The hottest Rust Substack posts right now

And their main takeaways
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What I Learned This Week #4

Eventually Consistent • 79 implied HN points • 16 Jun 24
🕹 Technology Data Storage Rust
  1. Storage engines are categorized into OLTP and OLAP, optimizing for different access patterns like low latency vs. high throughput respectively.
  2. Data structures meant for in-memory usage need encoding for network or disk storage to ensure platform independence and self-containment.
  3. When writing data to a file system, the OS buffers data in memory for performance, requiring explicit flushing to prevent the risk of data loss in case of system crashes.

Write Cleaner, More Maintainable Rust Code with PhantomData

Aayushya’s Substack • 99 implied HN points • 06 Mar 24
🕹 Technology Coding Software Development Rust Programming
  1. Using PhantomData in Rust can help reduce code duplication by creating a generic struct with common fields and methods.
  2. Marker types like FreeLineQuantityTag and BilledLineQuantityTag can help differentiate between types when refactoring code.
  3. Leveraging advanced Rust features like PhantomData can lead to more maintainable and expressive code in real-world projects.

DSA For The Rest Of Us - Part 1

Data Engineering Central • 157 implied HN points • 13 Mar 23
🕹 Technology Data Engineering Data Structures Software Development Rust Programming
  1. Understanding Data Structures and Algorithms is important for becoming a better engineer, even if you may not use them daily.
  2. Linked Lists are a linear data structure where elements are not stored contiguously in memory but are linked using pointers.
  3. Creating a simple Linked List in Rust involves defining nodes with values and pointers to other nodes, creating a LinkedList to hold these nodes, and then linking them to form a chain.

The One Billion Row Challenge in Rust: Part 1

Art’s Substack • 3 HN points • 12 Jun 24
🕹 Technology Programming Benchmarking Optimization Rust
  1. The One Billion Row Challenge in Rust involves writing a program to analyze temperature measurements from a huge file, requiring specific constraints for station names and temperature values.
  2. The initial naive implementation faced performance challenges due to reading the file line by line, prompting optimizations like skipping UTF-8 validation and using integer values for faster processing.
  3. Despite improvements in subsequent versions, performance was still slower than the reference implementation, calling for further enhancements in the next part of the challenge.
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Efficient, Extensible, Expressive: Typed Tagless Final Interpreters in Rust

Get Code • 2 HN points • 22 Mar 23
🕹 Technology Programming Syntax Rust
  1. Typed Tagless Final Interpreters in Rust provide efficiency, extensibility, and expressiveness.
  2. Domain-specific languages focus on solving specific problems well and can be embedded into a host language like Rust.
  3. In the final style, the host language's type system is leveraged directly, allowing for type-safe operations like formatted string processing.