masterdata-storage-strategy

name	masterdata-storage-strategy
description	Apply when deciding whether VTEX Master Data is the right storage for a given workload, designing JSON Schemas with v-indexed, v-cache, v-security, and v-triggers, planning entity capacity and lifecycle, or auditing existing Master Data usage. Covers when to use MD versus Catalog, OMS, VBase, or external databases, schema design best practices, indexing strategy, trigger patterns, and operational considerations. Use before creating any new Master Data entity.
metadata	{"track":"masterdata","tags":["masterdata","masterdata-v2","storage","json-schema","v-indexed","v-cache","v-security","v-triggers","indexing","triggers","data-architecture"],"globs":["masterdata/**/*.json","**/dataentities/**"],"version":"1.0","purpose":"Choose the right storage and design Master Data schemas for performance, security, and maintainability","applies_to":["deciding whether Master Data fits a workload","designing JSON Schemas with VTEX extensions","configuring indexing and caching strategies","setting up triggers for automated workflows","auditing and governing existing MD entities","capacity planning for large datasets"],"excludes":["VTEX IO app integration patterns (see vtex-io-masterdata)","IO client usage and CRUD code (see vtex-io-masterdata)"],"decision_scope":["masterdata-vs-catalog-vs-oms-vs-vbase-vs-external","v-indexed-what-to-index","v-cache-on-vs-off","trigger-vs-event-vs-cron"],"vtex_docs_verified":"2026-03-30"}

Master Data storage strategy

When this skill applies

Use this skill before creating any new Master Data entity or when auditing existing usage. It helps you answer:

• Is Master Data the right storage for this data, or would Catalog, OMS, VBase, or an external database serve better?
• How should I design the JSON Schema for performance and security?
• Which fields should I index (v-indexed), and which should I not?
• Should I enable or disable caching (v-cache)?
• Do I need triggers (v-triggers), or is an event-driven IO approach better?
• How do I plan for capacity and lifecycle of schemas and documents?

Do not use this skill for:

• VTEX IO app integration patterns (MasterDataClient, masterdata builder, CRUD in code) — use vtex-io-masterdata
• Performance patterns for IO services (LRU, VBase caching layers) — use vtex-io-application-performance

Decision rules

When to use Master Data

Master Data is a good fit when all of the following are true:

1. Document-oriented access — Your data is naturally key-value or document-shaped (JSON documents with variable schemas). You query by indexed fields and retrieve full or partial documents.
2. Platform-integrated — You benefit from VTEX-native features: v-triggers for automated workflows, v-security for per-field public access, v-indexed for search/filter, and the masterdata builder for schema-as-code.
3. Moderate volume — Your entity will hold thousands to low millions of documents. MD handles this well with proper indexing.
4. Not on the purchase critical path — MD is not optimized for sub-10ms latency. Synchronous MD reads in checkout/cart/payment flows risk conversion if MD is slow.
5. No better native fit — The data doesn't belong in Catalog (product/SKU attributes), OMS (order data), CL/AD (customer profiles/addresses), or VBase (app-specific cache/state).

When NOT to use Master Data

Data type	Better storage	Why
Product attributes, specifications	Catalog (specifications, unstructured specs)	Native indexing, search integration, catalog APIs
Order data, order history	OMS (via OMS APIs + BFF cache)	Single source of truth; duplicating to MD creates drift
Customer profiles, addresses	CL/AD native entities	Platform-managed, already indexed and cached
App-specific cache or temp state	VBase	Designed for per-app ephemeral storage, no schema overhead
Application logs, debug traces	ctx.vtex.logger	Structured logging infrastructure, not a database
High-throughput time-series data	External database (SQL, NoSQL, time-series DB)	MD is not designed for millions of writes/day
Relational data with joins	External SQL database	MD has no join support; denormalize or use a relational DB
Data requiring strong consistency	External database	MD is eventually consistent for indexed fields

