FastAPI Backend Development Bootcamp · Ders

Toplama Akışları ve Karmaşık Sorgular

Analiz tarzı uç noktaları verimli biçimde desteklemek için filtreleme, gruplama ve toplama akışlarını çalıştırın.

Toplama Akışları ve Karmaşık Sorgular, CoddyKit'te ücretsiz bir FastAPI Backend Development Bootcamp dersidir. Bu, 4 dersinin 3. dersidir. Aşağıdan dersin tamamını ücretsiz okuyabilir, sonra tarayıcıda yerleşik kod editörü ve 7/24 yapay zeka koçu ile uygulamalı olarak pratik yapabilirsin. Bu, FastAPI Backend Development Bootcamp öğrenme yolunun bir parçasıdır ve ilerlemeniz web ve CoddyKit uygulaması arasında senkronize olur. FastAPI Backend Development Bootcamp kursu toplamda 4 dersten oluşur.

Bu dersin bazı bölümleri henüz çevrilmemiş olup İngilizce olarak gösterilmektedir.

Why Aggregation Pipelines?

Simple find queries return documents as-is. But analytics endpoints often need grouped, computed, and reshaped data: total revenue per month, average rating per product, top 10 active users.

MongoDB's aggregation pipeline runs this work inside the database, so you ship only the final result over the wire instead of pulling thousands of documents into Python and looping.

Pipeline = an ordered list of stages
Each stage takes a stream of documents in and emits documents out
Beanie exposes it through Document.aggregate(pipeline)

Our Beanie Models

Throughout this lesson we use an Order document. Each order has a customer, a status, a total amount, and a created timestamp. We'll build analytics endpoints on top of it.

Beanie documents subclass beanie.Document, which is itself a Pydantic model bound to a MongoDB collection.

from datetime import datetime
from beanie import Document
from pydantic import Field

class Order(Document):
    customer_id: str
    status: str  # "paid", "pending", "cancelled"
    total: float
    created_at: datetime = Field(default_factory=datetime.utcnow)

    class Settings:
        name = "orders"

The $match Stage

$match filters documents, exactly like a find query. Place it as early as possible so later stages process fewer documents and any indexes can be used.

This pipeline keeps only paid orders. Beanie's aggregate takes a plain list of dicts and runs it against the collection.

pipeline = [
    {"$match": {"status": "paid"}}
]

paid_orders = await Order.aggregate(pipeline).to_list()

The $group Stage

$group is the heart of analytics. It buckets documents by an _id expression and computes accumulators over each bucket.

$sum — total of a field (or count with $sum: 1)
$avg, $min, $max
$push / $addToSet — collect values into an array

Here we compute total revenue and order count per customer.

pipeline = [
    {"$match": {"status": "paid"}},
    {"$group": {
        "_id": "$customer_id",
        "revenue": {"$sum": "$total"},
        "order_count": {"$sum": 1},
    }},
]

rows = await Order.aggregate(pipeline).to_list()
# [{"_id": "c1", "revenue": 240.0, "order_count": 3}, ...]

Field Paths vs Literals

Inside aggregation expressions, a string starting with $ is a field path (read the value of that field). A plain string is a literal.

"$total" → the value of the total field
"total" → the literal string "total"
{"$sum": 1} → add the literal 1 for every document = a count

Mixing these up is the #1 beginner mistake. $sum: "$total" sums amounts; $sum: 1 counts rows.

Sorting and Limiting Results

Add $sort and $limit after grouping to build a leaderboard. Sort uses 1 for ascending and -1 for descending.

This returns the top 5 customers by revenue — a classic analytics endpoint payload.

pipeline = [
    {"$match": {"status": "paid"}},
    {"$group": {
        "_id": "$customer_id",
        "revenue": {"$sum": "$total"},
    }},
    {"$sort": {"revenue": -1}},
    {"$limit": 5},
]

top_customers = await Order.aggregate(pipeline).to_list()

Reshaping with $project

$project chooses which fields to keep and lets you rename or compute new ones. After a $group the bucket key lives in _id, which is rarely the name your API consumers expect.

Here we rename _id to customer_id and drop the default _id from the output.

pipeline = [
    {"$group": {
        "_id": "$customer_id",
        "revenue": {"$sum": "$total"},
    }},
    {"$project": {
        "_id": 0,
        "customer_id": "$_id",
        "revenue": 1,
    }},
]

Mapping Results to a Pydantic Model

Aggregation returns raw dicts, not Order documents (the shape changed). Pass a projection_model so Beanie validates each row into a typed Pydantic model — perfect for a FastAPI response_model.

from pydantic import BaseModel

class CustomerRevenue(BaseModel):
    customer_id: str
    revenue: float

pipeline = [
    {"$group": {"_id": "$customer_id", "revenue": {"$sum": "$total"}}},
    {"$project": {"_id": 0, "customer_id": "$_id", "revenue": 1}},
    {"$sort": {"revenue": -1}},
]

results = await Order.aggregate(
    pipeline, projection_model=CustomerRevenue
).to_list()  # List[CustomerRevenue]

Grouping by Date with $dateToString

For time-series analytics, group by a formatted date. $dateToString turns a timestamp into a string bucket like "2026-06" for monthly revenue.

