Huawei Cloud Mrs Migration Planner logo

Huawei Cloud Mrs Migration Planner

Organization
huaweicloud
huawei-cloud-mrs-migration-planner

Huawei Cloud MRS migration planning. CDH/EMR migration to MRS, component investigation, cluster type and node spec matching, outputs report and Excel. Investigates existing cluster component usage and resource scale, matches MRS cluster type and node flavors, outputs migration planning report with minimum resource surplus. Applicable to MRS cluster planning, node spec selection, big data cluster migration, CDH/EMR migration, and resource planning scenarios. Trigger words: "MRS planning", "MRS sizing", "MRS cluster planning", "MRS node spec", "CDH migration to MRS", "EMR migration to MRS", "MRS resource planning", "Huawei Cloud MRS", "MapReduce Service", "big data cluster migration", "MRS spec recommendation", "migrate to MRS", "MRS规划", "MRS选型", "MRS集群规划", "MRS节点规格", "CDH迁移MRS", "EMR迁移MRS", "MRS资源规划", "华为云MRS", "大数据集群迁移", "MRS规格推荐", "迁移MRS"

Overview

Publisherhuaweicloud
Repositoryhuaweicloud-skills
Skill namehuawei-cloud-mrs-migration-planner
Stars
59
Forks
24
Bundled files
10
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by huaweicloud on GitHub. Read the source before you install it.

Installation

Install the Huawei Cloud Mrs Migration Planner AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/huaweicloud/huaweicloud-skills/tree/master/skills/bigdata/mrs/huawei-cloud-mrs-migration-planner
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/huaweicloud/huaweicloud-skills.git /tmp/huaweicloud-skills
mkdir -p .claude/skills
cp -r /tmp/huaweicloud-skills/skills/bigdata/mrs/huawei-cloud-mrs-migration-planner .claude/skills/huawei-cloud-mrs-migration-planner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Huawei Cloud Mrs Migration Planner in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Huawei Cloud Mrs Migration Planner on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Huawei Cloud Mrs Migration Planner is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Huawei Cloud MRS Cluster Migration Planning and Sizing

Trigger Words

Chinese triggers: "MRS规划", "MRS选型", "MRS集群规划", "MRS节点规格", "CDH迁移MRS", "EMR迁移MRS", "MRS资源规划", "华为云MRS", "MapReduce Service", "大数据集群迁移", "MRS规格推荐", "迁移MRS"

English triggers: "MRS planning", "MRS sizing", "MRS cluster planning", "MRS node spec", "CDH migration to MRS", "EMR migration to MRS", "MRS resource planning", "Huawei Cloud MRS", "MapReduce Service", "big data cluster migration", "MRS spec recommendation", "migrate to MRS"

Overview

For scenarios migrating big data clusters such as CDH / Tencent EMR / Alibaba EMR to Huawei Cloud MRS, this skill investigates existing cluster component usage and resource scale, matches MRS cluster type and node flavors, and outputs a migration planning report with minimum resource surplus. Functional boundary: component investigation -> cluster type matching -> node spec planning -> storage & disk planning -> report output -> Excel generation.

Prerequisites

  • Python3 + openpyxl (pip install openpyxl) — for generating multi-sheet Excel report

Workflow

PhaseContentOutput
Phase 1Component investigation: collect existing big data component usage, workload characteristics, Master sizing infoComponent list + workload profile
Phase 2Cluster type matching: select cluster type based on components, MRS version, dependency resolutionCluster type + component dependencies
Phase 3Node spec planning: Master sizing (industry NameNode heap standards), Core/Task single-plan selection (minimum surplus)Single-plan flavor + data disk config
Phase 4Storage & disk planning: system disk, data disk, OBS capacity calculationDisk config + OBS plan
Phase 5Report output: structured Markdown report with flavor recommendation, resource comparison, decision rationaleMarkdown report
Phase 6Excel report generation: 4-sheet multi-dimensional report (single plan)MRS_Cluster_Sizing_Report.xlsx

Phase 1: Component Investigation

Collect existing big data component usage, workload characteristics, and Master sizing information. There are 18 key decision questions, including data volume, daily incremental data, data retention period, storage mode, elastic Task nodes, off-peak daily load (asked only when elastic Task is enabled; used to size Core nodes), elastic Task runtime hours, peak buffer confirmation, Master sizing info (HDFS total file count, HBase deployment, YARN concurrent container count), and migration scenario Master node utilization check (Q14, triggered only in migration scenarios, verifies whether the MRS planned Master spec is sufficient).

