Atlas Tools¶

📖 MongoDB Atlas - Atlas documentation 📖 Atlas Search - Atlas Search documentation

Atlas Search¶

Overview¶

• Full-text search engine built on Apache Lucene
• Integrated directly into MongoDB Atlas
• Accessed via $search aggregation stage
• Supports autocomplete, fuzzy matching, faceted search, and scoring
• Separate from MongoDB text indexes (more powerful)

📖 Atlas Search Overview - Search concepts

Search Index Definition¶

{
  "mappings": {
    "dynamic": true,
    "fields": {
      "title": {
        "type": "string",
        "analyzer": "lucene.standard"
      },
      "description": {
        "type": "string",
        "analyzer": "lucene.english"
      },
      "price": {
        "type": "number"
      },
      "tags": {
        "type": "string",
        "analyzer": "lucene.keyword"
      },
      "productName": {
        "type": "autocomplete",
        "tokenization": "edgeGram",
        "minGrams": 3,
        "maxGrams": 15
      }
    }
  }
}

Index Types: - dynamic: true - Automatically index all fields (good for prototyping) - dynamic: false - Only index explicitly defined fields (recommended for production)

Field Types: - string - Text fields with analyzer - number - Numeric fields - date - Date fields - boolean - Boolean fields - geo - GeoJSON objects - autocomplete - Autocomplete suggestions - token - Exact match tokens

Search Operators¶

$search Stage:

db.products.aggregate([
  { $search: {
    index: "default",  // Index name (optional if "default")
    text: {
      query: "wireless headphones",
      path: "title",
      fuzzy: { maxEdits: 1 }  // Allow 1 character edit
    }
  }},
  { $limit: 10 },
  { $project: {
    title: 1,
    price: 1,
    score: { $meta: "searchScore" }
  }}
]);

Text Search:

{ $search: {
  text: {
    query: "coffee",
    path: ["title", "description"],  // Search multiple fields
    fuzzy: { maxEdits: 2, prefixLength: 3 }
  }
}}

Phrase Search:

{ $search: {
  phrase: {
    query: "new york",
    path: "city",
    slop: 1  // Allow 1 word between terms
  }
}}

Autocomplete Search:

{ $search: {
  autocomplete: {
    query: "head",
    path: "productName",
    tokenOrder: "sequential"
  }
}}

Compound Search:

{ $search: {
  compound: {
    must: [
      { text: { query: "laptop", path: "title" } }
    ],
    should: [
      { text: { query: "gaming", path: "description" } }
    ],
    mustNot: [
      { text: { query: "refurbished", path: "condition" } }
    ],
    filter: [
      { range: { path: "price", gte: 500, lte: 2000 } }
    ]
  }
}}

Compound Clauses: - must - Must match (affects score) - should - Should match (boosts score) - mustNot - Must not match (filters, does not affect score) - filter - Must match (filters, does not affect score)

Range Search:

{ $search: {
  range: {
    path: "price",
    gte: 100,
    lte: 500
  }
}}

Search Facets¶

db.products.aggregate([
  { $searchMeta: {
    facet: {
      operator: {
        text: { query: "laptop", path: "title" }
      },
      facets: {
        brandFacet: {
          type: "string",
          path: "brand",
          numBuckets: 10
        },
        priceFacet: {
          type: "number",
          path: "price",
          boundaries: [0, 500, 1000, 2000, 5000]
        }
      }
    }
  }}
]);

Search Scoring¶

// Boost specific fields
{ $search: {
  compound: {
    should: [
      {
        text: {
          query: "laptop",
          path: "title",
          score: { boost: { value: 3 } }
        }
      },
      {
        text: {
          query: "laptop",
          path: "description",
          score: { boost: { value: 1 } }
        }
      }
    ]
  }
}}

Atlas Charts¶

📖 Atlas Charts - Charts documentation

Overview¶

• Built-in data visualization tool in MongoDB Atlas
• No data movement or ETL required
• Connects directly to Atlas collections
• Supports real-time and scheduled refresh
• Embeddable charts for applications

Chart Types¶

• Bar/Column - Categorical comparisons
• Line - Time-series trends
• Area - Stacked time-series
• Scatter - Correlations between variables
• Donut/Pie - Proportional distribution
• Heatmap - Two-dimensional distribution
• Geospatial - Map-based visualization
• Number - Single metric display
• Table - Tabular data display
• Gauge - Progress toward a goal

Key Features¶

• Data Source - Connect to Atlas collections or views
• Aggregation - Charts use aggregation pipeline under the hood
• Filters - Interactive filtering on dashboards
• Embedding - Embed charts in web applications via SDK
• Dashboards - Organize multiple charts into dashboards
• Sharing - Share with team members or embed publicly

