Change streams on collections in MongoDB - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

Output of a simple change stream watching inserts

Consider a MongoDB collection users. You open a change stream with the pipeline filtering only insert operations. If a new document {_id: 1, name: 'Alice'} is inserted, what will the change stream output contain?

MongoDB

const changeStream = db.users.watch([{ $match: { operationType: 'insert' } }]);
// After inserting { _id: 1, name: 'Alice' }

A A change event document with operationType 'delete' and documentKey { _id: 1 }

B A change event document with operationType 'insert' and fullDocument containing { _id: 1, name: 'Alice' }

C No change event will be emitted because inserts are not captured

D A change event document with operationType 'update' and fullDocument containing { _id: 1, name: 'Alice' }

💡 Hint

Change streams capture insert operations and include the full inserted document.

Change streams emit events for operations like insert, update, and delete. When filtering for inserts, the event includes the inserted document in fullDocument.

❓ query_result

intermediate

Filtering change stream events by field value

You want to watch changes on the orders collection but only for documents where the status field is 'shipped'. Which pipeline will correctly filter these events?

MongoDB

const pipeline = [
  { $match: { 'fullDocument.status': 'shipped' } }
];
const changeStream = db.orders.watch(pipeline);

A Pipeline with { $match: { 'fullDocument.status': 'shipped' } }

B Pipeline with { $match: { operationType: 'update', 'updateDescription.updatedFields.status': 'shipped' } }

C Pipeline with { $match: { 'documentKey.status': 'shipped' } }

D Pipeline with { $match: { status: 'shipped' } }

💡 Hint

The fullDocument field contains the entire changed document after the operation.

To filter by a field value in the changed document, you match on 'fullDocument.fieldName'.

❓ schema

advanced

Required collection configuration for change streams

Which of the following is required for a MongoDB collection to support change streams?

A The collection must be a capped collection

B The collection must have a unique index on the _id field

C The collection must have a TTL index

D The collection must be part of a replica set or a sharded cluster

💡 Hint

Change streams rely on the oplog which exists only in replica sets or sharded clusters.

Change streams require the collection to be in a replica set or sharded cluster because they use the oplog to track changes.

🔧 Debug

advanced

Why does this change stream not receive any events?

You run this code to watch changes on the products collection:

const changeStream = db.products.watch([{ $match: { operationType: 'insert' } }]);
changeStream.on('change', (next) => console.log(next));

But no events appear even after inserting documents. What is the most likely reason?

A The MongoDB deployment is a standalone server, not a replica set or sharded cluster

B The pipeline is missing a $project stage

C The change stream cursor was not iterated with next() or on('change')

D The insert operations are not supported by change streams

💡 Hint

Change streams require a replica set or sharded cluster to work.

Change streams do not work on standalone MongoDB servers because they rely on the oplog available only in replica sets or sharded clusters.

❓ optimization

expert

Optimizing change stream pipeline for update operations

You want to watch only update operations on the inventory collection where the quantity field was changed. Which pipeline is the most efficient to achieve this?

A [{ $match: { 'updateDescription.updatedFields.quantity': { $exists: true } } }]

B [{ $match: { operationType: 'update' } }, { $match: { 'updateDescription.updatedFields.quantity': { $exists: true } } }]

C [{ $match: { operationType: 'update', 'updateDescription.updatedFields.quantity': { $exists: true } } }]

D [{ $match: { operationType: 'update', 'fullDocument.quantity': { $exists: true } } }]

💡 Hint

Combine conditions in a single $match stage for better performance.

Using a single $match stage with both conditions reduces pipeline stages and improves efficiency.