Module Review: Aggregation

[!NOTE] This module explores the core principles of Module Review: Aggregation, deriving solutions from first principles and hardware constraints to build world-class, production-ready expertise.

1. Key Takeaways

  • Pipelines: Data processing in MongoDB is done via a pipeline of stages, processed in order.
  • Filter First: Always use $match as early as possible to utilize indexes and reduce data volume.
  • The Big 5: Master $match, $group, $project, $unwind, and $sort to handle 90% of use cases.
  • Blocking vs Streaming: Be aware that $sort and $group block execution until all data is received, subject to a 100MB memory limit (unless allowDiskUse: true).
  • Advanced Features: Use $lookup for joins, $bucket for histograms, and $facet for multi-pipeline dashboards.

2. Interactive Flashcards

Test your knowledge by clicking on the cards to reveal the answers.

Why should you place `$match` at the start of a pipeline?
1. To utilize indexes (performance). 2. To reduce the number of documents subsequent stages need to process.
Which stage is used to deconstruct an array field into multiple documents?
`$unwind`
What is the default memory limit for blocking stages like `$sort`?
100MB. If exceeded, the query fails unless `{ allowDiskUse: true }` is specified.
How do you perform a Left Outer Join in MongoDB?
Using the `$lookup` stage.
What does `$facet` allow you to do?
It allows you to run multiple aggregation pipelines in parallel on the same set of input documents (great for dashboards).
What is the risk of using `$lookup` on a non-indexed foreign field?
MongoDB must perform a full collection scan on the target collection for *every* input document, which is extremely slow.
What is the difference between `$bucket` and `$bucketAuto`?
`$bucket` requires you to define boundaries manually. `$bucketAuto` automatically determines boundaries to evenly distribute documents.
How does the optimizer optimize a sequence of `$sort` followed by `$match`?
It reorders them to `$match` first (to reduce dataset) and then `$sort`.

3. Cheat Sheet: SQL vs Aggregation

If you are coming from a Relational Database background, use this mapping.

SQL Concept Aggregation Stage Description
WHERE $match Filter documents
GROUP BY $group Group documents
HAVING $match Filter groups (place after $group)
SELECT $project Pick/rename fields
ORDER BY $sort Sort results
LIMIT $limit Limit number of results
OFFSET $skip Skip results
JOIN $lookup Left outer join
UNION ALL $unionWith Combine two collections

4. Glossary

For definitions of terms like Accumulator, Pipeline, and Cursor, check out the MongoDB Glossary.

5. Next Steps

Now that you’ve mastered the aggregation pipeline, it’s time to learn how to make your queries lightning fast with Indexing.

Module 5: Indexing