The result is ideal for charting endpoints: one row per month, sorted chronologically.

pipeline = [
    {"$match": {"status": "paid"}},
    {"$group": {
        "_id": {"$dateToString": {
            "format": "%Y-%m", "date": "$created_at"
        }},
        "revenue": {"$sum": "$total"},
    }},
    {"$sort": {"_id": 1}},
]

monthly = await Order.aggregate(pipeline).to_list()

Wiring It Into a FastAPI Endpoint

Put the pipeline behind an async route. Because the aggregation runs in MongoDB, the handler stays tiny and fast even over millions of orders.

Using projection_model as the response_model gives you automatic validation and OpenAPI docs.

from fastapi import APIRouter

router = APIRouter()

@router.get("/analytics/top-customers", response_model=list[CustomerRevenue])
async def top_customers(limit: int = 5):
    pipeline = [
        {"$match": {"status": "paid"}},
        {"$group": {"_id": "$customer_id", "revenue": {"$sum": "$total"}}},
        {"$project": {"_id": 0, "customer_id": "$_id", "revenue": 1}},
        {"$sort": {"revenue": -1}},
        {"$limit": limit},
    ]
    return await Order.aggregate(pipeline, projection_model=CustomerRevenue).to_list()

Modeling a Pipeline in Pure Python

The pipeline pattern — match, group, sum — is just data transformation. Here is the same logic in plain Python so you can see what MongoDB does internally: filter, bucket by key, accumulate a sum.

In production MongoDB does this far faster and with indexes, but understanding the shape helps you write correct stages.

orders = [
    {"customer_id": "c1", "status": "paid", "total": 100.0},
    {"customer_id": "c2", "status": "paid", "total": 40.0},
    {"customer_id": "c1", "status": "paid", "total": 60.0},
    {"customer_id": "c2", "status": "pending", "total": 999.0},
]

revenue = {}
for o in orders:
    if o["status"] != "paid":  # $match
        continue
    revenue[o["customer_id"]] = revenue.get(o["customer_id"], 0) + o["total"]  # $group + $sum

top = sorted(revenue.items(), key=lambda kv: kv[1], reverse=True)  # $sort
for customer_id, total in top:
    print(f"{customer_id}: {total}")

Quick Check

You want total revenue per customer, counting only paid orders. Which pipeline is correct?

Recap

You can now build analytics-style endpoints with Beanie aggregation pipelines:

$match early to filter and use indexes
$group with accumulators ($sum, $avg, $sum: 1 for counts)
Remember field paths need a leading $; plain strings are literals
$sort + $limit for leaderboards, $project to reshape and rename _id
$dateToString for time-series buckets
Pass projection_model to map raw rows into typed Pydantic models for clean FastAPI response_model output

The pipeline runs inside MongoDB, keeping your handlers small and your endpoints fast.

Sıkça Sorulan Sorular

“Toplama Akışları ve Karmaşık Sorgular” dersi ücretsiz mi?

Evet — “Toplama Akışları ve Karmaşık Sorgular” dersin tüm metni burada web'de ücretsiz olarak okunabilir. Etkileşimli olarak pratik yapmak (yerleşik kod editörü ve 7/24 yapay zeka koçu) ve FastAPI Backend Development Bootcamp kursunun geri kalanını açmak için CoddyKit PRO'ya yükselt. FastAPI Backend Development Bootcamp kursu toplamda 4 dersten oluşur.

“Toplama Akışları ve Karmaşık Sorgular” dersinde ne öğreneceğim?

Analiz tarzı uç noktaları verimli biçimde desteklemek için filtreleme, gruplama ve toplama akışlarını çalıştırın. FastAPI Backend Development Bootcamp ile uygulamalı kodu tarayıcıda doğrudan çalıştırarak pratik yaparsın ve 7/24 yapay zeka koçu dersi çalışırken sorularını yanıtlar.

FastAPI Backend Development Bootcamp öğrenmeye başlamak için deneyim gerekli mi?

Önceden deneyim gerekmez. CoddyKit'te FastAPI Backend Development Bootcamp, başlangıçtan ileri seviyeye kadar yapılandırıldığı için buradan başlayabilir veya başından başlayıp kendi hızında ilerleme yapabilirsin. Bu, 4 dersinin 3. dersidir.

“Toplama Akışları ve Karmaşık Sorgular” dersi ne kadar sürer?

Çoğu CoddyKit dersi yaklaşık 5–10 dakika sürer. Her biri kısa ve etkileşimli olduğu için sabit ilerleme yaparsın ve web ile uygulama arasında tam olarak bıraktığın yerden devam edebilirsin.

Bu FastAPI Backend Development Bootcamp dersinde kod yazıp çalıştırabilir miyim?

Evet. Her FastAPI Backend Development Bootcamp dersi yerleşik bir kod editörü içerir, bu sayede tarayıcıda gerçek kod yazıp çalıştırabilir ve anlık yapay zeka geri bildirimi alırsın — yerel kurulum gerekli değildir.

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