Lecture Notes on 'Designing Data-Intensive Applications'

Introduction

• The lecture focuses on the book 'Designing Data-Intensive Applications' and its core concepts.
• Key focus areas are reliability, scalability, and maintainability in system design.
• Importance of understanding trade-offs when deciding on technologies (e.g., SQL vs NoSQL).
• System design should ensure applications are reliable, scalable, and maintainable.

System Design Principles

Reliability

• A system must perform its intended function consistently.
• Avoid random or incorrect outputs.
• Handle hardware and software errors effectively.

Scalability

• The system should handle increasing loads (e.g., millions of users).
• Design for growth from a single to multiple users.

Maintainability

• Systems should be easy to evolve and maintain over time.
• Avoid “spaghetti code” that makes updates challenging.

Data Storage

• Choosing the right database type: relational vs. document-based (NoSQL).
• Relational databases are suitable for ACID properties and complex joins.
• NoSQL databases offer flexibility and scalability.

Methods of Storing Data

Write Ahead Logs (WAL)

• Track data offsets for faster retrieval.
• Allow crash recovery and fast appends.

SSTables and LSM Trees

• Common in NoSQL databases like Cassandra.
• Efficient for write-heavy applications.

B-Trees

• Used in SQL databases for efficient reads.

Other Storage Options

• Analytic databases for repeated queries.
• Column-based tables for faster analytics.

Encoding and Evolution

• Encoding data for interoperability between different systems and languages.
• Common encoding formats: JSON, XML, and binary.
• Evolution involves maintaining system adaptability over time.

Replication

• Ensures data availability and fault-tolerance.
• Types include leader-follower and multi-leader replication.
• Helps with load balancing and preventing single points of failure.

Partitioning

• Breaks down databases into smaller, more manageable parts.
• Increases query speed by reducing search space.

Transactions

• Preventing race conditions and dirty reads/writes.
• Adherence to ACID properties for transaction reliability.
• Different isolation levels for balancing performance and consistency.

Advanced Topics

Serializability

• Highest level of transaction isolation, ensuring strict consistency.
• Techniques like two-phase locking and version control.

Challenges in Distributed Systems

• Managing replication and partitioning at scale.
• Handling network delays and inconsistencies.

Conclusion

• Understanding system design principles is crucial for developing robust applications.
• Focus on scalability as a core challenge in building data-intensive systems.
• Importance of practical learning through mock interviews and real-world application of theories.