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The API automatically selects the most efficient index based on your query parameters. Understanding these patterns helps you write performant queries.

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Index Usage

Queries filtered by tag use a dedicated tag index and are highly efficient. Use tag filters when possible for best performance.
{
  "filter": {
    "tags": { "any": ["hero", "product"] }
  }
}
Queries with name-based sorting use a dedicated index. Combining name sorting with status filters is optimal.
{
  "filter": {
    "status": { "eq": "active" }
  },
  "sort": [{ "field": "name", "order": "asc" }]
}
Queries with date-based sorting use the table’s primary index. This is the default and most efficient for recent assets.
{
  "sort": [{ "field": "createdAt", "order": "desc" }]
}
Queries combining multiple filters may use different execution strategies internally. The API automatically selects the most efficient index based on your query parameters.

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Best Practices

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Use Specific Filters First

More selective filters should be primary. Tag and type filters are highly optimized: 
{
  "filter": {
    "tags": { "any": ["hero"] },
    "type": { "eq": "image/jpeg" },
    "size": { "lt": 5000000 }
  }
}

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Limit Result Size

Always use pagination and reasonable limits: 
{
  "filter": { ... },
  "limit": 50,
  "page": 1
}

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Avoid Wildcards at Start

Pattern matching with leading wildcards (%name) is slower than trailing wildcards (name%): 
// Faster
{ "name": { "like": "hero-%" } }

// Slower
{ "name": { "like": "%-hero" } }

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Use Date Ranges

When filtering by date, provide both bounds when possible: 
{
  "filter": {
    "createdAt": {
      "gte": "2024-01-01T00:00:00Z",
      "lte": "2024-03-31T23:59:59Z"
    }
  }
}