Using DeepSeek AI to Implement Task Scheduling System Sharding

Recently, the Chinese AI company DeepSeek attracted attention by surpassing ChatGPT in the U.S. download rankings, even prompting former President Trump to warn that its emergence sounds an alarm for related American industries.

Curious about its capabilities, the author tried to let DeepSeek V3 help implement a sharding feature for a task scheduling system.

1 Task Scheduling System

The scheduling system consists of three core components:

Gateway layer – handles application entry and task submission.

Admin layer – manages tasks, performs sharding, and provides the UI.

Worker layer – executes the scheduled tasks and forwards them to the gateway.

Each task is assigned a slot when created; the total number of slots is 1024.

When the number of workers is 4, the slot distribution result is shown in the above diagram.

2 DeepSeek Process

The author first wrote a simple pseudocode, which is shown below:

After submitting this code to DeepSeek, the author was surprised to see that DeepSeek displayed its thinking process—a feature not observed in ChatGPT or other models.

Subsequently, DeepSeek provided a detailed analysis, suggested fixes, and delivered the final code.

The author was impressed by the logic and rigor of the output. To ensure balanced sharding, DeepSeek dynamically handled the remainder by using remainder to distribute the leftover slots to the first few workers, guaranteeing that slots 0‑1023 are fully covered.

Finally, DeepSeek also generated a simple test case, showing a thoughtful touch.