/* DBLingo — Retail & E-commerce industry content pack. * Every level has proprietary exercise content specific to retail workflows. */ registerDBLingoIndustry(createDBLingoIndustryPack({ id: 'retail', name: 'Retail & E-commerce', shortName: 'Retail', description: 'MongoDB powers retail experiences by joining product catalogs, inventory, carts, orders, personalization, search, and recommendations.', promise: 'MongoDB powers retail experiences by joining product catalogs, inventory, carts, orders, personalization, search, and recommendations.', searchPhrase: 'waterproof hiking jacket', nouns: { profile: 'shopper', profilePlural: 'shoppers', profileCollection: 'customers', item: 'product', itemPlural: 'products', itemCollection: 'products', catalog: 'product catalog', user: 'store associate', transaction: 'order', transactionPlural: 'orders', documentPlural: 'product descriptions' }, stream: { eventCollection: 'orderEvents', alertCollection: 'opsAlerts', eventPlural: 'order and inventory events', criticalLabel: 'fulfillment-risk events', scoreField: 'priorityScore', threshold: 85, alertType: 'fulfillment_risk', sourceDb: 'retail', outcome: 'store operations dashboards and recommendation workflows' }, aha: [ { title: 'AHA: Product catalogs are naturally document-shaped', message: 'Shoes have sizes, electronics have specs, food has nutrition — MongoDB documents hold any product shape without a universal table.' }, { title: 'AHA: Personalization needs changing signals', message: 'Shopper preferences, cart history, and recommendations evolve constantly — flexible documents keep up without migrations.' }, { title: 'AHA: Aggregation ranks revenue and inventory quickly', message: 'Pipeline stages can compute best-sellers, low-stock alerts, and revenue trends from live order data.' }, { title: 'AHA: Search and vectors create discovery experiences', message: 'Atlas Search powers autocomplete and faceted browse; Vector Search enables "find similar" and visual search.' } ], levels: { /* ===== WORLD 1: Documents & Collections ===== */ d1: { title: 'Build a product document', kind: 'shape', prompt: 'Drag the right values into the product listing for a hiking jacket.', sub: 'Product documents combine scalar values with size arrays and embedded material details.', why: 'Retail product catalogs are naturally document-shaped — a jacket can keep its price, available sizes, and material details together, while shoes and electronics add their own attributes without a one-size-fits-all table.', skeleton: [ { key: '_id', type: 'oid', value: 'ObjectId("66b...")' }, { key: 'name', type: 'slot', slot: 'name' }, { key: 'price', type: 'slot', slot: 'price' }, { key: 'sizes', type: 'slot', slot: 'sizes' }, { key: 'material', type: 'slot', slot: 'material' } ], bank: [ { id: 'name', label: '"Trail Pro Jacket"', kind: 'value' }, { id: 'price', label: '189.99', kind: 'value' }, { id: 'sizes', label: '["S", "M", "L"]', kind: 'value' }, { id: 'material', label: '{ shell: "nylon", waterproof: true }', kind: 'value' }, { id: 'd1', label: 'Trail Pro Jacket', kind: 'value' }, { id: 'd2', label: '"189.99"', kind: 'value' }, { id: 'd3', label: '[S, "M", "L"]', kind: 'value' } ], answer: { name: 'name', price: 'price', sizes: 'sizes', material: 'material' } }, d2: { title: 'insertOne() — add to catalog', kind: 'blocks', prompt: 'Add a new sneaker to the products collection. Drag the missing pieces.', sub: 'insertOne() writes a single product document and returns its generated _id.', why: 'New products arrive daily in retail. `insertOne()` captures the full product shape — name, price, variants — in a single write, and MongoDB generates the `_id` automatically.', snippet: [ 'db.', { slot: 'col' }, '.', { slot: 'op' }, '({', '\n name: ', { slot: 'val' }, ',', '\n price: 129.00,', '\n inStock: ', { slot: 'bool' }, '\n})' ], bank: [ { id: 'op', label: 'insertOne', kind: 'op', answer: 'op' }, { id: 'col', label: 'products', kind: 'field', answer: 'col' }, { id: 'val', label: '"Air Runner