$lookup with pipeline (advanced join) in MongoDB - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of the $lookup stage in MongoDB aggregation?

The $lookup stage is used to perform a left outer join to another collection in the same database, allowing you to combine documents from two collections based on a related field.

intermediate

How does $lookup with a pipeline differ from a simple $lookup with localField and foreignField?

Using $lookup with a pipeline allows you to perform more complex operations like filtering, projecting, and sorting on the joined collection before joining, unlike the simple field-to-field equality match.

intermediate

In a $lookup with pipeline, what does the let field do?

The let field defines variables from the current collection's document that can be used inside the pipeline stages to reference values dynamically during the join.

advanced

Write a simple example of a $lookup stage using pipeline to join orders with products where the product price is greater than 100.

Example:

{
  $lookup: {
    from: "products",
    let: { product_id: "$productId" },
    pipeline: [
      { $match: { $expr: { $and: [ { $eq: ["$_id", "$$product_id"] }, { $gt: ["$price", 100] } ] } } }
    ],
    as: "expensiveProducts"
  }
}

beginner

What is the output of a $lookup stage with pipeline if no matching documents are found in the joined collection?

The output will include the original document with the joined field as an empty array, indicating no matches were found.

What does the as field specify in a $lookup stage?

A The name of the new array field to add the joined documents

B The collection to join with

C The local field to match

D The foreign field to match

The as field defines the name of the array field in the output documents where the joined documents will be stored.

Which operator allows you to use variables from the current document inside the $lookup pipeline?

A $let

B let

C $expr

D $match

The let field defines variables from the current document that can be referenced inside the $lookup pipeline.

In $lookup with pipeline, how do you compare fields from the joined collection and the outer collection?

A Using <code>$eq</code> inside <code>$expr</code> with <code>$$</code> to reference variables

B Using <code>$match</code> with field names directly

C Using <code>$group</code> stage

D Using <code>$project</code> stage

You use $expr with comparison operators like $eq and reference outer variables with $$ inside the $lookup pipeline.

What type of join does $lookup perform in MongoDB?

A Inner join

B Full outer join

C Right outer join

D Left outer join

$lookup performs a left outer join, meaning all documents from the left collection are included, with matching documents from the right collection if available.

If you want to filter joined documents in $lookup, which feature should you use?

A localField and foreignField

B as field

C pipeline with $match

D sort stage

Using pipeline with a $match stage inside $lookup lets you filter the joined documents before adding them.

Explain how $lookup with pipeline works and why it is useful compared to a simple $lookup.

Think about how you can filter and transform joined data before adding it.

Describe the role of the let and pipeline fields inside a $lookup stage.

Consider how variables help connect fields from two collections.

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