Index Selectivity

Lesson content

Index Selectivity MongoMart experience path: This topic is taught through the same MongoMart e-commerce system used throughout the course. Shared database // Core MongoMart collections customers: { _id, name, email, addresses[], createdAt } products: { _id, sku, name, categoryId, price, attributes, stock } carts: { _id, customerId, items[], expiresAt } orders: { _id, orderNo, customerId, items[], status, total, orderedAt } inventory: { _id, sku, available, reserved, warehouseId } payments: { _id, orderId, providerRef, status, amount } notifications: { _id, customerId, type, payload, sentAt } Beginner foundation Define Index Selectivity, identify what problem it solves, and run the smallest working example before adding abstraction. Real-world scenario The orders collection grows past 100 million documents and the account-history page must remain predictably fast. Practical example db.orders.createIndex({ customerId: 1, orderedAt: -1 }) db.orders.find({ customerId: customerId }) .sort({ orderedAt: -1 }) .limit(20) .explain("executionStats") Intermediate reasoning Predict the exact documents read or changed. Test missing fields, nulls, duplicates, empty arrays, and boundary values. Validate the result with a second query or assertion. Advanced production use Measure behavior with realistic cardinality and concurrency. Add indexes or distributed features only after identifying the actual access pattern and failure mode. 10-year experience perspective Use profiler and executionStats evidence; evaluate keys examined, documents examined, spills, cache pressure, p95/p99 latency, and write cost. Review checklist Correctness and atomicity Schema evolution and backward compatibility Performance and capacity evidence Security and privacy Failure recovery, monitoring, and ownership Easy example Find one customer by email and return only the fields needed by the screen. db.customers.findOne( { email: "asha@example.com" }, { name: 1, email: 1, status: 1 } ) How to verify the easy example Run it with representative input. Confirm the expected output. Try one missing, invalid, or boundary value. Advanced example Aggregate completed orders by customer, rank revenue, and verify the access pattern with execution statistics. db.orders.aggregate([ { $match: { status: "completed" } }, { $group: { _id: "$customerId", revenue: { $sum: "$total" } } }, { $setWindowFields: { sortBy: { revenue: -1 }, output: { revenueRank: { $denseRank: {} } } } }, { $limit: 20 } ]).explain("executionStats") Advanced review Explain the tradeoffs and assumptions. Test failure, scale, security, and recovery behavior. Capture evidence from tests, execution plans, logs, or review output. Additional practical guidance Index Selectivity production detail Use profiler and executionStats evidence; evaluate keys examined, documents examined, spills, cache pressure, p95/p99 latency, and write cost. Required evidence A repeatable setup and test case The expected successful result One failure or edge case Relevant explain, profiler, log, or monitoring output A rollback or recovery approach