$lookup with pipeline (advanced join) in MongoDB - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

What is the output of this $lookup with pipeline?

Given two collections orders and products, what will be the result of the following aggregation on orders?

{
  $lookup: {
    from: "products",
    let: { product_id: "$productId" },
    pipeline: [
      { $match: { $expr: { $eq: ["$_id", "$$product_id"] } } },
      { $project: { name: 1, price: 1, _id: 0 } }
    ],
    as: "productDetails"
  }
}

Assume orders has a document { _id: 1, productId: 101, quantity: 2 } and products has { _id: 101, name: "Pen", price: 1.5 }.

A [{ _id: 1, productId: 101, quantity: 2, productDetails: [{ name: "Pen", price: 1.5 }] }]

B [{ _id: 1, productId: 101, quantity: 2, productDetails: [{ _id: 101, name: "Pen", price: 1.5 }] }]

C [{ _id: 1, productId: 101, quantity: 2, productDetails: [] }]

D SyntaxError: Invalid $lookup pipeline syntax

💡 Hint

Remember that $project can exclude _id by setting it to 0.

The $lookup uses a pipeline to match products._id with orders.productId. The $project excludes _id, so the embedded productDetails array contains only name and price.

📝 Syntax

intermediate

Which $lookup pipeline syntax is invalid?

Identify the option that will cause a syntax error in a MongoDB aggregation $lookup stage using a pipeline.

A { $lookup: { from: "items", let: { id: "$itemId" }, pipeline: [ { $match: { $expr: { $eq: ["$_id", "$id"] } } } ], as: "details" } }

B } } "sliated" :sa ,] } } } ]"di$$" ,"di_$"[ :qe$ { :rpxe$ { :hctam$ { [ :enilepip ,} "dImeti$" :di { :tel ,"smeti" :morf { :pukool$ {

C { $lookup: { from: "items", let: { id: "$itemId" }, pipeline: [ { $match: { $expr: { $eq: ["$_id", "$$id"] } } } ], as: "details" } }

D $lookup: { from: "items", let: { id: "$itemId" }, pipeline: [ { $match: { $expr: { $eq: ["$_id", "$$id"] } } } ], as: "details" } }

💡 Hint

Check how variables are referenced inside the pipeline with let.

Variables defined in let must be referenced inside the pipeline with $$ prefix, e.g., "$$id". Option A uses "$id" which is invalid and causes a syntax error.

❓ optimization

advanced

How to optimize $lookup pipeline for large collections?

You have a $lookup with a pipeline joining orders to products. The products collection is very large. Which option best improves performance?

A Use $group in the pipeline to aggregate all products before matching

B Use $unwind on the <code>products</code> collection before $lookup

C Remove the pipeline and use simple localField/foreignField matching

D Add an index on <code>products._id</code> and use $match with $expr in the pipeline

💡 Hint

Indexes help queries run faster, especially on large collections.

Adding an index on products._id helps the $match stage inside the pipeline run efficiently. Using $expr with indexed fields is optimal. Other options either do not improve or degrade performance.

🔧 Debug

advanced

Why does this $lookup pipeline return empty arrays?

Given this aggregation on orders:

{
  $lookup: {
    from: "products",
    let: { pid: "$productId" },
    pipeline: [
      { $match: { $expr: { $eq: ["$_id", "$pid"] } } }
    ],
    as: "productInfo"
  }
}

It returns productInfo as empty arrays even though matching documents exist. What is the cause?

A The <code>productId</code> field does not exist in <code>orders</code>

B Variable <code>pid</code> is referenced incorrectly without <code>$$</code> prefix inside $expr

C The <code>from</code> collection name is misspelled

D The <code>$match</code> stage is missing a $project

💡 Hint

Check how variables from let are used inside the pipeline.

Variables defined in let must be referenced with $$ inside the pipeline. Here, "$pid" is incorrect; it should be "$$pid". Without this, the match fails and returns empty arrays.