When elastic Task nodes are enabled, the off-peak (non-peak) daily load MUST be explicitly asked of the user, and Core nodes are sized from this daily load. Peak demand is covered by elastic Task nodes.

Full data volume = existing data + daily incremental data × retention period. All sizing (cores/memory/storage) is calculated on this full data volume, not on the incremental portion alone.

Component mapping table and cluster type matching rules: references/component-mapping.md

Phase 2: Cluster Type and Version Matching

Select cluster type (Analysis/Streaming/Hybrid/Custom) and MRS version based on component usage. Default recommendation: MRS 3.3.0-LTS.

Detailed cluster type selection logic: references/component-mapping.md

Phase 3: Node Spec Planning

  • Master sizing: Based on industry NameNode heap memory standards (Cloudera: 1GB/M blocks; Aliyun: (Files_M+Blocks_M)×512MB). Master memory >= 64GB (MRS 3.x hard constraint). 7-step algorithm includes migration scenario utilization check (Step 7).
  • Core/Task single-plan selection: All Core nodes use the same flavor (uniform), minimum surplus principle (Total_excess = CPU_excess% + Mem_excess% minimized). Data disk is planned flexibly: per-node shuffle requirement = vCPU × 40 GB, disk size 600 GB minimum and never above 800 GB; use the fewest disks that fit (e.g. 640 GB -> 1×640 GB, 1400 GB -> 2×700 GB, 2400 GB -> 4×600 GB).
  • Flavor selection: Master and Core can use different series. Master selected by memory requirement, Core selected by workload (Spark/Flink -> aC9, HBase -> m6, Kafka -> i3, cost-sensitive -> s7). Core nodes must be uniform.

Master 7-step algorithm: references/master-sizing-algorithm.md Core/Task single-plan algorithm: references/core-task-single-plan-algorithm.md ECS flavor catalog and series selection matrix: references/ecs-flavor-catalog.md

Phase 4: Storage and Disk Planning

System disk 480GB High IO, Master data disk 1×600GB General SSD, Core/Task data disk calculated by formula. OBS storage-compute separation: OBS capacity = actual required storage (single-replica); total shuffle = per-node disk total × node count.

Full disk config and OBS capacity calculation: references/storage-disk-planning.md

Phase 5: Report Output

Generate structured Markdown report with 5 sections: component investigation summary, cluster configuration, node specs and recommendation rationale, original cluster vs MRS resource comparison, recommendation decision summary.

Report template: references/excel-report-template.md

Phase 6: Excel Report Generation

Agent writes Phase 1-4 investigation results (source cluster info, single plan, recommendation reason, OBS storage, decisions) to report.json, then generates 4-sheet Excel report: cluster spec recommendation, resource comparison, selection decision rationale, OBS storage spec.

For migration scenarios, source_cluster MUST also carry the Q14 inputs (node_count, master_node_count, master_node_memory_gb, master_node_vcpu, master_mem_utilization_pct, master_cpu_utilization_pct) so the script can render the Sheet 1 "Q14 Check (Master utilization)" column objectively instead of relying on prose in the decisions table.

