed to maintain consistent performance
  • Focus on response time percentiles, not just mean averages

Scaling Approaches:

• Vertical Scaling (Scale Up): Move to more powerful machines
• Horizontal Scaling (Scale Out): Distribute load across multiple machines

Practical Advice:

• Early-stage startups should prioritize feature iteration over hypothetical future scaling

3. Maintainability

Definition: Ensuring the system remains adaptable and understandable throughout its lifecycle.

Key Principles:

1. Operability:

   • Facilitate smooth operations
   • Preserve organizational knowledge
   • Provide predictable system behavior

2. Simplicity:

   • Remove accidental complexity
   • Create good abstractions
   • Ease understanding for new engineers

3. Evolvability (Extensibility):

   • Enable easy system modifications
   • Adapt to unanticipated use cases
   • Support changing requirements

System Design Philosophy

• No single tool meets all data processing needs
• Break work into efficiently manageable tasks
• Create custom data systems by combining general-purpose components
• Focus on functional and non-functional requirements

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