🧠 Conceptual

expert

What is the main advantage of using $lookup with a pipeline over localField/foreignField?

Choose the best explanation for why you would use $lookup with a pipeline instead of the simpler localField/foreignField syntax.

A It automatically indexes the joined fields for better performance

B It always runs faster than localField/foreignField joins

C It allows complex filtering, transformations, and multiple stages on the joined collection before embedding results

D It returns results as a flat list instead of an array

💡 Hint

Think about flexibility and control over the joined data.

The pipeline form of $lookup lets you apply multiple aggregation stages like $match, $project, $group, etc., on the joined collection. This is not possible with the simple localField/foreignField syntax, which only matches equality.

Practice

1. What is the main purpose of using $lookup with a pipeline in MongoDB?

easy

A. To create indexes on joined collections automatically

B. To update documents in the joined collection

C. To perform advanced joins with filtering and transformations on the joined data

D. To delete documents from multiple collections at once

Solution

1. Step 1: Understand $lookup basic function

   $lookup is used to join documents from another collection into the current one.

2. Step 2: Role of pipeline in $lookup

   Using a pipeline allows filtering and transforming the joined documents before adding them.

3. Final Answer:

   To perform advanced joins with filtering and transformations on the joined data -> Option C

4. Quick Check:

   $lookup with pipeline = advanced join [OK]

Hint: Remember: pipeline lets you filter joined data inside $lookup [OK]

Common Mistakes:

• Thinking $lookup with pipeline updates or deletes data
• Confusing $lookup with index creation
• Assuming $lookup merges collections permanently

2. Which of the following is the correct syntax to define variables for use inside the pipeline of a $lookup stage?

easy

A. "params: { var1: \"$field1\", var2: \"$field2\" }"

B. "vars: { var1: \"$field1\", var2: \"$field2\" }"

C. "variables: [ var1: \"$field1\", var2: \"$field2\" ]"

D. "let: { var1: \"$field1\", var2: \"$field2\" }"

Solution

1. Step 1: Recall $lookup variable syntax

   Variables for the pipeline are defined using the let keyword as an object.

2. Step 2: Match correct keyword and structure

   Only let with an object of key-value pairs is valid syntax.

3. Final Answer:

   "let: { var1: \"$field1\", var2: \"$field2\" }" -> Option D

4. Quick Check:

   Variables in $lookup pipeline = let [OK]

Hint: Use 'let' keyword with object to define variables in $lookup [OK]

Common Mistakes:

• Using 'vars' or 'variables' instead of 'let'
• Using array syntax instead of object for variables
• Omitting the $ sign before field names

3. Given collections orders and products, what will be the output of this aggregation on orders?

{
  $lookup: {
    from: "products",
    let: { prod_id: "$productId" },
    pipeline: [
      { $match: { $expr: { $eq: ["$_id", "$$prod_id"] } } },
      { $project: { name: 1, price: 1, _id: 0 } }
    ],
    as: "productDetails"
  }
}

medium

A. Each order document will have a 'productDetails' field with only product IDs

B. Each order document will have a new field 'productDetails' containing an array with matching product's name and price

C. The query will throw a syntax error due to incorrect variable reference

D. The 'productDetails' field will be a single object, not an array

Solution

1. Step 1: Understand $lookup with pipeline and let

   The let defines prod_id from orders.productId. The pipeline matches products._id to this variable.

2. Step 2: Analyze pipeline stages

   The pipeline filters products matching the order's productId and projects only name and price fields, excluding _id.