X"', kind: 'value', answer: 'val' }, { id: 'bool', label: 'true', kind: 'value', answer: 'bool' }, { id: 'x1', label: 'addOne', kind: 'op' }, { id: 'x2', label: 'inventory', kind: 'field' }, { id: 'x3', label: 'Air Runner X', kind: 'value' } ] }, d3: { title: 'updateOne() — mark item shipped', kind: 'fill', prompt: 'Update order #ORD-7821 to "shipped" status.', sub: '$set patches specific fields without replacing the entire order document.', why: 'Order status changes frequently (placed → paid → shipped → delivered). `$set` updates only the status field, preserving all line items, payment details, and shipping info in the document.', snippet: [ 'db.orders.updateOne(', '\n { orderId: "ORD-7821" },', '\n { ', { blank: 'op' }, ': { ', { blank: 'field' }, ': ', { blank: 'val' }, ' } }', '\n)' ], choices: { op: { options: ['$set', '$push', '$inc', '$replace'], answer: '$set' }, field: { options: ['status', 'state', 'shipped', 'flag'], answer: 'status' }, val: { options: ['"shipped"', 'shipped', 'true', '1'], answer: '"shipped"' } } }, d4: { title: 'deleteOne() — remove expired cart', kind: 'fill', prompt: 'Delete the oldest abandoned cart from the carts collection.', sub: 'deleteOne() removes exactly one document — sort to target the oldest.', why: 'Abandoned carts pile up and waste storage. Deleting the oldest expired cart keeps the collection lean. Always sort by creation date to pick the right one — a broad filter could delete active carts.', snippet: [ 'db.carts.', { blank: 'op' }, '(', '\n { status: ', { blank: 'val' }, ' },', '\n { sort: { createdAt: ', { blank: 'dir' }, ' } }', '\n)' ], choices: { op: { options: ['deleteOne', 'removeOne', 'dropOne'], answer: 'deleteOne' }, val: { options: ['"abandoned"', 'abandoned', '"expired"'], answer: '"abandoned"' }, dir: { options: ['1', '-1', '"asc"'], answer: '1' } } }, /* ===== WORLD 2: Querying ===== */ q1: { title: 'Find products by category', kind: 'blocks', prompt: 'Find every product in the "electronics" category.', sub: 'Equality queries match a field value directly — fast with an index.', why: 'Category browsing is the most common retail query. An index on `category` lets MongoDB return matching products in microseconds, even across millions of SKUs.', snippet: [ 'db.products.find({ ', { slot: 'field' }, ': ', { slot: 'val' }, ' })' ], bank: [ { id: 'field', label: 'category', kind: 'field', answer: 'field' }, { id: 'val', label: '"electronics"', kind: 'value', answer: 'val' }, { id: 'x1', label: 'type', kind: 'field' }, { id: 'x2', label: 'electronics', kind: 'value' }, { id: 'x3', label: 'department', kind: 'field' } ] }, q2: { title: '$gt — premium products', kind: 'blocks', prompt: 'Find products priced over $500 for the luxury tier page.', sub: '$match with $gt filters products above a price threshold.', why: 'Price-range filters are fundamental to e-commerce. Placing $match with an indexed `price` field first means MongoDB scans only the relevant price bucket, not the entire catalog.', snippet: [ 'db.products.aggregate([', '\n { ', { slot: 'stage' }, ': { price: { ', { slot: 'op' }, ': ', { slot: 'val' }, ' } } }', '\n])' ], bank: [ { id: 'stage', label: '$match', kind: 'stage', answer: 'stage' }, { id: 'op', label: '$gt', kind: 'op', answer: 'op' }, { id: 'val', label: '500', kind: 'value', answer: 'val' }, { id: 'x1', label: '$filter', kind: 'stage' }, { id: 'x2', label: '$gte', kind: 'op' }, { id: 'x3', label: '$lt', kind: 'op' }, { id: 'x4', label: '"500"', kind: 'value' } ] }, q3: { title: '$in — multi-category browse', kind: 'fill', prompt: 'Find products in "shoes" or "accessories" categories.', sub: '$in matches any value in the array — great for multi-select filters.', why: 'Shoppers often filter by multiple categories simultaneously. `$in` handles this efficiently with a single index scan, unlike chaining multiple `$or` conditions.', snippet: [ 'db.products.find({', '\n category: { ', { blank: 'op' }, ': ', { blank: 'arr' }, ' }', '\n})' ], choices: { op: { options: ['$in', '$any', '$or', '$contains'], answer: '$in' }, arr: { options: ['["shoes", "accessories"]', '"shoes, accessories"', '{ shoes, accessories }', '[shoes, accessories]'], answer: '["shoes", "accessories"]' } } }, q4: { title: 'Compound filter — price + rating', kind: 'fill', prompt: 'Find in-stock products rated 4 stars or above, priced under $50.', sub: 'Combine equality and range filters in one query object.', why: 'Shoppers want "good and cheap" — combining inStock equality with rating/price ranges. MongoDB ANDs multiple fields implicitly, making these compound filters simple to write and fast to execute.', snippet: [ 'db.products.find({', '\n inStock: true,', '\n rating: { ', { blank: 'gte' }, ': 4 },', '\n price: { ', { blank: 'lt' }, ': 50 }', '\n})' ], choices: { gte: { options: ['$gte', '$gt', '$geq', '>='], answer: '$gte' }, lt: { options: ['$lt', '$lte', '$leq', '<='], answer: '$lt' } } }, /* ===== WORLD 3: Aggregation Pipeline ===== */ a1: { collection: 'orderLines', // shown in the solved-query preview title: 'Best-sellers by revenue', kind: 'reorder', prompt: 'Rank products by total revenue in 2024 — re-order the pipeline stages.', sub: 'Filter to 2024 orders, group by product, sort by revenue, limit to top 10.', why: 'Best-seller rankings drive homepage placement and restocking decisions. Filter first (shrink the dataset), group to sum revenue per SKU, sort descending, and limit to the top N.', stages: [ { id: 'm', code: '$match: { orderDate: { $gte: ISODate("2024-01-01") } }', sub: 'filter to 2024 orders', correct: 0 }, { id: 'g', code: '$group: { _id: "$productId", revenue: { $sum: "$lineTotal" } }', sub: 'sum revenue per product', correct: 1 }, { id: 's', code: '$sort: { revenue: -1 }', sub: 'highest revenue first', correct: 2 }, { id: 'l', code: '$limit: 10', sub: 'top 10 products', correct: 3 } ], initial: ['s', 'l', 'm', 'g'] }, a2: { title: '$project — order summary', kind: 'fill', prompt: 'Return just the product name and total quantity sold — hide _id.', sub: '$project controls which fields appear in the output.', why: 'Dashboard widgets need lean payloads. `$project` with `_id: 0` strips the internal identifier, delivering only the fields the frontend needs — productName and totalQty.', snippet: [ 'db.orderLines.aggregate([', '\n { $project: {', '\n _id: ', { blank: 'id' }, ',', '\n productName: ', { blank: 'one' }, ',', '\n totalQty: ', { blank: 'one2' }, '\n } }', '\n])' ], choices: { id: { options: ['0', '1', 'false', 'null'], answer: '0' }, one: { options: ['1', '0', 'true', '"yes"'], answer: '1' }, one2: { options: ['1', '0', '"$totalQty"', 'yes'], answer: '1' } } }, a3: { collection: 'orderLines', // shown in the solved-query preview title: '$lookup — enrich orders with product info', kind: 'reorder', prompt: 'Attach product details to each order line, then show just the product name.', sub: '$lookup joins products, $unwind flattens, $project picks fields.', why: 'Order history pages need product names alongside line items. `$lookup` brings product data in, `$unwind` flattens the array, and `$project` delivers a clean shape for the storefront.', stages: [ { id: 'lo', code: '$lookup: { from: "products", localField: "productId", foreignField: "_id", as: "product" }', sub: 'join product catalog', correct: 0 }, { id: 'un', code: '$unwind: "$product"', sub: 'flatten to one doc', correct: 1 }, { id: 'pr', code: '$project: { _id: 0, orderId: 1, productName: "$product.name", qty: 1 }', sub: 'keep useful fields', correct: 2 } ], initial: ['pr', 'lo', 'un'] }, a4: { title: '$sort + $limit — newest arrivals', kind: 'blocks', prompt: 'Show the 6 most recently added products.', sub: 'Sort by addedAt descending, then limit to 6 for the "New Arrivals" carousel.', why: 'The "New Arrivals" section drives discovery. `$sort` + `$limit` together let MongoDB stream only the top-N newest products without sorting the entire catalog in memory.', snippet: [ 'db.products.aggregate([', '\n { ', { slot: 'sort' }, ': { addedAt: ', { slot: 'dir' }, ' } },', '\n { ', { slot: 'limit' }, ': ', { slot: 'n' }, ' }', '\n])' ], bank: [ { id: 'sort', label: '$sort', kind: 'stage', answer: 'sort' }, { id: 'limit', label: '$limit', kind: 'stage', answer: 'limit' }, { id: 'dir', label: '-1', kind: 'value', answer: 'dir' }, { id: 'n', label: '6', kind: 'value', answer: 'n' }, { id: 'x1', label: '$top', kind: 'stage' }, { id: 'x2', label: '$first', kind: 'stage' }, { id: 'x3', label: '1', kind: 'value' }, { id: 'x4', label: '"6"', kind: 'value' } ] }, /* ===== WORLD 4: Indexes & Performance ===== */ i1: { title: 'Index strategies for e-commerce', kind: 'index', prompt: 'The `products` collection has four query patterns. Pick the best index for each.', sub: 'Match each access pattern to single-field, compound, Atlas Search, or no index.', why: 'E-commerce queries span simple category lookups, category+price combos, full-text product search, and low-cardinality flags. Each needs the right index type to keep page loads under 100ms.', collection: 'products', fields: [ { name: 'category', type: 'String', need: 'single', used: 'db.products.find({ category })' }, { name: 'category + price', type: 'String + Number', need: 'compound', used: 'db.products.find({ category }).sort({ price: 1 })' }, { name: 'description', type: 'String', need: 'search', used: 'Atlas Search for product discovery and autocomplete' }, { name: 'isFeatured', type: 'Bool', need: 'none', used: 'low cardinality — only true/false values' } ], bank: [ { id: 'single', label: 'Single-field', kind: 'index' }, { id: 'compound', label: 'Compound', kind: 'index' }, { id: 'search', label: 'Atlas Search', kind: 'index' }, { id: 'none', label: 'No index', kind: 'index' } ] }, i2: { title: 'ESR for product listings', kind: 'reorder', prompt: 'Order this compound index for: find in-stock products under $100, sorted by rating.', sub: 'For: db.products.find({ inStock: true, price: { $lt: 100 } }).sort({ rating: -1 })', why: 'ESR in retail: equality (inStock) narrows to available items, sort (rating) delivers results pre-ordered for display, range (price) scans only the relevant price bucket.', stages: [ { id: 'e', code: 'inStock: 1', sub: 'filter: { inStock: true }', correct: 0 }, { id: 's', code: 'rating: -1', sub: '.sort({ rating: -1 })', correct: 1 }, { id: 'r', code: 'price: 1', sub: 'filter: { price: { $lt: 100 } }', correct: 2 } ], initial: ['r', 's', 'e'] }, i3: { title: 'TTL — expire abandoned carts', kind: 'fill', prompt: 'Auto-delete shopping carts 7 days after last update.', sub: 'TTL indexes clean up stale carts automatically — no scheduled jobs needed.', why: 'Abandoned carts waste storage and skew analytics. A TTL index on `updatedAt` automatically removes carts after 7 days (604800 seconds), keeping the collection lean without maintenance scripts.', snippet: [ 'db.carts.', { blank: 'op' }, '(', '\n { updatedAt: 1 },', '\n { ', { blank: 'key' }, ': ', { blank: 'sec' }, ' }', '\n)' ], choices: { op: { options: ['createIndex', 'addIndex', 'index', 'ensureIndex'], answer: 'createIndex' }, key: { options: ['expireAfterSeconds', 'ttl', 'expires', 'maxAge'], answer: 'expireAfterSeconds' }, sec: { options: ['604800', '7', '168', '86400'], answer: '604800' } } }, i4: { title: 'Covered query — SKU price check', kind: 'fill', prompt: 'Query only fields inside the index { sku: 1, price: 1 } for a zero-fetch lookup.', sub: 'When all projected fields live in the index, MongoDB never touches the document.', why: 'Price-check APIs hit millions of times per day. A covered query on { sku, price } returns from the index alone — zero document fetches, sub-millisecond latency at checkout scale.', snippet: [ 'db.products.find(', '\n { sku: ', { blank: 'val' }, ' },', '\n { _id: ', { blank: 'id' }, ', sku: 1, price: 1 }', '\n).', { blank: 'verb' }, '("executionStats")' ], choices: { val: { options: ['"SKU-JACKET-01"', 'SKU-JACKET-01', '*', '{}'], answer: '"SKU-JACKET-01"' }, id: { options: ['0', '1', 'null', 'true'], answer: '0' }, verb: { options: ['explain', 'plan', 