Full sheet column definitions: references/excel-report-template.md Python script implementation: scripts/generate_mrs_report.py

Output Rules

  • Single-plan output: Output only 1 final plan (minimum surplus); no dual-plan comparison; no user selection needed
  • Minimum surplus selection: For each candidate flavor calculate N = max(ceil(V/flavor_vCPU), ceil(M/flavor_memory)), Total_excess = CPU_excess% + Mem_excess%, select the minimum. On tie, select fewer nodes
  • Migration scenario Master utilization check (Q14): Migration scenarios must ask current Master node CPU/memory utilization. When MRS planned total memory < original cluster actual usage (total × utilization), must annotate "memory severely insufficient" and upgrade flavor. The conclusion MUST be recorded in Sheet 1 column O "Q14 Check (Master utilization)" on the Master row (rendered by the script from the source_cluster Q14 inputs) — not only as prose inside the decisions table
  • Core node uniform constraint: All Core nodes must use the same flavor; heterogeneous deployment not allowed
  • Shuffle disk config: Excel report must list Shuffle disk details: per-node shuffle requirement = vCPU × 40 GB, single disk 600-800 GB (flexible: fewest disks that fit), disk count = the planned flexible count
  • Shuffle total: Total shuffle = per-node data disk total (disk size × disk count) × node count
  • OBS capacity: OBS storage size = actual required storage (single-replica), no package tier rounding
  • Full data volume basis: All sizing (cores, memory, storage) is based on full data volume = existing data + daily incremental data × retention period
  • Resource comparison table: Report must include original cluster vs MRS recommended config comparison table, with difference values and growth percentages
  • Recommendation decision rationale: Each node flavor and decision point must include clear reasoning text
  • Functional boundary: MRS cluster full-process planning (component investigation -> spec selection -> Excel report); excludes deployment/operations, performance tuning, data migration implementation, price estimation

Core Commands

Report Generation

Agent writes Phase 1-4 investigation results to report.json, then generates Excel:

bash
python scripts/generate_mrs_report.py --input report.json

If no --input provided, runs built-in demo data (1480 cores CDH migration):

bash
python scripts/generate_mrs_report.py

Optional --output to specify output path:

bash
python scripts/generate_mrs_report.py --input report.json --output MRS_Report.xlsx

File naming: MRS_Cluster_Sizing_Report_<YYYYMMDD>.xlsx

Parameter Confirmation

The following parameters must be confirmed with the user before entering spec selection:

ParameterRequired/OptionalDescriptionDefault
Data volumeRequiredExisting data volume (GB/TB); basis for storage and Master sizingN/A
Daily incremental dataRequiredDaily incremental data volume (GB/day); full data volume = existing data + daily incremental × retention periodN/A
Data retention periodRequiredData retention period (days); full data volume = existing data + daily incremental × retention daysN/A
Storage modeRequiredHDFS / OBS storage-compute separation / MixedN/A
Elastic Task nodesRequiredWhether elastic Task nodes are needed; peak/off-peak scalingNo
Off-peak daily loadConditionalNon-peak daily workload level (% of original cluster resources); used to size Core nodes; peak demand is covered by elastic Task nodes; only asked when elastic Task enabledN/A
Task runtime hoursConditionalElastic Task node daily runtime hours for on-demand billing cost estimation; only asked when elastic Task enabled8h/day
Peak buffer confirmationConditionalOnly when user confirms "peak resources continuously saturated (>90%)" add 20% bufferNo buffer
HDFS total file countRequiredFor NameNode heap calculation (Master sizing)N/A
HBase deploymentRequiredWhether HBase is deployed; affects Master flavor selectionNo
YARN concurrent container countRequiredFor ResourceManager heap estimationN/A
Migration scenario Master utilization (Q14)ConditionalMigration scenarios must ask current Master node CPU/memory utilization to verify MRS planned Master spec sufficiencyN/A

