Aggregation pipeline nima ne?

Question

Accepted Answer

**Aggregation pipeline** shine MongoDB's powerful framework for **data processing and analysis** — yana wuce documents ta jerin **stages**, kwallon yana canza data (filtering, grouping, computing). Ita ce MongoDB's equivalent na SQL's GROUP BY da hadaddun analytical queries, amma mai sassauci sosai.

## The pipeline concept: stages transform data

```js
db.orders.aggregate([
  { $match: { status: "completed" } },          // 1. FILTER (like WHERE)
  { $group: {                                    // 2. GROUP and aggregate
      _id: "$customerId",                         //    group by customerId
      total: { $sum: "$amount" },                 //    sum amounts per customer
      count: { $sum: 1 }                          //    count orders
  }},
  { $sort: { total: -1 } },                       // 3. SORT by total
  { $limit: 10 }                                  // 4. top 10
]);
// documents flow through each stage; each stage's output feeds the next
```

Documents suna gudu ta stages a jerin — kwallon stage yana canza su da yana wuce sakamakon gida na gida. Wannan pipeline yana lissafin per-customer totals, sorted, top 10 — kamar SQL GROUP BY query.

## Common pipeline stages

```text
$match    → filter documents (like WHERE) — put EARLY to reduce data
$group    → group by a key and aggregate ($sum, $avg, $max, $min, $push, etc.)
$sort     → order documents
$project  → reshape documents (include/exclude/compute fields)
$limit / $skip → pagination
$unwind   → deconstruct an array field into multiple documents (one per element)
$lookup   → JOIN with another collection
$addFields → add computed fields
$count    → count documents
```

## $lookup — joining collections

```js
// $lookup performs a left-outer JOIN to another collection
db.orders.aggregate([
  { $lookup: {
      from: "customers",
      localField: "customerId",
      foreignField: "_id",
      as: "customer"           // joined customer data added as an array field
  }}
]);
```

`$lookup` yana bubuwa joins (waɗanda MongoDB in jiya kewayon embedding) — mai amfani lokacin da kake bukata haduwa da collections.

## $unwind — expand arrays

```js
// turn one document with an array into multiple documents (one per array element)
db.posts.aggregate([
  { $unwind: "$tags" },                           // one doc per tag
  { $group: { _id: "$tags", count: { $sum: 1 } } } // count posts per tag
]);
```

## Me yake da mahimmanci

Aggregation pipeline ita ce ɗaya daga cikin MongoDB's most powerful and important features — ita shine primary tool don **data analysis, reporting, da complex transformations**, saboda haka fahimtar ta shine mahalaci don serious MongoDB work jiya daga basic queries.

Pipeline's **stage-based model** (documents suna gudu ta sequential transformation stages, kwallon filtering, grouping, reshaping, ko computing) shine conceptually elegant da yana bubuwa sophisticated data processing da simple `find` queries ba zai iya — ita shine MongoDB's equivalent na SQL's GROUP BY da analytical queries, amma mai sassauci da composable sosai.

Fahimtar common stages — **`$match`** (filtering, ideally early don rage data), **`$group`** (grouping da aggregating tare da `$sum`, `$avg`, da sauransu — ainihin analytics), **`$sort`/`$limit`** (ordering da pagination), **`$project`** (reshaping documents), **`$unwind`** (expanding arrays zuwa documents — mahimmanci don analyzing array data, gama a MongoDB), da **`$lookup`** (joining collections — bubuwa joins MongoDB in jiya kewayon) — yana rufe toolkit don real data processing.

Pipeline yana kullace reporting, analytics, data transformation, da complex queries (aggregations, joins, array processing) da gama ne bukatuwa a applications.

Saboda data analysis da complex querying suna da mahimmanci amfani na kowane database, da saboda aggregation pipeline shine MongoDB's powerful, flexible mechanism don su (tafi sosai jiya daga basic CRUD), fahimtar aggregation pipeline — stage model da key stages ($match, $group, $sort, $project, $unwind, $lookup) — shine mahalaci, frequently-applied MongoDB knowledge don analytics, reporting, da complex data transformation, hallmark na advanced MongoDB skill da ɗaya daga cikin most important MongoDB topics don samun real analytical value daga database.