EP111: My Favorite 10 Books for Software Developers

This week’s system design refresher:

• 10 Coding Principles Explained in 5 Minutes (Youtube video)

• My Favorite 10 Books for Software Developers

• 25 Papers That Completely Transformed the Computer World

• Change Data Capture: Key to Leverage Real-time Data

• IPv4 vs. IPv6, what are the differences?

• SPONSOR US

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10 Coding Principles Explained in 5 Minutes

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My Favorite 10 Books for Software Developers

General Advice

1. The Pragmatic Programmer by Andrew Hunt and David Thomas

2. Code Complete by Steve McConnell: Often considered a bible for software developers, this comprehensive book covers all aspects of software development, from design and coding to testing and maintenance.

Coding

1. Clean Code by Robert C. Martin

2. Refactoring by Martin Fowler

Software Architecture

1. Designing Data-Intensive Applications by Martin Kleppmann

2. System Design Interview (our own book :))

Design Patterns

1. Design Patterns by Eric Gamma and Others

2. Domain-Driven Design by Eric Evans

Data Structures and Algorithms

1. Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein

2. Cracking the Coding Interview by Gayle Laakmann McDowell

Over to you: What is your favorite book?

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25 Papers That Completely Transformed the Computer World

1. Dynamo - Amazon’s Highly Available Key Value Store

2. Google File System: Insights into a highly scalable file system

3. Scaling Memcached at Facebook: A look at the complexities of Caching

4. BigTable: The design principles behind a distributed storage system

5. Borg - Large Scale Cluster Management at Google

6. Cassandra: A look at the design and architecture of a distributed NoSQL database

7. Attention Is All You Need: Into a new deep learning architecture known as the transformer

8. Kafka: Internals of the distributed messaging platform

9. FoundationDB: A look at how a distributed database

10. Amazon Aurora: To learn how Amazon provides high-availability and performance

11. Spanner: Design and architecture of Google’s globally distributed databas

12. MapReduce: A detailed look at how MapReduce enables parallel processing of massive volumes of data

13. Shard Manager: Understanding the generic shard management framework

14. Dapper: Insights into Google’s distributed systems tracing infrastructure

15. Flink: A detailed look at the unified architecture of stream and batch processing

16. A Comprehensive Survey on Vector Databases

17. Zanzibar: A look at the design, implementation and deployment of a global system for managing access control lists at Google 

18. Monarch: Architecture of Google’s in-memory time series database 

19. Thrift: Explore the design choices behind Facebook’s code-generation tool

20. Bitcoin: The ground-breaking introduction to the peer-to-peer electronic cash system

21. WTF - Who to Follow Service at Twitter: Twitter’s (now X) user recommendation system

22. MyRocks: LSM-Tree Database Storage Engine

23. GoTo Considered Harmful

24. Raft Consensus Algorithm: To learn about the more understandable consensus algorithm

25. Time Clocks and Ordering of Events: The extremely important paper that explains the concept of time and event ordering in a distributed system 

Over to you: I’m sure we missed many important papers. Which ones do you think should be included?

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Change Data Capture: Key to Leverage Real-time Data

90% of the world’s data was created in the last two years and this growth will only get faster.

However, the biggest challenge is to leverage this data in real-time. Constant data changes make databases, data lakes, and data warehouses out of sync.

CDC or Change Data Capture can help you overcome this challenge.

CDC identifies and captures changes made to the data in a database, allowing you to replicate and sync data across multiple systems.

So, how does Change Data Capture work? Here's a step-by-step breakdown:

1 - Data Modification: A change is made to the data in the source database. It could be an insert, update, or delete operation on a table.

2 - Change Capture: A CDC tool monitors the database transaction logs to capture the modifications. It uses the source connector to connect to the database and read the logs.

3 - Change Processing: The captured changes are processed and transformed into a format suitable for the downstream systems.

4 - Change Propagation: The processed changes are published to a message queue and propagated to the target systems, such as data warehouses, analytics platforms, distributed caches like Redis, and so on.

5 - Real-Time Integration: The CDC tool uses its sink connector to consume the log and update the target systems. The changes are received in real time, allowing for conflict-free data analysis and decision-making.

Users only need to take care of step 1 while all other steps are transparent.

A popular CDC solution uses Debezium with Kafka Connect to stream data changes from the source to target systems using Kafka as the broker. Debezium has connectors for most databases such as MySQL, PostgreSQL, Oracle, etc.

Over to you: have you leveraged CDC in your application before?

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IPv4 vs. IPv6, what are the differences?

The transition from Internet Protocol version 4 (IPv4) to Internet Protocol version 6 (IPv6) is primarily driven by the need for more internet addresses, alongside the desire to streamline certain aspects of network management.

• Format and Length
  IPv4 uses a 32-bit address format, which is typically displayed as four decimal numbers separated by dots (e.g., 192.168.0. 12). The 32-bit format allows for approximately 4.3 billion unique addresses, a number that is rapidly proving insufficient due to the explosion of internet-connected devices.
  
  In contrast, IPv6 utilizes a 128-bit address format, represented by eight groups of four hexadecimal digits separated by colons (e.g., 50B3:F200:0211:AB00:0123:4321:6571:B000). This expansion allows for approximately much more addresses, ensuring the internet's growth can continue unabated.

• Header
  The IPv4 header is more complex and includes fields such as the header length, service type, total length, identification, flags, fragment offset, time to live (TTL), protocol, header checksum, source and destination IP addresses, and options.
  
  IPv6 headers are designed to be simpler and more efficient. The fixed header size is 40 bytes and includes less frequently used fields in optional extension headers. The main fields include version, traffic class, flow label, payload length, next header, hop limit, and source and destination addresses. This simplification helps improve packet processing speeds.

• Translation between IPv4 and IPv6
  As the internet transitions from IPv4 to IPv6, mechanisms to allow these protocols to coexist have become essential:
  
  - Dual Stack: This technique involves running IPv4 and IPv6 simultaneously on the same network devices. It allows seamless communication in both protocols, depending on the destination address availability and compatibility. The dual stack is considered one of the best approaches for the smooth transition from IPv4 to IPv6.

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