Atlas Data API¶

📖 Atlas Data API - Data API documentation

Overview¶

• HTTP/REST API for CRUD operations on Atlas data
• No MongoDB driver required
• Access from any HTTP client (curl, fetch, etc.)
• Authentication via API keys

Endpoints¶

Base URL: https://data.mongodb-api.com/app/<app-id>/endpoint/data/v1

Find Documents:

curl -X POST "https://data.mongodb-api.com/app/<app-id>/endpoint/data/v1/action/find" \
  -H "Content-Type: application/json" \
  -H "api-key: <API-KEY>" \
  -d '{
    "dataSource": "Cluster0",
    "database": "mydb",
    "collection": "users",
    "filter": { "status": "active" },
    "projection": { "name": 1, "email": 1 },
    "limit": 10
  }'

Insert Document:

curl -X POST ".../action/insertOne" \
  -H "Content-Type: application/json" \
  -H "api-key: <API-KEY>" \
  -d '{
    "dataSource": "Cluster0",
    "database": "mydb",
    "collection": "users",
    "document": { "name": "Alice", "email": "alice@example.com" }
  }'

Update Document:

curl -X POST ".../action/updateOne" \
  -d '{
    "dataSource": "Cluster0",
    "database": "mydb",
    "collection": "users",
    "filter": { "email": "alice@example.com" },
    "update": { "$set": { "lastLogin": { "$date": "2024-01-15T00:00:00Z" } } }
  }'

Available Actions: - findOne / find - Query documents - insertOne / insertMany - Insert documents - updateOne / updateMany - Update documents - deleteOne / deleteMany - Delete documents - aggregate - Run aggregation pipeline

Atlas Triggers¶

📖 Atlas Triggers - Triggers documentation

Trigger Types¶

Database Triggers¶

• Fire on collection changes (insert, update, delete, replace)
• Can be configured for specific operations
• Access to change event document (fullDocument, updateDescription)
• Support for document preimage (before change)

Configuration:

// Trigger function receives change event
exports = function(changeEvent) {
  const fullDocument = changeEvent.fullDocument;
  const operationType = changeEvent.operationType;
  const updateDescription = changeEvent.updateDescription;

  // Process the change
  if (operationType === "insert") {
    // Handle new document
  } else if (operationType === "update") {
    // Handle update
    const updatedFields = updateDescription.updatedFields;
  }
};

Trigger Options: - Operation Types: Insert, Update, Delete, Replace - Full Document: Include the full document after change - Document Preimage: Include document before change (requires oplog) - Project: Filter which fields trigger the function - Match Expression: Filter which documents trigger the function

Scheduled Triggers¶

• Fire on a cron schedule
• No change event - runs at specified times
• Good for periodic tasks (cleanup, reports, sync)

Cron Expressions:

# Every hour
0 * * * *

# Every day at midnight
0 0 * * *

# Every Monday at 9 AM
0 9 * * 1

Authentication Triggers¶

• Fire on user authentication events
• Create, login, delete user events
• Good for auditing and user onboarding

Trigger Functions¶

• Written in JavaScript
• Run in Atlas App Services runtime
• Can access MongoDB collections
• Can call external services (HTTP, email, etc.)
• Have access to context object (user, environment)

// Access collections in trigger function
exports = async function(changeEvent) {
  const mongodb = context.services.get("mongodb-atlas");
  const db = mongodb.db("mydb");
  const auditCollection = db.collection("audit_log");

  await auditCollection.insertOne({
    timestamp: new Date(),
    operation: changeEvent.operationType,
    collection: changeEvent.ns.coll,
    documentId: changeEvent.documentKey._id,
    userId: context.user.id
  });
};

Atlas Additional Features¶

Performance Advisor¶

• Analyzes slow queries automatically
• Suggests indexes to create
• Shows index usage statistics
• Identifies unused indexes for potential removal
• Available for M10+ clusters

Data Explorer¶

• Browse collections and documents in the Atlas UI
• Run queries and aggregation pipelines
• Edit documents directly
• View collection statistics
• Export query results

Atlas CLI¶

# Login
atlas auth login

# List clusters
atlas clusters list

# Create cluster
atlas clusters create myCluster --region US_EAST_1 --tier M10

# Load sample data
atlas clusters sampleData load myCluster

# Manage database users
atlas dbusers create --username myuser --password mypass --role readWriteAnyDatabase

📖 Atlas CLI - CLI documentation