How DeepSeek AI Supercharged My Task Scheduler Sharding Design
The author explores using DeepSeek V3 to design and implement a sharding mechanism for a three‑component task scheduling system, showcases the AI's step‑by‑step reasoning, code generation, and testing, and compares its output with that of Tongyi Qianwen, highlighting DeepSeek's superior programming assistance.
DeepSeek Gains Spotlight
Recently, Chinese AI company DeepSeek became popular, surpassing ChatGPT in the US download rankings and prompting remarks from US President Trump.
Task Scheduling System
The system consists of three core components:
Gateway layer: handles application access and task submission.
Admin layer: manages tasks, performs sharding, and provides the UI.
Worker layer: schedules tasks and triggers them to the gateway.
Each task is assigned a slot when created, with a total of 1024 slots. When there are four workers, the slot distribution is illustrated below.
DeepSeek Process
The author first wrote a simple pseudocode, which is shown in the following image.
After submitting this code to DeepSeek, the model displayed its thinking process—a feature not seen in ChatGPT or Tongyi Qianwen.
DeepSeek then provided a detailed analysis, suggested fixes, and produced the final code. It handled the remainder of slot division using the remainder variable, assigning any leftover slots to the first few workers to ensure full coverage of slots 0‑1023.
Finally, DeepSeek generated a simple test case, demonstrating thoughtful completeness.
Comparison with Tongyi Qianwen
When the author tried Tongyi Qianwen on the same pseudocode, the model produced a simple polished version that felt rather bland. In contrast, DeepSeek’s reasoning was more creative and far exceeded expectations in the programming domain.
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Su San Talks Tech
Su San, former staff at several leading tech companies, is a top creator on Juejin and a premium creator on CSDN, and runs the free coding practice site www.susan.net.cn.
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