Notes

  1. MRS 3.x Master nodes must have >= 64 GB RAM — hard requirement. Master node count: original cluster < 100 cores -> 3 nodes; >= 100 cores -> 5-11 nodes; > 500 Core nodes -> 9-11 nodes
  2. Master vCPU selection rule (by Core node count): <= 20 -> 16 cores, 20-50 -> 32 cores, > 50 -> 64 cores. OBS mode minimum 128GB to ensure ResourceManager scheduling
  3. Default ECS series: aC9 (General Computing-plus, QingTian 1.0). Master and Core can use different series, but Core nodes must be uniform
  4. Data disk planning (flexible): per-node shuffle requirement = vCPU × 40 GB; single disk between 600 GB and 800 GB; use the fewest disks that fit (640 GB -> 1×640 GB, 1400 GB -> 2×700 GB, 2400 GB -> 4×600 GB). No fixed minimum of 4 disks
  5. Elastic Task algorithm (total buffer method): Ask the user for the off-peak daily load L% first; Total = original × (1 + buffer%); Core = original × L% (off-peak daily load, no buffer); Task = Total - Core (includes all buffer). Core is sized for the non-peak daily load, and peak demand is covered by elastic Task nodes
  6. OBS storage-compute separation: NameNode heap requirement reduced 50-70%. OBS capacity = actual required storage (single-replica); no package tier rounding
  7. Shuffle total: Total shuffle = per-node disk total (disk size × disk count) × node count
  8. Buffer determination: Only when user confirms "peak resources continuously saturated (>90%)" add 20% buffer; buffer applied to total resources then split into Core and Task
  9. Kerberos does not affect resource spec; only affects Master node count and cluster security architecture. Production/finance scenarios recommend enabling
  10. ZooKeeper is automatically included in all cluster plans. DBService is auto-configured as a dependency
  11. Excel report (Phase 6): Must generate 4-sheet Excel file. generate_mrs_report.py _enrich_node_detail() auto-parses vCPU from flavor and calculates disk count and capacity

References

DocumentDescription
Component MappingComponent mapping table and cluster type matching rules
ECS Flavor CatalogECS flavor catalog and series selection matrix
Master Sizing AlgorithmMaster 7-step sizing algorithm
Single Plan AlgorithmCore/Task single-plan selection algorithm
Storage Disk PlanningStorage and disk planning
Sizing ScenariosCommon spec selection scenarios
Excel Report TemplateExcel report template and data structure
IAM PoliciesIAM permission requirements (no API permissions needed for this planning skill)
Verification MethodsOutput verification and validation methods

Code Structure

text
scripts/
└── generate_mrs_report.py            # Excel report generation (4 sheets, single plan)
references/
├── component-mapping.md              # Component mapping table and cluster type matching rules
├── core-task-single-plan-algorithm.md # Core/Task single-plan selection algorithm
├── ecs-flavor-catalog.md             # ECS flavor catalog and series selection matrix
├── excel-report-template.md          # Excel report template and data structure
├── iam-policies.md                   # IAM permission requirements (no API permissions needed)
├── master-sizing-algorithm.md        # Master 7-step sizing algorithm
├── sizing-scenarios.md               # Common spec selection scenarios
├── storage-disk-planning.md          # Storage and disk planning
└── verification-method.md            # Output verification and validation methods

Verification

  • Execute python scripts/generate_mrs_report.py to generate Excel report; confirm 4 sheets output (cluster spec recommendation / resource comparison / selection decision rationale / OBS storage spec)
  • Verify recommended spec satisfies hard constraints: Master >= 64GB RAM, Core nodes uniform, node count meets HA requirements
  • Verify resource comparison table includes original cluster vs MRS recommended config comparison, with difference values and growth percentages
  • Migration scenario confirms Master utilization check has been executed (Q14) and that Sheet 1 column O "Q14 Check (Master utilization)" is populated on the Master row
  • Verify report.json carries the Q14 inputs (master_node_count, master_node_memory_gb, master_node_vcpu, master_mem_utilization_pct, master_cpu_utilization_pct) in migration scenarios; column O renders "-" when they are absent

