Plan a minimum-spec Huawei Cloud DWS cluster with calculated compute, storage, cache, and scale-out guidance.
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What is Huawei Cloud DWS Sizing Planner?
This Openclaw Skills planner turns greenfield or migration requirements into a practical Huawei Cloud Data Warehouse Service (DWS) configuration. It evaluates coupled and decoupled storage-compute architectures, derives CPU and memory needs, checks dedicated DWS flavors, and calculates storage or cache capacity against architecture-specific limits.
The skill produces one minimum-spec plan that meets the stated constraints, explains the selection rationale, and generates a structured Markdown report and five-sheet Excel workbook. Optional daily growth and retention inputs inform storage reserve and scale-out timing without inflating the initial node specification.
Huawei Cloud DWS Sizing Planner Use Cases
- Size a new Huawei Cloud DWS cluster from warehouse data volume, workload, query patterns, and target region.
- Plan migration to DWS using competitor CPU and memory details or source-cluster survey inputs.
- Compare coupled storage-compute with decoupled storage-compute based on capacity, performance, hot-data ratio, or growth needs.
- Select DWS-specific node flavors while enforcing minimum per-node resources and high-availability node counts.
- Estimate storage or cache requirements, including index overhead for workloads with many point queries.
- Forecast growth reserves and scale-out timing when daily incremental data and retention period are known.
- Generate an auditable sizing report for technical reviews and migration planning.
How Huawei Cloud DWS Sizing Planner Works
- Collect the scenario, current warehouse volume, workload and query characteristics, architecture, deployment region, and applicable migration details. Ask for daily incremental data and retention period when growth forecasting is needed.
- Confirm coupled or decoupled architecture. Coupled planning uses a 70% high watermark; decoupled planning uses an 80% high watermark and a cache policy.
- Derive CPU cores from current warehouse volume for greenfield deployments, or match competitor CPU for migration when provided. Apply workload and memory-ratio rules where applicable; daily growth does not change initial CPU or node sizing.
- Check dedicated DWS flavor candidates and hard constraints, including at least 16 vCPU and 64 GB per node and at least three nodes. Select one plan with the minimum compute surplus that satisfies requirements.
- Calculate storage or cache capacity from the selected architecture, hot-data assumptions, high watermark, and any applicable index-space allowance. For decoupled storage, apply the cache policy priority: custom, hot window, then cost- or performance-optimized.
- If both daily incremental data and retention period are available, estimate end-of-period data, growth headroom, and a scale-out point. Keep these recommendations separate from the initial minimum-spec plan.
- Produce the sizing report and five-sheet Excel workbook, then verify the sheet count, resource constraints, and calculation boundaries.
Huawei Cloud DWS Sizing Planner Setup
Prerequisites: Python 3 and the openpyxl package. Make the skill's scripts/ and references/ files available in the skill environment, including the DWS flavor catalog and sizing calculation references.
Install the Python dependency if it is not already available:
pip install openpyxl
To generate the workbook, use the skill's required action=exec mechanism to run the report-generation script. Direct shell execution of scripts is prohibited by the skill's security rule. The documented script is:
python scripts/generate_dws_report.py
The generated workbook uses the filename pattern DWS_Sizing_Report_<YYYYMMDD>.xlsx. Before planning, collect the required scenario, data-volume, workload, query, architecture, and region inputs; gather competitor or source-cluster details for migration scenarios. Missing values may use documented defaults, but the report must annotate them.
Huawei Cloud DWS Sizing Planner Data Schema & Taxonomy
The skill organizes its implementation into a report-generation script and reference documents:
| Path | Purpose |
|---|---|
scripts/generate_dws_report.py |
Generates the Excel report with five sheets. |
references/cpu-sizing-algorithm.md |
CPU derivation for migration and greenfield scenarios. |
references/storage-calculation.md |
Coupled and decoupled capacity, watermarks, and index-space calculations. |
references/ecs-flavor-catalog.md |
Dedicated DWS flavor specifications, including dwsx2, dwsx3, and dwsax3 series. |
references/single-plan-algorithm.md |
Minimum-spec candidate selection and comparison rationale. |
references/excel-report-template.md |
Workbook template and report_data structure. |
references/source-cluster-survey.md |
Migration survey checklist covering five groups and 20 questions. |
| Other reference files | Acceptance criteria, IAM notes, common sizing scenarios, and verification methods. |
The Excel output contains these five sheets:
| Sheet | Contents |
|---|---|
| Config Recommendation Overview | Single-plan summary and detailed specifications. |
| Resource and Storage Calculation | CPU derivation and storage or cache calculation steps. |
| Selection Decision Rationale | Decision points and candidate-flavor comparison. |
| Capacity Growth and Scale-out Recommendations | Growth and estimated scale-out timing; shows N/A if the required growth inputs are absent. |
| Decoupled OBS Spec | OBS capacity calculation. |
Planning inputs include scenario type, total warehouse data, migration CPU and memory details when applicable, workload, query characteristics, architecture, cache policy for decoupled deployments, target region, and optional daily growth and retention. Defaults such as 30% index space or 100% performance-oriented cache must be disclosed when used.
Huawei Cloud DWS Sizing Planner Advanced Features
- Produces one minimum-spec recommendation rather than Plan A/Plan B alternatives, selecting for minimum compute surplus while satisfying hard constraints.
- Supports both coupled and decoupled architectures; architecture changes storage or cache calculations, not the CPU sizing basis.
- Handles migration and greenfield CPU-sizing paths, including competitor matching, documented fallback calculations, and source-cluster utilization survey inputs.
- Supports cost-optimized, performance-optimized, hot-window, and custom hot-data cache policies for decoupled deployments, with explicit precedence rules.
- Adds optional storage-growth reserve and scale-out cadence estimates without increasing the initial node specification.
- Generates a five-sheet Excel report with calculation details, candidate comparisons, capacity-growth guidance, and decoupled OBS sizing.
- Applies DWS-specific flavor and availability checks; general-purpose ECS flavors are not substitutes for dedicated DWS flavors.
- Includes validation guidance for node limits, workbook sheets, CPU derivation, and the separation of daily growth from initial sizing.
SKILL.md
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