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dhis2-query-optimization

Chunk large DHIS2 analytics queries by org units, periods, or data elements.

Updated Jan 20, 2026

Search Tags:#scalability #api #data-analytics #query-optimization #chunking #retry #dhis2

Ready to run

npx skills add https://github.com/BLSQ/mcp_servers --skill dhis2-query-optimization

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dhis2-query-optimization
Source: https://github.com/BLSQ/mcp_servers/tree/main/.claude/skills/dhis2-query-optimization
Command: npx skills add https://github.com/BLSQ/mcp_servers --skill dhis2-query-optimization

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps teams avoid failed DHIS2 queries by chunking requests and estimating complexity.

Core Features & Use Cases

Adaptive chunking: split large analytics requests by org units, periods, or data elements to stay within server limits.
URL length handling and timeout resilience: automatically detect long URLs and apply safe fallbacks and retries.
Practical workflows: use when querying large datasets, using children=True, or when queries fail with timeouts.

Quick Start

Configure your Python DHIS2 client and run an adaptive analytics example with a large set of org units, periods, and data elements to observe dynamic chunking.

Frequently Asked Questions about dhis2-query-optimization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I stop large DHIS2 analytics queries from timing out?▼

Avoid DHIS2 query timeouts by splitting large analytics requests into smaller chunks based on org units, periods, or data elements. This adaptive chunking keeps payload sizes within server limits and adds retry logic for resilience.

What causes DHIS2 API requests to fail when fetching huge datasets?▼

DHIS2 API requests fail when fetching huge datasets because excessive URL lengths and massive payload sizes overwhelm the server. Estimating query complexity and applying safe fallbacks prevents these large request failures.

How do I implement query chunking for DHIS2 data elements and periods?▼

Implement query chunking by dividing your DHIS2 requests across data elements, periods, or org units using deterministic patterns. Configure a Python DHIS2 client to dynamically split the workload and apply retry logic.

Can I optimize DHIS2 queries with children=True without hitting URL length limits?▼

Yes, you can optimize DHIS2 queries with children=True by automatically detecting URL length limits and applying safe fallbacks. Splitting the org unit hierarchy requests into chunks prevents URL overflow errors.

Do I need a Python environment to use adaptive query planning for DHIS2?▼

Yes, you need a Python DHIS2 client environment to use this adaptive query planning. The deterministic chunking strategies, URL length checks, and retry logic rely on Python to execute safely.

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