This article examines the latest Nvidia GPU lineup—including A100, H100, A800, H800, and the upcoming H20—detailing their architectures, performance metrics for AI training and inference, cost considerations, and provides a step‑by‑step guide for building a high‑performance compute center.
This article examines MySQL slow‑query troubleshooting, explaining how the optimizer estimates costs, why indexes may be ineffective even when present, the impact of memory fragmentation, pitfalls of prefix indexes, index merging techniques, and additional resource‑related factors that can cause seemingly healthy SQL statements to become slow.
This article examines MySQL's cost estimation process, explains why added indexes may still be ineffective, discusses the impact of memory fragmentation, and presents practical techniques such as prefix index handling, index merging, and resource monitoring to improve slow‑query performance.
This article provides a detailed technical analysis of NVIDIA's A100, H100, A800, H800 and H20 GPUs, compares their architectures, performance and cost, and offers step‑by‑step guidance on building a private AI compute center, selecting hardware, software stacks and budgeting for different workloads.
This article analyzes a slow Join query on OceanBase 3.2.3.3, examines execution plans and table statistics, proposes two rewrite methods—adding a filter condition and using a hash‑join hint—and demonstrates how these changes reduce execution time from over 500 seconds to just a few seconds.
This article explains how MySQL estimates the I/O and CPU costs of different indexes when executing COUNT(*) and other queries, demonstrates the calculations with a concrete example on a 100k‑row table, and shows why the optimizer sometimes picks a full‑table scan over a seemingly better index.
The article analyses the computational resources, hardware costs, and human investment needed to train and serve large language models such as GPT‑3, discusses practical cost calculations, highlights the challenges faced by Chinese AI teams, and argues for sustained, long‑term funding to achieve meaningful breakthroughs.
This article examines how MySQL decides whether to use an index or perform a full table scan for COUNT(*) and other queries, explains the IO and CPU cost model, demonstrates the calculations with a sample table, and shows how optimizer trace can reveal mismatches between estimated and actual performance.
This article explains how PostgreSQL generates execution plans, details the plan tree structure, cost parameters, scan and join methods, and provides practical tips for using EXPLAIN, tuning planner settings, and applying hints to optimize SQL performance.
This article explains how to use MySQL's EXPLAIN FORMAT=JSON to view detailed execution plan costs, illustrates table structures, walks through two query examples with cost breakdowns, and clarifies the calculation of read_cost and eval_cost for both simple and join queries.
This article explains the fundamentals of relational optimization, the role of the optimizer, how CPU and I/O costs are estimated, and why accurate database statistics are essential for effective SQL performance tuning.
This article uses a dialogue‑driven story to explain how PostgreSQL gathers statistics, computes selection rates, evaluates various physical scan and join paths, and combines CPU, I/O, and communication costs—including startup versus total cost—to choose the most efficient execution plan.
This article walks through practical steps for identifying performance bottlenecks in PostgreSQL, selecting appropriate columns and index types, interpreting system statistics, and evaluating cost estimates with real‑world examples to dramatically reduce query latency.
This article walks through the full lifecycle of building a modest mobile app in Shenzhen, detailing team composition, office costs, development phases, and a month‑by‑month budget that totals roughly one million RMB for the first version.
This article breaks down the true cost of developing a native mobile app in China, covering developer salaries, team composition, timeline, low‑budget alternatives, template platforms, and when a company should choose a native app versus a Web‑App solution.