Best Practices

  • Migration scenarios must ask Master node CPU/memory utilization; when MRS planned memory < original cluster actual usage, must annotate "memory severely insufficient"
  • When elastic Task is enabled, ask the user for the non-peak (off-peak) daily load; size Core from that daily load and cover peak demand with elastic Task nodes (never silently derive the load)
  • Elastic Task split uses total buffer method: buffer applied to total resources then split into Core and Task; do not add buffer to Core
  • OBS storage-compute separation can reduce NameNode heap requirement 50-70%; preferred for large-capacity scenarios
  • OBS capacity is given as the actual required storage (single-replica); do not round up to a package tier
  • Core/Task shuffle disks are planned flexibly within 600-800 GB per disk; total shuffle = per-node disk total × node count
  • All sizing uses the full data volume (existing data + daily incremental × retention period)
  • Peak buffer only added when user confirms "resources continuously saturated > 90%"; no buffer by default
  • Kerberos does not affect resource spec; only affects Master node count and security architecture

Edge Cases

  • Master memory hard constraint: MRS 3.x Master nodes must have >= 64GB RAM; cannot be lower regardless of cluster size
  • Core node heterogeneous prohibition: All Core nodes must use the same flavor; heterogeneous mixing not allowed. Master and Core can use different series
  • Elastic Task buffer position: Buffer applied to total resources then split into Core and Task; prohibited from adding buffer to Core nodes (common error: Core = total × load% × (1 + buffer%))
  • Off-peak daily load must be asked: When elastic Task is enabled, the non-peak (off-peak) daily load MUST be explicitly asked of the user and used to size Core; do not silently derive it from the task window. Peak demand is covered by elastic Task nodes
  • OBS storage-compute separation NameNode optimization: OBS mode reduces NameNode heap requirement 50-70%; Master memory spec can be appropriately reduced
  • Peak buffer trigger condition: Only when user explicitly confirms "peak resources continuously saturated (>90%)" add 20% buffer; no buffer by default
  • Migration scenario Master utilization check: Migration scenarios must ask original cluster Master node CPU/memory utilization (Q14); when MRS planned total memory < original cluster actual usage, annotate "memory severely insufficient" and upgrade flavor
  • Small cluster Master node count: Original cluster < 100 cores -> 3 Master nodes; >= 100 cores -> 5-11 nodes; > 500 Core nodes -> 9-11 nodes. Prohibit 1480-core cluster selecting 3 Master nodes
  • Flexible shuffle disk: per-node shuffle requirement = vCPU × 40 GB, split into disks of 600-800 GB; 640 GB -> 1×640 GB, 1400 GB -> 2×700 GB, 2400 GB -> 4×600 GB. There is no fixed minimum disk count
  • Shuffle total calculation: Total shuffle = per-node disk total (disk size × disk count) × node count; do not report shuffle total from the raw vCPU × 40 requirement alone
  • OBS capacity: OBS storage size = actual required storage (single-replica); package tiers are not used
  • Data volume basis: Sizing uses the full data volume = existing data + daily incremental data × retention period

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Huawei Cloud Mrs Migration Planner AI skill do?

Huawei Cloud MRS migration planning. CDH/EMR migration to MRS, component investigation, cluster type and node spec matching, outputs report and Excel. Investigates existing cluster component usage and resource scale, matches MRS cluster type and node flavors, outputs migration planning report with minimum resource surplus. Applicable to MRS cluster planning, node spec selection, big data cluster migration, CDH/EMR migration, and resource planning scenarios. Trigger words: "MRS planning", "MRS sizing", "MRS cluster planning", "MRS node spec", "CDH migration to MRS", "EMR migration to MRS", "...

Why use Huawei Cloud Mrs Migration Planner on TypingMind?

Because you install it once and use it with any model. Huawei Cloud Mrs Migration Planner is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Huawei Cloud Mrs Migration Planner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huaweicloud/huaweicloud-skills/tree/master/skills/bigdata/mrs/huawei-cloud-mrs-migration-planner. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Huawei Cloud Mrs Migration Planner?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Huawei Cloud Mrs Migration Planner?

As many as you like. As long as a model supports skills, you can use Huawei Cloud Mrs Migration Planner with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Huawei Cloud Mrs Migration Planner AI skill free?

Yes. It is published on GitHub by huaweicloud under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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