Being a CI Test in the 80s, 1,000x Faster Postgres Query, & 3 More
Top-5 Reads Shaping the Future of Tech 🖖
Welcome to a week’s worth of tech news. Here’s what’s new:
📜 CI Tests’ History from 80’s to 2024: Learn how CI tests have evolved from manual reviews to automated systems, and potentially AI-driven reviews.
🤦 Hitting the Wall With Rust’s Borrow Checker: Rust's lifetime system ensures references remain valid during their usage, requiring precise management.
💨 Making a Postgres Query 1,000 Times Faster: See how to achieve a 1,000-fold increase in PostgreSQL query performance.Â
📈 I’ve Stopped Using Box Plots. Should You? Traditional box plots are unintuitive and prone to misinterpretation vs. strip plots or distribution heatmaps.
🤧 Your Own Constant Folder In C/C++: Discover how creating one optimizes code performance and accuracy.
From the 80’s to 2024 - How CI Tests Were Invented and Optimized (🔗 Read Paper)
Modern dev teams rigorously test every code change before merging, a practice known as continuous integration tests. This process, essential for maintaining code quality, involves running hundreds of test suites daily and has evolved from slow, manual inspections in the 1980s to today's fast, automated systems.
Key Points
1980s: Manual code reviews dominated, like Michael Fagan’s inspections.
1990s: Specialized testers wrote unit tests, which were infrequent and slow.
Early 2000s: Automated integration testing servers emerged, testing each code change.
2011: Zero-maintenance CI services like CircleCI and Travis CI made frequent testing accessible to smaller teams.
2024: Efforts to maximize the speed of testing code changes are pushing the boundaries with fast computers, parallel tests, caching, and potential AI-driven reviews.
Hitting the Wall With Rust’s Borrow Checker (🔗 Read Paper)
Rust's lifetime system ensures references are valid for the duration they're used, requiring careful lifetime management. This mechanism, while robust, poses challenges for more complex lifetime relationships, particularly in functions.
Key Points
Single Lifetime: Rust allows a single lifetime parameter for simple cases, where one lifetime suffices for all references.
Multiple Lifetimes: For more complex cases, multiple lifetime parameters are used, with bounds to define relationships between them.
Higher-Rank Lifetimes: Rust supports higher-rank lifetimes using for<'a> syntax, although bounds in type signatures require creative workarounds.
Making a Postgres Query 1,000 Times Faster (🔗 Read Paper)
When deploying Mattermost at scale, Elasticsearch integration optimizes search query performance and enhances result precision by indexing all searchable data.
Key Points
Indexing Process: Elasticsearch indexes data to enable rapid retrieval during search queries, ensuring efficient performance.
Query Optimization: Challenges arise during full database indexing, where optimizing queries like GetPostsBatchForIndexing becomes crucial for sustained performance.
Performance Insights: Monitoring tools like Grafana reveal critical database call durations, aiding in pinpointing and resolving bottlenecks for smoother operations.
I’ve Stopped Using Box Plots. Should You? (🔗 Read Paper)
Having taught thousands of workshop participants how to read box plots, Nick Desbarats has come to believe that their traditional design is unnecessarily unintuitive and prone to misinterpretation compared to alternative chart types.
Key Points
Understanding Box Plots: Box plots are often poorly understood, with less than 20% of workshop participants initially grasping their interpretation.
Cognitive Effort and Chart Types: They require significant cognitive effort to understand compared to simpler and more informative chart types like strip plots or distribution heatmaps.
Misleading Visual Representations: Traditional box plots can mislead by visually suggesting distributions that may not accurately represent the data, unlike more accurate alternatives such as jittered strip plots or distribution heatmaps.
Your Own Constant Folder In C/C++ (🔗 Read Paper)
In discussing the usage of _mm_sqrt_ps in code, particularly under Clang's behavior with -ffast-math, it was found that Clang could mishandle the intrinsic. This led to exploring solutions involving inline assembly and custom constant folding to optimize performance and accuracy.
Key Points
Intrinsic Quirk with Clang: Clang's -ffast-math setting alters the behavior of _mm_sqrt_ps, necessitating caution due to potential precision issues with rsqrtps.
Optimization with Inline Assembly: Using inline assembly ensures deterministic instruction selection, avoiding compiler optimizations that may compromise performance.
Custom Constant Folding: Implementing custom constant folding using __builtin_constant_p offers a workaround to ensure optimal performance and avoid unnecessary instructions in inline contexts
🎬 And that's a wrap! Stick around & you’ll never miss a beat of the most essential news and discoveries across the tech ecosystem.