华为云 DWS 规格规划器

安装指南

1. ClawHub CLI 从源直接安装技能的最快方式。
   npx clawhub@latest install huawei-cloud-dws-spec-planner
2. 手动安装 将技能文件夹复制到以下位置之一
   全局模式
   ~/.openclaw/skills/
   工作区
   /skills/
   优先级：工作区 > 本地 > 内置
3. 提示词安装 将此提示词复制到 OpenClaw 即可自动安装。
   请帮我使用 Clawhub 安装 huawei-cloud-dws-spec-planner。如果尚未安装 Clawhub，请先安装（npm i -g clawhub）。
   更倾向于下载？
   获取包含原生技能文件的ZIP压缩包。
   下载技能 (.zip)

什么是

此 Openclaw Skills 规划器可将全新部署或迁移需求转化为实用的华为云数据仓库服务（DWS）配置。它会评估存储计算耦合和解耦架构，推导 CPU 与内存需求，检查专属 DWS 规格，并根据架构特定限制计算存储或缓存容量。
该技能会生成一个满足所述约束的最低规格方案，说明选型依据，并生成结构化 Markdown 报告和包含五个工作表的 Excel 工作簿。可选的每日增长量和保留期输入可用于估算存储预留和扩容时间，而不会抬高初始节点规格。

应用场景

根据数仓数据量、工作负载、查询模式和目标区域，为新的华为云 DWS 集群确定规格。
使用竞争产品的 CPU 和内存信息或源集群调研数据，规划迁移到 DWS。
根据容量、性能、热数据比例或增长需求，比较存储计算耦合与解耦架构。
选择 DWS 专属节点规格，同时确保每个节点的最低资源量和高可用节点数。
估算存储或缓存需求，包括大量点查询工作负载的索引开销。
已知每日增量数据和保留期时，预测增长预留和扩容时间。
生成可用于技术评审和迁移规划的可审计规格报告。

工作原理

收集场景、当前数仓容量、工作负载和查询特征、架构、部署区域及适用的迁移详情。如需预测增长，还应询问每日增量数据和保留期。
确认采用耦合或解耦架构。耦合规划使用 70% 的高水位线；解耦规划使用 80% 的高水位线和缓存策略。
全新部署时根据当前数仓容量推导 CPU 核数；如迁移时提供竞争产品的 CPU 信息，则与其匹配。在适用情况下应用工作负载和内存比例规则；每日增长不会改变初始 CPU 或节点规格。
检查专属 DWS 规格候选项和硬性约束，包括每节点至少 16 vCPU 和 64 GB，以及至少三个节点。选择满足要求且计算资源冗余最小的方案。
根据选定架构、热数据假设、高水位线及适用的索引空间预留计算存储或缓存容量。对于解耦存储，按以下优先级应用缓存策略：自定义、热数据窗口，然后是成本优化或性能优化。
如果同时提供每日增量数据和保留期，则估算期末数据量、增长余量和扩容时间点。将这些建议与初始最低规格方案分开。
生成规格报告和包含五个工作表的 Excel 工作簿，然后验证工作表数量、资源约束和计算边界。

配置指南

前置条件：Python 3 和 openpyxl 包。确保技能环境中可访问该技能的 scripts/ 和 references/ 文件，包括 DWS 规格目录和规格计算参考资料。
如果尚未安装 Python 依赖，请安装：
pip install openpyxl
要生成工作簿，请使用该技能规定的 action=exec 机制运行报告生成脚本。根据技能安全规则，禁止直接通过 shell 执行脚本。文档中指定的脚本为：
python scripts/generate_dws_report.py
生成的工作簿采用文件名模式 DWS_Sizing_Report_.xlsx。规划前，请收集必需的场景、数据量、工作负载、查询、架构和区域输入；迁移场景还需收集竞争产品或源集群详情。缺失值可采用文档规定的默认值，但报告必须注明。

