11h ago · 3 min read · Why controlling traffic matters more than handling it In Part 1, we saw how systems collapse under pressure.In Part 2 and 3, we looked at caching and database bottlenecks. But there is one concept that directly controls pressure: Rate limiting. Most ...
Join discussion13h ago · 5 min read · How to Profile and Speed Up Any Python Pipeline by 10x Meta description: Optimize your Python pipelines with profiling and performance tweaks, achieving up to 10x speed improvements. Tags: Python, optimization, profiling, performance, pipelines Estim...
Join discussion17h ago · 4 min read · PostgreSQL Covering Indexes: Eliminate Heap Fetches with INCLUDE I was profiling a dashboard that loaded in 3 seconds. The main query filtered by customer_id and selected customer_name, email, and last_order_date. PostgreSQL found 2,000 matching rows...
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18h ago · 5 min read · ChatGPT for Performance Optimization: Prompts That Find the Bottleneck Performance problems are humbling. You've got a feature that works fine in development, but under real load it falls apart. The query that ran in 200ms on your laptop takes 8 seco...
Join discussion1d ago · 6 min read · Last week I migrated a client's WordPress site off shared hosting onto a $6/month VPS. The before-and-after was genuinely embarrassing. We're talking TTFB dropping from 2.8 seconds to 180 milliseconds. Same code. Same database. Same content. The only...
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1d ago · 32 min read · TLDR: A Spark shuffle is the most expensive operation in any distributed job — it moves every matching key across the network, writes temporary sorted files to disk, and forces a hard synchronization barrier between every upstream and downstream stag...
Join discussion1d ago · 27 min read · TLDR: Spark's partition count and partitioning strategy are the two levers that determine whether a job scales linearly or crumbles under data growth. HashPartitioner distributes keys by hash modulo — fast and uniform for well-distributed keys, catas...
Join discussion1d ago · 25 min read · TLDR: Calling cache() or persist() does not immediately store anything — Spark caches lazily at the first action, partition by partition, managed by a per-executor BlockManager. When memory fills up, LRU eviction silently drops or spills partitions. ...
Join discussion1d ago · 26 min read · 📖 The 45-Minute Join Stage That Became 90 Seconds A data engineering team at a retail company was running a nightly Spark job that joined their 500 GB transaction fact table against a 50 MB product dimension table. The job had been in production for...
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