MongoDB Expert

MongoDB Expert

Model data, index it, and query it the MongoDB way: around access patterns, not normalization. The costly mistake this skill prevents is a relationally-normalized schema ported into Mongo - a design that forces application-side joins on every read and, in the opposite failure, unbounded embedded arrays that grow until documents hit the 16MB limit and writes start failing in production.

Operating procedure

Schema first, indexes second, pipelines third - indexes serve queries, and queries are determined by the schema, so working backwards wastes every downstream decision.

Step 1: Gather inputs

1. The top read and write patterns, ranked by frequency: which entities are fetched together, filtered by what, sorted by what.
2. Cardinality and growth of every one-to-many relationship: bounded ("an order has 5-50 line items") or unbounded ("a user accrues events forever"). Label estimates as guesses.
3. Read/write ratio per collection and expected document counts.
4. Deployment shape: replica set or sharded cluster, and durability requirements.

Step 2: Decide embed vs reference per relationship

Apply these rules in order; the first that fires decides.

1. Accessed together and the child side is bounded (a known, modest maximum) → embed. One read serves the whole query.
2. Child is unbounded, large, or shared by multiple parents → reference. Unbounded arrays are the canonical anti-pattern: array growth relocates documents, bloats indexes, and marches toward the 16MB document cap.
3. Need the most recent few children with the parent, but the full set is unbounded → subset pattern: embed the latest N (say 10) and keep the full history in a referenced collection.
4. High-volume time-ordered children (events, readings) → bucket pattern: one document per parent per time window (for example, per hour) holding an array capped by the window.

Budget documents well under 16MB - treat a typical document above a few hundred KB as a design smell to justify, not a limit to approach.

Step 3: Index for the actual queries

Indexes are the single biggest performance lever.

• Follow the ESR rule for compound indexes: Equality fields first, then Sort fields, then Range fields. A query filtering on customerId and sorting by createdAt is fully served by { customerId: 1, createdAt: -1 }.
• Verify with explain("executionStats"): require IXSCAN, never COLLSCAN, on any hot path; check that totalDocsExamined is close to nReturned - a 100:1 ratio means the index is not selective for that query.
• Use partial indexes to index only the documents a query can match (for example, only status: "active").
• Use covered queries - projection limited to indexed fields - to skip fetching documents entirely.
• Cap the index count: every index taxes every write. If a collection carries more than 5-8 indexes, audit for unused ones with $indexStats.

Step 4: Build pipelines that shrink early

Filter early so indexes apply and less data flows downstream:

db.orders.aggregate([
  { $match: { status: "shipped", createdAt: { $gte: start } } },
  { $group: { _id: "$customerId", total: { $sum: "$amount" }, n: { $sum: 1 } } },
  { $sort: { total: -1 } },
  { $limit: 100 }
])

• Place $match and $project as early as possible; only a leading $match can use indexes.
• $lookup is a join - index the foreign field or the lookup degenerates to a per-document collection scan.
• Use $facet to compute multiple aggregations in one pass over the data.
• Stages have memory limits; pass { allowDiskUse: true } for large groupings and sorts, and treat needing it on a hot path as a signal to pre-aggregate.

Step 5: React to changes with change streams

const stream = db.collection("orders").watch([
  { $match: { operationType: "insert" } }
])
for await (const change of stream) { handle(change.fullDocument) }

Persist the resume token and resume from it after failures, or events are silently lost. Change streams require a replica set.

Step 6: Set the operational posture

• Run a replica set for durability; use write concern w: "majority" for anything you cannot afford to lose on failover.
• Choose read concern to match consistency needs; defaults favor speed over read-your-own-writes.
• Shard on a high-cardinality key that distributes writes evenly. Never shard on a monotonically increasing key (timestamps, ObjectId) - all inserts land on one shard. Use a hashed or compound key instead.
• Enable the database profiler (or slow-query log) with a threshold around 100ms and review it routinely.
• Use multi-document transactions only when truly needed; single-document operations are already atomic, and a schema that embeds what changes together rarely needs transactions at all.

Worked artifact: schema pair, bad vs good

The domain: an e-commerce store with users, orders, and order events (status changes, notifications - unbounded over time).

Bad - relational normalization ported into Mongo, plus an unbounded embed:

// users:   { _id, name, email }
// orders:  { _id, userId, status,
//            itemIds: [ObjectId, ...],          // items referenced -> N+1 reads
//            events: [{ ts, type, note }, ...]  // UNBOUNDED array
//          }
// items:   { _id, sku, name, price }

Rendering one order page costs 1 read for the order, N reads for the items, and the events array grows forever - busy orders churn toward the 16MB cap, every event push rewrites a growing document, and the events field bloats every index that includes it.

Good - embed the bounded, reference the unbounded, subset the recent:

// orders: {
//   _id, userId, status, createdAt,
//   items: [                       // EMBEDDED: bounded (5-50), read together,
//     { sku, name, price, qty }    // price snapshotted at purchase time
//   ],
//   recentEvents: [                // SUBSET: latest 10 only, capped with
//     { ts, type }                 // $push + $slice on every insert
//   ]
// }
// order_events: {                  // REFERENCED: unbounded full history
//   _id, orderId, ts, type, note   // index: { orderId: 1, ts: -1 }
// }
// users: { _id, name, email }      // shared by many orders -> referenced

One read renders the order page (items and recent activity embedded); the full audit trail lives in order_events, indexed for the rare "show all history" query; documents stay small and stable-sized. Items embed a price snapshot deliberately - the order must show what was paid, not the current catalog price.

Deliverable

Produce a schema and query design document containing: each relationship with its embed/reference/subset/bucket ruling and the rule that decided it, the collection sketches with representative documents, the index list with the query each serves and its ESR ordering, explain-plan verification notes for the hot paths, and the operational settings (write concern, shard key if sharded, profiler threshold).

Do NOT

• Do not normalize by habit; every referenced relationship is a join the application pays for on each read.
• Do not embed unbounded arrays; use the subset or bucket pattern before growth forces an emergency migration.
• Do not order compound index fields by intuition; violate ESR and the index serves the filter but not the sort, forcing in-memory sorts.
• Do not ship a query whose explain plan shows COLLSCAN on a hot path, or whose docs-examined dwarfs docs-returned.
• Do not shard on a monotonically increasing key; a single hot shard absorbs all writes.
• Do not reach for transactions to patch a schema that split data which changes together - restructure instead.

Quality bar

The design ships only when: every relationship's embed/reference decision cites cardinality and access pattern, not preference; no document can grow without bound under any workload; every hot query shows IXSCAN with docs-examined near docs-returned in explain("executionStats"); leading $match stages hit indexes; and the write-concern and shard-key choices are stated with their failure trade-offs.