Schema design principles

• One entity per concept — Don't mix unrelated data in a single entity. Each entity should represent a clear business concept (e.g. reviews, wishlists, legacyOrders).
• Index what you query — Only fields in v-indexed can be used in where clauses. But don't over-index: each indexed field increases write latency and storage because the index is updated on every document change.
• Minimal v-default-fields — Return only the fields most consumers need by default. Large default payloads waste bandwidth.
• v-cache matches the workload — Leave true (default) for read-heavy entities. Set to false for entities with high write frequency where consumers need immediate consistency after writes.
• v-security is explicit — Set allowGetAll: false unless unauthenticated list access is intentional. Use publicRead, publicWrite, publicFilter only for fields that must be accessible without authentication.

VTEX schema extensions (v-* fields) — reference

Master Data v2 extends standard JSON Schema with v-* properties that control indexing, caching, security, defaults, triggers, and schema inheritance. These are VTEX-specific; standard JSON Schema validators ignore them.

v-indexed

Array of field names that Master Data will create secondary indexes for.

• Only indexed fields can appear in where clauses for searchDocuments and scrollDocuments. Queries on non-indexed fields trigger full document scans that time out on large datasets.
• Each index is updated on every document write. Over-indexing increases write latency and storage cost proportionally.
• When to index: fields used in where filters, sort expressions, or publicFilter. When not to index: large text fields (description, notes), fields never queried, or fields only read by document ID (indexing adds no benefit for getDocument).

{ "v-indexed": ["email", "status", "createdAt"] }

v-cache

Boolean (default true). Controls whether Master Data caches GET responses for individual documents.

• true (default) — Read-heavy entities benefit from caching. Most entities should leave this as default.
• false — Use for entities with high write frequency where consumers need fresh reads immediately after writes (e.g. real-time counters, configuration flags, session-like state).

{ "v-cache": false }

v-default-fields

Array of field names returned when the caller does not specify a fields parameter in the API request.

• Keep this minimal — only the fields most consumers need by default.
• Reduces payload size for common queries.

{ "v-default-fields": ["email", "status", "score", "createdAt"] }

v-security

Object controlling unauthenticated (public) access to fields. By default, all fields require authentication.

Property	Type	Description
allowGetAll	boolean	If true, unauthenticated users can list all documents. Default false; keep it off unless intentional.
publicRead	string[]	Fields readable without authentication
publicWrite	string[]	Fields writable without authentication
publicFilter	string[]	Fields usable in where clauses without authentication (must also be in v-indexed)

{
  "v-security": {
    "allowGetAll": false,
    "publicRead": ["status", "displayName", "rating"],
    "publicWrite": [],
    "publicFilter": ["status"]
  }
}

Never include PII (email, phone, addresses), internal IDs, or business-sensitive data in publicRead or publicFilter.

v-triggers

Array of trigger objects that define automated actions executed when documents are created or updated and meet specified conditions.

Property	Type	Description
name	string	Unique trigger name
active	boolean	Enable/disable the trigger
condition	string	where-style filter (e.g. "approved=false", "status=pending AND priority>3")
action.type	string	"email", "http" (webhook), or "action"
action.provider	string	Email provider name (for email type)
action.uri	string	Webhook URL (for http type)
action.method	string	HTTP method for webhook (for http type)
retry.times	number	Retry count on failure
retry.delay	object	Delay between retries (e.g. { "addMinutes": 5 })

{
  "v-triggers": [
    {
      "name": "notify-on-creation",
      "active": true,
      "condition": "status=new",
      "action": {
        "type": "email",
        "provider": "default",
        "subject": "New record: {{title}}",
        "to": ["[e-mail]"],
        "body": "Record {{id}} created by {{author}}"
      },
      "retry": { "times": 3, "delay": { "addMinutes": 5 } 
    
    
       
       
       
       
         
         
         
            
      
               
    
  

v-canonicalto

URL pointing to another schema in the same entity for schema inheritance. The current schema inherits properties and constraints from the target.

