This scenario describes a classic one-to-many relationship where the ‘many’ side (orders) is unbounded and frequently updated. Storing orders in a separate collection linked by customer_id is the correct approach. It prevents customer documents from growing indefinitely, avoids large document rewrites, and allows for efficient indexing and querying on the orders collection itself. Embedding is best for small, bounded arrays or data that is always accessed with its parent.

The most impactful performance improvement for aggregation pipelines often comes from using indexes to quickly filter and sort documents in the initial stages (like $match and $sort). This reduces the amount of data that needs to be processed in memory by subsequent, more expensive stages like $group. While allowDiskUse can prevent errors, it’s a last resort that indicates the operation is resource-intensive. Running heavy analytics on primary nodes during peak hours is a poor practice that can affect application latency.

For a compound index, the order of fields is critical. A query can only use a compound index for filtering if it includes criteria for a prefix of the index’s fields. If you have a compound index on { department: 1, status: 1 }, a query filtering only on ‘status’ cannot use that index efficiently because ‘status’ is not the leading field. The query planner would likely opt for a collection scan. The solution is to create a separate single-field index on ‘status’ or adjust the compound index order if the query patterns support it.

This is a classic concurrency problem. Two application threads can read the same inventory count simultaneously, both see it as >0, and both proceed to decrement and create an order, leading to overselling. Multi-document transactions (available in replica sets and sharded clusters) provide atomicity across multiple documents and collections. By wrapping the read, update, and insert operations in a transaction, you ensure that all steps complete successfully as a single unit of work, or none of them do, preventing this race condition.

The dominant access pattern is a targeted read by email. A shard key on hashed email will distribute writes evenly across the cluster (good write scalability) and allow the mongos router to direct a query for a specific email to a single shard (efficient targeted read). Sharding by country would lead to unbalanced chunks if some countries have vastly more users than others (the “hotspot” problem). A compound key starting with country would make targeted email queries inefficient unless the country is also specified. _id is a good default for unsharded collections but doesn’t align with the main query pattern here.

Secondary nodes in a replica set apply operations from the primary’s oplog. If the oplog is too small relative to the write volume, it can roll over before a lagging secondary has a chance to catch up. When this happens, the secondary can no longer stay in sync via the oplog and must perform an initial sync, which is a very I/O-intensive process involving a full copy of the data. This can manifest as sustained high disk I/O on the secondary. Ensuring your oplog is appropriately sized for your workload’s write volume and the maximum expected secondary lag is a critical operational task.

For a mandatory application-level field that doesn’t need to be immediately backfilled for historical queries, the safest and most efficient strategy is often to let the schema evolve gradually. Deploy the application code that writes the new field for all new and updated documents. Existing documents will remain without the field until they are next written. This avoids a massive, blocking update operation that could strain the cluster and cause performance issues. You can optionally backfill older documents later with a controlled, batched process if absolutely necessary for reporting.

A ‘W’ lock indicates an exclusive write lock. Foreground index creation obtains an exclusive write lock on the entire database (or collection, depending on version) for the duration of the build, blocking all other read and write operations on that database/collection. This is why foreground index builds are disruptive and generally not recommended for production systems. The other operations typically hold less restrictive locks (e.g., read locks for queries, intent locks for writes) or yield periodically.

The Database Profiler is the primary tool for identifying slow queries. By setting the profiling level (e.g., to log operations slower than 100ms), MongoDB will write detailed documents to the system.profile collection for every operation that exceeds the threshold. You can then analyze this collection to find the most frequent or slowest operations, including their query shapes, execution times, and plan summaries. mongostat gives system-level metrics, explain is for deep-dive on a known query, and $currentOp shows currently running operations, not historical slow ones.

This scenario describes a classic one-to-many relationship where the ‘many’ side (orders) is unbounded and frequently updated. Storing orders in a separate collection linked by customer_id is the correct approach. It prevents customer documents from growing indefinitely, avoids large document rewrites, and allows for efficient indexing and querying on the orders collection itself. Embedding is best for small, bounded arrays or data that is always accessed with its parent.0