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?
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10 Coding Principles Explained in 5 Minutes
My Favorite 10 Books for Software Developers
General Advice
The Pragmatic Programmer by Andrew Hunt and David Thomas
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
Clean Code by Robert C. Martin
Refactoring by Martin Fowler
Software Architecture
Designing Data-Intensive Applications by Martin Kleppmann
System Design Interview (our own book :))
Design Patterns
Design Patterns by Eric Gamma and Others
Domain-Driven Design by Eric Evans
Data Structures and Algorithms
Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein
Cracking the Coding Interview by Gayle Laakmann McDowell
Over to you: What is your favorite book?
25 Papers That Completely Transformed the Computer World
Google File System: Insights into a highly scalable file system
Scaling Memcached at Facebook: A look at the complexities of Caching
BigTable: The design principles behind a distributed storage system
Cassandra: A look at the design and architecture of a distributed NoSQL database
Attention Is All You Need: Into a new deep learning architecture known as the transformer
Kafka: Internals of the distributed messaging platform
FoundationDB: A look at how a distributed database
Amazon Aurora: To learn how Amazon provides high-availability and performance
Spanner: Design and architecture of Google’s globally distributed databas
MapReduce: A detailed look at how MapReduce enables parallel processing of massive volumes of data
Shard Manager: Understanding the generic shard management framework
Dapper: Insights into Google’s distributed systems tracing infrastructure
Flink: A detailed look at the unified architecture of stream and batch processing
Zanzibar: A look at the design, implementation and deployment of a global system for managing access control lists at Google
Monarch: Architecture of Google’s in-memory time series database
Thrift: Explore the design choices behind Facebook’s code-generation tool
Bitcoin: The ground-breaking introduction to the peer-to-peer electronic cash system
WTF - Who to Follow Service at Twitter: Twitter’s (now X) user recommendation system
Raft Consensus Algorithm: To learn about the more understandable consensus algorithm
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?
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?
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.SPONSOR US
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Test-Driven Development by Example, by Kent Beck, has got to be on that list of books.
Studied the Designing Data-Intensive Applications book when I was in uni and I learned A LOT from it. 100/10 would recommend