数据架构

该技能将实现内容组织为报告生成脚本和参考文档：
路径 用途
scripts/generate_dws_report.py 生成包含五个工作表的 Excel 报告。
references/cpu-sizing-algorithm.md 迁移和全新部署场景的 CPU 推导。
references/storage-calculation.md 耦合与解耦容量、水位线和索引空间计算。
references/ecs-flavor-catalog.md 专属 DWS 规格说明，包括 dwsx2、dwsx3 和 dwsax3 系列。
references/single-plan-algorithm.md 最低规格候选方案选择及比较依据。
references/excel-report-template.md 工作簿模板和 report_data 结构。
references/source-cluster-survey.md 迁移调研清单，涵盖五组共 20 个问题。
其他参考文件 验收标准、IAM 说明、常见规格场景和验证方法。
Excel 输出包含以下五个工作表：
工作表 内容
配置建议概览 单一方案摘要和详细规格。
资源与存储计算 CPU 推导及存储或缓存计算步骤。
选型决策依据 决策要点及候选规格比较。
容量增长与扩容建议 增长和预计扩容时间；缺少必需增长输入时显示 N/A。
解耦 OBS 规格 OBS 容量计算。
规划输入包括场景类型、数仓总数据量、适用时的迁移 CPU 和内存详情、工作负载、查询特征、架构、解耦部署的缓存策略、目标区域，以及可选的每日增长量和保留期。使用默认值（例如 30% 的索引空间或 100% 的性能优化缓存）时必须披露。

高级特性

只生成一个最低规格建议，而非 Plan A/Plan B 备选方案；在满足硬性约束的前提下，选择计算资源冗余最小的方案。
支持耦合和解耦架构；架构会改变存储或缓存计算，但不会改变 CPU 规格的计算依据。
支持迁移和全新部署的 CPU 规格路径，包括竞争产品匹配、文档规定的回退计算和源集群利用率调研输入。
解耦部署支持成本优化、性能优化、热数据窗口和自定义热数据缓存策略，并明确规定优先级。
可选地增加存储增长预留和扩容周期估算，而不提高初始节点规格。
生成包含计算细节、候选方案比较、容量增长建议和解耦 OBS 规格的五工作表 Excel 报告。
执行 DWS 专属规格和可用性检查；通用 ECS 规格不能替代专属 DWS 规格。
提供节点限制、工作簿工作表、CPU 推导，以及每日增长与初始规格相互独立等方面的验证指导。

name: huawei-cloud-dws-spec-planner description: | Huawei Cloud DWS data warehouse configuration planning and sizing. Supports coupled/decoupled architecture, outputs minimum-spec plan and multi-sheet Excel report.
Automatically calculates CPU cores, storage capacity, and generates structured report.
Applicable to DWS cluster planning, node spec selection, data warehouse migration, capacity planning, and scale-out scenarios.
Trigger words: "DWS planning", "DWS sizing", "DWS cluster planning", "DWS node spec", "data warehouse migration to DWS", "DWS scale-out", "DWS capacity planning", "DWS规划", "DWS选型", "DWS集群规划", "DWS节点规格", "数据仓库迁移DWS", "存算一体", "存算分离", "DWS扩容", "DWS容量规划"
tags:

• huawei-cloud
• DWS
• sizing version: "2.5.1"
  license: MIT

Huawei Cloud DWS Data Warehouse Configuration Planning and Sizing

Trigger Words

Chinese triggers: "DWS规划", "DWS选型", "DWS集群规划", "DWS节点规格", "数据仓库迁移DWS", "华为云DWS", "Data Warehouse Service", "DWS sizing", "存算一体", "存算分离", "日增数据量", "DWS扩容", "DWS容量规划"

English triggers: "DWS planning", "DWS sizing", "DWS cluster planning", "DWS node spec", "data warehouse migration to DWS", "Huawei Cloud DWS", "Data Warehouse Service", "coupled storage-compute", "decoupled storage-compute", "daily incremental data", "DWS scale-out", "DWS capacity planning"

Overview

Based on migration or greenfield scenarios, configuration planning is performed for both coupled and decoupled storage-compute architecture modes. The skill outputs one minimum-spec plan that satisfies requirements (minimum compute surplus under constraints), along with storage growth reserve and scale-out cadence recommendations based on daily incremental data. Functional boundary: requirement survey -> architecture mode confirmation -> CPU core calculation -> minimum-spec flavor selection -> hot data storage/cache capacity calculation -> (optional) growth reserve and scale-out cadence -> report output -> Excel generation.

Security rule: Specifications are sourced from Huawei Cloud official document dws_01_00018 (updated 2026-07-31). Scripts must be executed via skill action=exec; direct shell execution is prohibited.