3. Final Answer:

   Each order document will have a new field 'productDetails' containing an array with matching product's name and price -> Option B

4. Quick Check:

   $lookup with pipeline returns array of matched docs [OK]

Hint: Remember $lookup with pipeline returns array in 'as' field [OK]

Common Mistakes:

• Expecting a single object instead of an array
• Using wrong variable reference syntax like '$prod_id' instead of '$$prod_id'
• Forgetting to project fields inside pipeline

4. Identify the error in this $lookup stage:

{
  $lookup: {
    from: "employees",
    let: { empId: "$employeeId" },
    pipeline: [
      { $match: { $expr: { $eq: ["$_id", "$$empId"] } } }
    ],
    as: "employeeInfo"
  }
}

medium

A. Variable reference inside $expr should be "$$empId" instead of "$empId"

B. The let keyword is not allowed inside $lookup

C. The from field must be an array, not a string

D. The as field cannot be named employeeInfo

Solution

1. Step 1: Check variable usage in $expr

   Variables defined in let must be referenced with double dollar signs $$ inside the pipeline.

2. Step 2: Identify incorrect variable reference

   The code uses "$empId" instead of "$$empId", causing a runtime error.

3. Final Answer:

   Variable reference inside $expr should be "$$empId" instead of "$empId" -> Option A

4. Quick Check:

   Use $$ to reference let variables inside pipeline [OK]

Hint: Use double $$ to reference let variables inside pipeline [OK]

Common Mistakes:

• Using single $ instead of double $$ for let variables
• Misplacing let outside $lookup
• Incorrect data type for 'from' field

5. You want to join orders with products but only include products priced above 100 in the joined array. Which $lookup pipeline correctly achieves this?

hard

A. { $lookup: { from: "products", let: { pid: "$productId" }, pipeline: [ { $match: { $expr: { $and: [ { $eq: ["$_id", "$$pid"] }, { $gt: ["$price", 100] } ] } } } ], as: "expensiveProduct" } }

B. { $lookup: { from: "products", localField: "productId", foreignField: "_id", as: "expensiveProduct", pipeline: [ { $match: { price: { $gt: 100 } } } ] } }

C. { $lookup: { from: "products", let: { pid: "$productId" }, pipeline: [ { $match: { _id: "$$pid", price: { $gt: 100 } } } ], as: "expensiveProduct" } }

D. { $lookup: { from: "products", let: { pid: "$productId" }, pipeline: [ { $match: { $expr: { $eq: ["$_id", "$$pid"] } } }, { $match: { price: { $gt: 100 } } } ], as: "expensiveProduct" } }

Solution

1. Step 1: Understand filtering with $expr and $and

   We must match documents where _id equals the variable and price is greater than 100 in a single $match using $expr and $and.

2. Step 2: Analyze each option's pipeline

   { $lookup: { from: "products", let: { pid: "$productId" }, pipeline: [ { $match: { $expr: { $and: [ { $eq: ["$_id", "$$pid"] }, { $gt: ["$price", 100] } ] } } } ], as: "expensiveProduct" } } correctly uses $expr with $and combining both conditions. { $lookup: { from: "products", localField: "productId", foreignField: "_id", as: "expensiveProduct", pipeline: [ { $match: { price: { $gt: 100 } } } ] } } wrongly uses pipeline with localField/foreignField which is invalid. { $lookup: { from: "products", let: { pid: "$productId" }, pipeline: [ { $match: { _id: "$$pid", price: { $gt: 100 } } } ], as: "expensiveProduct" } } uses invalid $match syntax inside pipeline. { $lookup: { from: "products", let: { pid: "$productId" }, pipeline: [ { $match: { $expr: { $eq: ["$_id", "$$pid"] } } }, { $match: { price: { $gt: 100 } } } ], as: "expensiveProduct" } } splits conditions into two $match stages, but second $match cannot use field without $expr inside pipeline.

3. Final Answer:

   Option A correctly filters joined products priced above 100 using $expr and $and -> Option A

4. Quick Check:

   Use $expr with $and for multiple conditions in $lookup pipeline [OK]

Hint: Combine conditions inside one $match using $expr and $and [OK]

Common Mistakes:

• Using localField/foreignField with pipeline together
• Splitting conditions into multiple $match without $expr
• Incorrect $match syntax inside pipeline