{
  "$type": "site.standard.document",
  "bskyPostRef": {
    "cid": "bafyreigs2rz33jlvzgxq7whgsditqde2ghnzdnvarm4bgbr7bt5v5a6nhi",
    "uri": "at://did:plc:5sgu76a53rz3n6unbykmovqy/app.bsky.feed.post/3mloh7lhfad62"
  },
  "description": "The Aggregation Pipeline is MongoDB's framework for transforming and combining documents through a sequence of stages, conceptually similar to a SQL SELECT with GROUP BY, JOIN, and window functions. A pipeline is an array of stage operators applied in order; the output of one stage feeds the next.\n\n\nCommon stages\n\n * $match: filter documents (equivalent to WHERE)\n * $project: reshape and select fields (equivalent to SELECT)\n * $group: group and aggregate (equivalent to GROUP BY)\n * $sort: sort d",
  "path": "/engineering-glossary/aggregation-pipeline-mongodb-queries/",
  "publishedAt": "2026-05-12T18:46:38.000Z",
  "site": "https://sahilkapoor.com",
  "tags": [
    "MongoDB",
    "Document Database",
    "NoSQL",
    "SQL",
    "BSON",
    "MongoDB Data Modeling: How to Design Schemas for Real-World Applications"
  ],
  "textContent": "**The Aggregation Pipeline** is MongoDB's framework for transforming and combining documents through a sequence of stages, conceptually similar to a SQL `SELECT` with `GROUP BY`, `JOIN`, and window functions. A pipeline is an array of stage operators applied in order; the output of one stage feeds the next.\n\n## Common stages\n\n  * `$match`: filter documents (equivalent to `WHERE`)\n  * `$project`: reshape and select fields (equivalent to `SELECT`)\n  * `$group`: group and aggregate (equivalent to `GROUP BY`)\n  * `$sort`: sort documents (equivalent to `ORDER BY`)\n  * `$limit`, `$skip`: pagination\n  * `$lookup`: left outer join to another collection\n  * `$unwind`: explode array fields into one document per element\n  * `$facet`: run multiple sub-pipelines in parallel on the same input\n  * `$graphLookup`: recursive graph traversal\n  * `$merge`, `$out`: write results to another collection\n\n\n\n## Performance considerations\n\n  * Place `$match` and `$project` early to reduce documents and fields flowing downstream.\n  * Use indexes to support early `$match` and `$sort` stages.\n  * `$lookup` is expensive; pre-denormalising data sometimes beats join-style queries.\n  * Use `explain()` to inspect the executed plan and see which stages used indexes.\n\n\n\n🔗\n\n**Related Terms**\nMongoDB, Document Database, NoSQL, SQL, BSON\n\n📖\n\n**Further Reading**\nMongoDB Data Modeling: How to Design Schemas for Real-World Applications",
  "title": "Aggregation Pipeline",
  "updatedAt": "2026-05-13T19:11:05.358Z"
}