Prerequisites

Python3 + openpyxl (pip install openpyxl)

Workflow

Phase 1: Requirement Survey

Collect the following key information (8 required + 2 optional):

Category Survey Item Description

Scenario type Migration / Greenfield Determines CPU core calculation method
Data volume Total data to warehouse (GB/TB) Basis for CPU core and storage capacity calculation
Daily incremental (optional) Daily average new data (GB/day) Storage growth reserve and scale-out cadence; does not change node spec
Retention period (optional) Retention cycle (months) Growth prediction enabled only when daily incremental is also provided
Competitor info Competitor CPU total, memory ratio Migration scenario only; matching configuration
Source cluster survey (v2.4.0) MPP cluster scale and utilization (5 groups, 20 questions) Calibrate matching cores and hot data criteria when migrating or taking over source workload
Workload Compute-intensive or not Greenfield scenario +30% cores
Query characteristics Many point queries or not Index space +30%~50%
Architecture mode Coupled / Decoupled Determines storage calculation method and high watermark
Cache strategy Cost-optimized / Performance-optimized / Hot window N days / Custom hot data volume Decoupled only (v2.3.0)

Deployment region Target region Flavor availability

Source cluster survey checklist: references/source-cluster-survey.md

Phase 2: Architecture Mode Confirmation
Architecture Applicable Scenario High Watermark Cache Strategy
Coupled =20TB or low hot data ratio / fast growth 80% Cost-optimized 50% / Performance-optimized 100% / Hot window N days / Custom (v2.3.0)

Storage calculation rules: references/storage-calculation.md

Phase 3: CPU Core Calculation
Migration scenario: Total CPU = competitor CPU total (matching configuration); memory ratio follows competitor; when competitor cores not provided, fallback to ceil(data volume/100GB)
Greenfield scenario: Base cores = ceil(total warehouse data/100GB); compute-intensive x1.3 (memory ratio 1:8), otherwise 1:4
Both decoupled and coupled use total warehouse data for CPU derivation (v2.5.1); cache ratio only affects storage/cache capacity allocation, not CPU cores
Constraints: single node >=16vCPU/64GB, node count >=3 (high availability)
Daily incremental data does not participate in CPU/node derivation; only used for Phase 5 growth reserve

Full CPU calculation logic: references/cpu-sizing-algorithm.md

Phase 4: Node Flavor Planning (Single Minimum-Spec Plan)

Iterate through DWS dedicated flavor catalog (dwsx2/dwsx3/dwsax3 series), satisfying hard constraints (>=16 cores/>=64GB/>=3 nodes), select the combination with minimum compute surplus (nodes + storage). Selection rationale is auto-generated via four-step formula chain: (1) compute derivation -> (2) memory ratio -> (3) concurrency/HA -> (4) storage verification.

Flavor catalog: references/ecs-flavor-catalog.md Selection algorithm: references/single-plan-algorithm.md

Phase 5: Storage Capacity Calculation and Growth Reserve

Storage capacity (based on current warehouse volume):

Coupled: Effective capacity = total / high watermark (70%); many point queries x(1+index%); per node = ceil(total/nodes, round up to 100GB)
Decoupled: Hot data calculated by cache policy -> effective cache = hot data / high watermark (80%) -> same as above
Cache policy priority: custom (custom hot data volume) > window (hot window N days) > cost/performance (50%/100%)

Growth reserve (optional, requires both daily incremental + retention period):

Planning period new data = daily incremental x 30 days/month x retention months; end-of-period total = current + new
Scale-out line (90% utilization) capacity -> net growth headroom -> estimated scale-out time point
Growth prediction does not change recommended spec; only outputs scale-out recommendations

Growth calculation and Excel template: references/excel-report-template.md

Phase 6: Report Output

Generate structured Markdown report + 5-sheet Excel report:

Sheet Name Content

1 Config Recommendation Overview Single plan summary + detailed spec 2 Resource and Storage Calculation CPU derivation + storage capacity calculation process 3 Selection Decision Rationale Decision points + candidate flavor comparison table 4 Capacity Growth and Scale-out Recommendations Daily incremental x retention period -> scale-out time point 5 Decoupled OBS Spec OBS capacity calculation

Common scenarios: references/sizing-scenarios.md

Output Rules

Output only one plan (minimum spec satisfying requirements, minimum compute surplus); no Plan A/B comparison
CPU/nodes derived from current warehouse volume; daily incremental does not inflate initial spec (minimum spec philosophy)
Both decoupled and coupled CPU calculated from total warehouse data (v2.5.1); cache ratio only affects storage/cache capacity allocation
Missing info handled with defaults but must be annotated (e.g., index space default +30%, cache strategy default performance-optimized 100%)
Excel report must generate 5 sheets; when daily incremental and retention period not provided, Sheet 4 outputs N/A
Key decision points must be proactively asked: scenario type, data volume, competitor CPU/memory ratio, compute-intensive, point queries, architecture mode, cache strategy, deployment region