'analyze', 'trace'], answer: 'explain' } } }, /* ===== WORLD 5: Atlas Superpowers ===== */ v1: { title: '$vectorSearch — similar products', kind: 'fill', prompt: 'Find the 5 products most visually similar to a query image embedding.', sub: 'Atlas Vector Search finds nearest neighbors by cosine similarity on product embeddings.', why: 'Visual similarity ("shop the look") uses product image embeddings. Vector Search finds items with similar visual features even when text descriptions differ — powering "more like this" recommendations.', snippet: [ 'db.products.aggregate([{', '\n $vectorSearch: {', '\n index: "product_embed_idx",', '\n path: ', { blank: 'path' }, ',', '\n queryVector: imageEmbedding,', '\n numCandidates: 100,', '\n ', { blank: 'limit' }, ': 5', '\n }', '\n}])' ], choices: { path: { options: ['"embedding"', '"vector"', 'embedding', '"image"'], answer: '"embedding"' }, limit: { options: ['limit', 'numResults', 'topK', 'k'], answer: 'limit' } } }, v2: { title: '$search — product discovery', kind: 'blocks', prompt: 'Use Atlas Search to find products matching "waterproof hiking jacket".', sub: 'Atlas Search powers autocomplete, faceted browse, and relevance-ranked product results.', why: 'Site search is the #1 revenue driver in e-commerce. Atlas Search gives you relevance scoring, typo tolerance, synonyms, and facets — all from the same data already in MongoDB.', snippet: [ 'db.products.aggregate([', '\n { ', { slot: 'stage' }, ': {', '\n ', { slot: 'operator' }, ': {', '\n path: ', { slot: 'path' }, ',', '\n query: ', { slot: 'query' }, '\n }', '\n } }', '\n])' ], bank: [ { id: 'stage', label: '$search', kind: 'stage', answer: 'stage' }, { id: 'operator', label: 'text', kind: 'op', answer: 'operator' }, { id: 'path', label: '"description"', kind: 'field', answer: 'path' }, { id: 'query', label: '"waterproof hiking jacket"', kind: 'value', answer: 'query' }, { id: 'x1', label: '$text', kind: 'op' }, { id: 'x2', label: 'find', kind: 'op' }, { id: 'x3', label: 'waterproof hiking jacket', kind: 'value' } ] }, v3: { title: 'Atlas Trigger — low stock alert', kind: 'fill', prompt: 'Fire a restock notification whenever inventory is updated below threshold.', sub: 'Database triggers watch for changes and fire functions in real time.', why: 'Real-time inventory alerts prevent stockouts. Atlas Triggers fire on update events and can notify procurement systems within seconds — no polling or batch jobs needed.', snippet: [ '// Atlas Trigger config', '\n{', '\n type: "DATABASE",', '\n database: "ecommerce",', '\n collection: ', { blank: 'col' }, ',', '\n operationTypes: [', { blank: 'evt' }, '],', '\n function: ', { blank: 'fn' }, '\n}' ], choices: { col: { options: ['"inventory"', 'inventory', 'Inventory', '*'], answer: '"inventory"' }, evt: { options: ['"update"', '"insert"', '"write"', '"modify"'], answer: '"update"' }, fn: { options: ['"checkRestockLevel"', 'checkRestockLevel', 'fn()', 'restock()'], answer: '"checkRestockLevel"' } } }, v4: { collection: 'products', // shown in the solved-query preview title: 'RAG — shopping assistant', kind: 'reorder', prompt: 'Build a RAG pipeline for an AI shopping assistant: scope to store, retrieve semantically, trim for the LLM.', sub: 'Vector search scoped to store → drop low-confidence matches → project only what the LLM needs.', why: 'AI shopping assistants must search within the retailer\'s catalog only. Pre-filtering by storeId ensures relevance, and trimming the payload keeps LLM costs low while delivering accurate recommendations.', stages: [ { id: 'vs', code: '$vectorSearch: { index: "catalog_embed", path: "embedding", queryVector: q, limit: 8, filter: { storeId } }', sub: 'k-NN scoped to this store', correct: 0 }, { id: 'pj', code: '$project: { _id: 0, name: 1, description: 1, price: 1, score: { $meta: "vectorSearchScore" } }', sub: 'trim for the LLM', correct: 2 }, { id: 'ms', code: '$match: { score: { $gt: 0.75 } }', sub: 'drop weak matches', correct: 1 }, ], initial: ['pj', 'ms', 'vs'] } } }));