{
  "v-canonicalto": "https://{host}/api/dataentities/{entity}/schemas/{base-schema}"
}

additionalProperties

Standard JSON Schema property, but worth noting: set to false to reject fields not declared in properties. By default Master Data preserves extra fields without validation.

Hard constraints

Constraint: Index only fields used in where clauses or sort expressions

Every field in v-indexed creates a secondary index that is updated on every document write. Indexing fields that are never queried wastes write throughput and storage.

Why this matters — Over-indexing a high-write entity (e.g. indexing 15 fields when only 3 are queried) can double or triple write latency. On entities with millions of documents, unnecessary indexes also increase storage costs.

Detection — Compare v-indexed fields with actual where clauses in the codebase. Any indexed field not referenced in a where or sort is likely unnecessary.

Correct — Index only the fields you filter or sort on.

{
  "properties": {
    "email": { "type": "string" },
    "status": { "type": "string" },
    "score": { "type": "integer" },
    "notes": { "type": "string" },
    "createdAt": { "type": "string", "format": "date-time" }
  },
  "v-indexed": ["email", "status", "createdAt"]
}

Wrong — Indexing every field "just in case."

{
  "properties": {
    "email": { "type": "string" },
    "status": { "type": "string" },
    "score": { "type": "integer" },
    "notes": { "type": "string" },
    "createdAt": { "type": "string", "format": "date-time" }
  },
  "v-indexed": ["email", "status", "score", "notes" 

Constraint: Do not expose sensitive fields via v-security publicRead

The v-security.publicRead array makes fields accessible without any authentication. Never include PII (email, phone, addresses), internal IDs, or business-sensitive data in this list.

Why this matters — Public fields are accessible to anyone with the entity name and a document ID or search query. Exposing PII violates data protection regulations and creates security vulnerabilities.

Detection — Check v-security.publicRead and publicFilter for fields containing user data, internal references, or anything that should require authentication.

Correct — Expose only non-sensitive, display-oriented fields.

{
  "v-security": {
    "allowGetAll": false,
    "publicRead": ["status", "displayName", "rating"],
    "publicWrite": [],
    "publicFilter": ["status"]
  }
}

Wrong — Exposing PII and internal fields publicly.

{
  "v-security": {
    "allowGetAll": true,
    "publicRead": ["email", "phone", "cpf", "internalScore", "organizationId"],
    "publicWrite": ["email"],
    "publicFilter": ["email", "phone"]
  }
}

Constraint: Respect the 60-schema-per-entity limit

Master Data v2 entities have a hard limit of 60 schemas. The masterdata builder creates a new schema per app version linked or installed. Once the limit is reached, new versions fail to deploy.

Why this matters — During active development with frequent vtex link cycles, schemas accumulate quickly. Hitting the limit blocks deployment until old schemas are manually deleted.

Detection — Apps with many link/publish cycles. Check schema count via GET /api/dataentities/{entity}/schemas.

Correct — Periodically clean up unused schemas. Automate cleanup in CI/CD.

# List schemas to identify stale ones
curl "https://{account}.vtexcommercestable.com.br/api/dataentities/{entity}/schemas" \
  -H "X-VTEX-API-AppKey: {key}" -H "X-VTEX-API-AppToken: {token}"

# Delete unused schemas
curl -X DELETE "https://{account}.vtexcommercestable.com.br/api/dataentities/{entity}/schemas/{old-schema}" \
  -H "X-VTEX-API-AppKey: {key}" -H "X-VTEX-API-AppToken: {token}"

Wrong — Never cleaning up schemas during development until the limit blocks deployment.

