dsh-maxcompute
Run the following command in DeepSeek Harness:
dsh plugin install dingxin-tech/dsh-maxcompute
Paste the following prompt into your AI chat to install this plugin:
Run dsh plugin install dingxin-tech/dsh-maxcompute in the DeepSeek Harness terminal to install the plugin; full details at https://github.com/dingxin-tech/dsh-maxcompute
About this plugin
Working with MaxCompute (ODPS) inside DSH used to mean hand-typing CLI commands, writing throwaway scripts, or hard-coding table names into your prompts. dsh-maxcompute wraps metadata browsing, SQL execution, async job management, and result export into tools a model can call directly, so you can drive the full pipeline from list_tables through run_sql to job_result in plain language.
Safety and cost control sit at the core of the design. Every SQL statement is dry-run first; if the estimated scan crosses the configured GB threshold the call is refused outright. A monotonic read-only guard denies DDL/DML by default and cannot be re-opened by later listeners. Credentials never touch the plugin layer — they stay inside maxc's own config. Scan-size caps, timeouts, preview row limits, and the readonly flag are all overridable per patch layer, making it easy to tighten permissions in shared environments.
It is aimed at data engineers and analysts who query MaxCompute regularly from within a DSH workflow — anyone who wants an AI assistant to safely inspect schemas, run analytical SQL, and pull async job results without bouncing back and forth to a terminal. The plugin follows a swappable-provider architecture: if you have an internal gateway, you replace a single service module and the tools layer keeps working unchanged.
Use Cases
- Browse table schemas and partition values in a MaxCompute project via natural language
- Run cost-gated analytical SQL and fetch async job results inside an AI conversation
- Enforce a read-only guard and preview-row cap for SQL in a shared DSH workspace
Best For
- Data engineers who query MaxCompute regularly within a DSH workflow
- Analysts who want an AI assistant to interact safely with ODPS tables
- Team leads who need to tighten SQL permissions and scan costs in shared environments
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