Skip to main content
Modelence lets you configure indexes directly on each Store. Use:
  • indexes for regular MongoDB indexes (performance and constraints)
  • searchIndexes for MongoDB Atlas Search
  • indexCreationMode to control startup behavior

​
MongoDB Indexes

Configure indexes to improve query performance: 
export const dbPosts = new Store('posts', {
  schema: {
    title: schema.string(),
    authorId: schema.userId(),
    publishedAt: schema.date(),
    status: schema.string(),
  },

  indexes: [
    // Single field index
    { key: { authorId: 1 } },

    // Compound index
    { key: { status: 1, publishedAt: -1 } },

    // Named index
    {
      key: { title: 'text' },
      name: 'title_text_index'
    },

    // Unique index
    {
      key: { slug: 1 },
      unique: true
    }
  ]
});
Modelence automatically creates these indexes when the application starts.
Starting from modelence@0.12.0, Modelence manages framework-created indexes using this strategy.Modelence manages framework-created indexes with a _modelence_ name prefix. If you provide name, Modelence still prefixes it automatically. During startup reconciliation, Modelence will create missing managed indexes, drop outdated managed indexes, and recreate changed ones.
Manual indexes that do not use the _modelence_ prefix are preserved unless they conflict with code-defined index keys.

​
Common Index Options

indexes accepts MongoDB index options like unique, sparse, expireAfterSeconds, and partialFilterExpression: 
indexes: [
  { key: { email: 1 }, unique: true },
  { key: { deletedAt: 1 }, sparse: true },
  { key: { expiresAt: 1 }, expireAfterSeconds: 0 }, // TTL
  { key: { status: 1 }, partialFilterExpression: { status: { $exists: true } } },
]

​
Index Creation Mode

By default, store indexes are created in the background during startup.
Set indexCreationMode: 'blocking' for indexes that must be created before startup continues. 
export const locksCollection = new Store('_modelenceLocks', {
  schema: {
    resource: schema.string(),
    instanceId: schema.string(),
    acquiredAt: schema.date(),
  },

  indexes: [
    { key: { resource: 1 }, unique: true },
    { key: { resource: 1, acquiredAt: 1 } },
  ],

  indexCreationMode: 'blocking',
});
Startup ordering:
  1. blocking indexes are awaited before migrations begin.
  2. Migrations are then started.
  3. background indexes may continue running in parallel with migrations.
In multi-instance deployments, index creation is coordinated through a distributed indexes lock so only one instance performs reconciliation at a time. If index creation fails for a store, startup continues and a warning is logged.
See Migrations for startup and race-condition guidance with cron jobs.

​
MongoDB Atlas Search Indexes

For advanced full-text search capabilities using MongoDB Atlas Search, configure search indexes: 
export const dbArticles = new Store('articles', {
  schema: {
    title: schema.string(),
    content: schema.string(),
    tags: schema.array(schema.string()),
    category: schema.string(),
    authorId: schema.userId(),
  },

  indexes: [
    { key: { authorId: 1 } },
  ],

  searchIndexes: [
    {
      name: 'article_search',
      definition: {
        mappings: {
          dynamic: false,
          fields: {
            title: {
              type: 'string',
              analyzer: 'lucene.standard'
            },
            content: {
              type: 'string',
              analyzer: 'lucene.standard'
            },
            tags: {
              type: 'string',
              analyzer: 'lucene.keyword'
            },
            category: {
              type: 'string',
              analyzer: 'lucene.keyword'
            }
          }
        }
      }
    }
  ]
});

​
Using Search Indexes

Once configured, use the search indexes with MongoDB’s aggregation pipeline: 
// Full-text search across title and content
const results = await dbArticles.aggregate([
  {
    $search: {
      index: 'article_search',
      text: {
        query: 'machine learning',
        path: ['title', 'content']
      }
    }
  },
  {
    $limit: 10
  }
]).toArray();

// Search with filters
const filteredResults = await dbArticles.aggregate([
  {
    $search: {
      index: 'article_search',
      compound: {
        must: [
          {
            text: {
              query: 'tutorial',
              path: 'title'
            }
          }
        ],
        filter: [
          {
            text: {
              query: 'javascript',
              path: 'category'
            }
          }
        ]
      }
    }
  }
]).toArray();
Search indexes require MongoDB Atlas. They are automatically created when your application starts, similar to regular indexes.

​
Vector Search Indexes

For Atlas Vector Search, you can define a vector index in searchIndexes: 
export const dbDocuments = new Store('documents', {
  schema: {
    title: schema.string(),
    embedding: schema.array(schema.number()),
  },
  indexes: [],
  searchIndexes: [
    Store.vectorIndex({
      field: 'embedding',
      dimensions: 1536,
      similarity: 'cosine',
    }),
  ],
});
See Voyage AI Tutorial for an end-to-end vector search example.