Core Commands

Report Generation

python scripts/generate_dws_report.py

File naming: DWS_Sizing_Report_.xlsx

Parameter Confirmation

Parameter Required/Optional Description Default

Scenario type Required Migration / Greenfield; determines CPU core calculation method N/A
Total warehouse data Required GB/TB; basis for CPU core and storage capacity calculation N/A
Competitor CPU total and memory ratio Conditional Migration scenario only; matching configuration; fallback to ceil(data/100GB) when not provided Uses fallback formula when not provided
Source cluster survey Conditional Calibrate matching cores and hot data caliber when migrating N/A
Workload Required Compute-intensive or not; greenfield compute-intensive x1.3 Non-compute-intensive
Query characteristics Required Many point queries or not; index space +30%~50% Default +30% with annotation
Architecture mode Required Coupled / Decoupled; determines storage calculation and high watermark N/A
Cache strategy Conditional Decoupled only; cost-optimized 50% / performance-optimized 100% / hot window N days / custom hot data volume Performance-optimized 100%

Deployment region Required Target region N/A

Daily incremental data Optional Daily new data GB/day; for storage growth reserve and scale-out cadence only; does not change node spec N/A
Data retention period Optional Retention cycle (months); growth prediction enabled only when daily incremental also provided N/A

Notes

Production single node >= 16vCPU and >= 64GB (hard requirement); node count >= 3 (high availability)
DWS != general ECS: Uses dedicated flavor names (dwsx2/dwsx3/dwsax3 etc.); cannot use MRS aC9/ac7
Migration scenario cores: Match competitor cores; no hard >=96 floor; when competitor cores not provided, fallback to ceil(data volume/100GB)
CPU-memory ratio: Migration follows competitor; greenfield non-compute-intensive -> 1:4, compute-intensive -> 1:8
CPU derived from current warehouse volume: Daily incremental does not inflate initial spec; both decoupled and coupled use total warehouse data (v2.5.1); cache ratio does not affect CPU
Coupled high watermark 70% (SSS cloud disk), decoupled high watermark 80% (local cache disk)
Index space: Many point queries +30%~50%; default +30% with annotation
Cache policy (v2.3.0): cost -> total x50%, performance -> total x100% (default), window -> min(daily incremental x N, total), custom -> given value (= 16vCPU and >= 64GB, node count >= 3
Verify decoupled scenario CPU calculated from total warehouse data (same as coupled); cache ratio adjustment does not affect CPU cores
Confirm daily incremental data does not inflate initial spec (only used for Phase 5 growth reserve)

Best Practices

DWS uses dedicated flavor names (dwsx2/dwsx3/dwsax3); cannot use MRS aC9/ac7 general-purpose ECS flavors
Decoupled suitable for >=20TB or low hot data ratio / fast growth scenarios; coupled suitable for window > cost/performance; select based on business hot data characteristics
Source survey distinguishes logical volume vs physical volume with replicas (Doris default 3 replicas); matching cores calculated from CPU utilization
Daily incremental data does not change node spec; only affects scale-out cadence recommendations

Edge Cases

DWS dedicated flavors: DWS uses dedicated PaaS flavor names (dwsx2/dwsx3/dwsax3 etc.); cannot use general-purpose ECS flavors (e.g., MRS aC9/ac7); must confirm target region DWS flavor availability before sizing
Architecture switch: Decoupled suitable for >=20TB or low hot data ratio / fast growth; coupled suitable for hot data volume)
Daily incremental boundary: Daily incremental does not participate in CPU/node derivation; only used for Phase 5 growth reserve and scale-out cadence. When daily incremental + retention period not provided, Sheet 4 outputs N/A
Missing info handling: Missing info handled with defaults but must be annotated (index space default +30%, cache strategy default performance-optimized 100%); silent use of defaults is not allowed
Source survey caliber distinction: Migration scenario must distinguish logical volume vs physical volume with replicas (e.g., Doris default 3 replicas); matching cores calculated from source CPU utilization; memory ratio based on DWS query profile, not copied from source
Cache policy priority conflict: custom > window > cost/performance; when multiple strategies provided, use by priority; explain actual effective strategy to user
Very small data volume: When data =16vCPU hard requirement; actual minimum spec is 16 cores
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