Preferred pattern

Complete schema example with all VTEX extensions

{
  "$schema": "http://json-schema.org/schema#",
  "title": "product-review-v1",
  "type": "object",
  "properties": {
    "productId": { "type": "string" },
    "author": { "type": "string" },
    "email": { "type": "string", "format": "email" },
    "rating": { "type": "integer", "minimum": 1, "maximum": 5 }
          
          
        
          
  
      
      
   
    
    
    
    
    
    
  
   
   
     
     
      
      
      
      
      
      
    
     
       
  
   
    
       
       
       
       
         
         
         
         
         
      
       
         
            
      
    
  

Triggers: when to use and when not to

Use triggers when:

• You need email notifications on document changes (e.g. moderation alerts)
• You need to call an external webhook when a document meets a condition
• The action is simple, fire-and-forget, and doesn't need complex error handling

Do NOT use triggers when:

• You need complex orchestration, retries with backoff, or error recovery — use IO events instead
• You need sub-second response to changes — triggers have built-in delay
• The action modifies other MD entities in a chain — risk of cascading trigger loops
• You need conditional logic more complex than a where-style filter

Document counting without full fetch

Use the REST-Content-Range header to get document counts efficiently:

# Count documents without fetching them
curl "https://{account}.vtexcommercestable.com.br/api/dataentities/{entity}/search?_fields=id" \
  -H "REST-Range: resources=0-0" \
  -H "X-VTEX-API-AppKey: {key}" -H "X-VTEX-API-AppToken: {token}"
# Response header: REST-Content-Range: resources 0-0/12345
# The number after "/" is the total document count

Search vs Scroll

Use	When	Max page size
searchDocuments	Bounded result sets, UI pagination, known small size	100 per page
scrollDocuments	Large exports, bulk operations, unbounded iteration	Configurable batch

Common failure modes

• Over-indexing — Indexing 10+ fields on a high-write entity. Every write updates all indexes, increasing latency and storage.
• Missing indexes — Querying on non-indexed fields triggers full scans. Works in dev with 100 docs, times out in production with 100k.
• v-cache: false by default — Disabling cache on read-heavy entities forces every GET to hit the database. Only disable for high-write entities.
• allowGetAll: true with PII — Unauthenticated users can list all documents including sensitive data.
• Schema accumulation — 60 schemas from development cycles blocks production deployments.
• Trigger chains — Trigger A modifies entity B, which has a trigger that modifies entity A — infinite loop.
• MD as a log store — Entities growing unboundedly with traffic volume. Use ctx.vtex.logger instead.
• MD on critical path — Synchronous MD read in checkout with no timeout or fallback.

Review checklist

• Has a storage fit review been done? (MD vs Catalog vs OMS vs VBase vs external DB)
• Are only queried fields in v-indexed? No unnecessary indexes?
• Is v-cache set appropriately for the entity's read/write ratio?
• Does v-security restrict public access to non-sensitive fields only?
• Is allowGetAll set to false unless explicitly needed?
• Are triggers simple and non-chaining? No risk of trigger loops?
• Is there a schema cleanup strategy for the 60-schema limit?
• Is the entity off the purchase critical path (checkout, cart, payment)?
• For large datasets (100k+ docs), is scrollDocuments used instead of paginated search?
• Are v-default-fields minimal (not returning everything by default)?

Related skills

• vtex-io-masterdata — IO app integration: MasterDataClient, masterdata builder, CRUD patterns
• vtex-io-application-performance — Caching layers and BFF patterns when exposing MD data
• architecture-well-architected-commerce — Cross-cutting storage and architecture principles

Reference

• Working with JSON Schemas in Master Data v2 — v-indexed, v-cache, v-security, v-triggers configuration
• Master Data v2 Basics — Core concepts and data model
• Master Data Schema Lifecycle — Schema versioning and the 60-schema limit
• Setting Up Triggers on Master Data v2 — Trigger configuration and patterns
• Master Data v2 API Reference — Complete API specification
• Master Data v2 Document Saving Flow — Validation, indexing, and trigger execution order