Author: Xavier Fok

How to scrape OLX Brazil and LATAM marketplaces

Scrape OLX Brazil and you tap into the largest classifieds platform in Latin America, covering used cars, real estate, jobs, and general merchandise across Brazil, Argentina, Colombia, Peru, and Ecuador. OLX operates as a horizontal classifieds platform where individual sellers and small businesses publish ads, which makes the data shape fundamentally different from a marketplace like Mercado Libre. The scraping landscape is shaped by three things: a state-level geographic hierarchy that gates listings, an undocumented but stable JSON API behind the search experience, and Cloudflare bot protection that classifies non-Brazilian traffic aggressively.

This guide focuses on OLX Brazil at olx.com.br as the canonical example, with notes on how the patterns transfer to OLX Argentina, OLX Colombia, and the smaller LATAM properties.

Mapping OLX URL and listing structure

OLX Brazil URLs follow a regional hierarchy. National listings live under the root domain, but most search activity is filtered by state. URLs include the state code in the path: https://www.olx.com.br/sp/regiao-de-sao-paulo for Sao Paulo state, https://www.olx.com.br/rj for Rio de Janeiro, and so on for the 27 federative units. Within a state, listings are further filtered by category, sub-category, and city.

Individual ad URLs follow the pattern https://<region>.olx.com.br/<category>/<subcategory>/<title-slug>-<adId>. The trailing adId is the canonical identifier you should store as the primary key. Slugs change when sellers edit ads; adIds are stable for the life of the listing.

The OLX search experience is powered by a JSON API at https://www.olx.com.br/api/relevance/search. The endpoint accepts a category, subcategory, region, and pagination parameters and returns a structured listing payload. This is dramatically more reliable than HTML scraping because the API contract is stable while the visible markup changes regularly.

import httpx

SEARCH_API = "https://www.olx.com.br/api/relevance/search"

HEADERS = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
    "Accept": "application/json",
    "Accept-Language": "pt-BR,pt;q=0.9",
}

async def search_olx(category_id: int, region_code: str, page: int, proxy: str):
    params = {
        "category": category_id,
        "region": region_code,
        "o": page,
        "lim": 50,
    }
    async with httpx.AsyncClient(proxy=proxy, headers=HEADERS, timeout=20) as c:
        r = await c.get(SEARCH_API, params=params)
        if r.status_code == 200:
            return r.json().get("ad_list", [])
        return []

The response includes list_id (the canonical ad id), subject (title), body (description), price in BRL, category_id, category_name, state, city, seller_name, seller_phone_status, images, and created_at. For most analytical use cases this is sufficient and you do not need to fetch the individual ad detail page.

Brazilian proxy strategy

OLX Brazil’s bot detection is aggressive against non-Brazilian IPs. Cloudflare profiles the visitor IP geography, and any IP outside Brazil receives elevated scrutiny. For sustained scraping, Brazilian residential or mobile IPs are required. Vivo, Claro, and TIM mobile pools work well. Brazilian residential pools through major providers also work but cost more per gigabyte than the mobile equivalent at scale.

For workloads under 10,000 ad reads per day, a small Brazilian residential pool with sticky 15-minute sessions is sufficient. For higher volumes, dedicated mobile ports are the cleaner path because they can sustain higher request rates without challenges.

Region	Recommended proxy origin	Tolerance per IP
Sao Paulo state	Brazilian mobile or residential in SP	200 req/hr per IP
Rio de Janeiro	Brazilian residential anywhere	200 req/hr per IP
Northeast states	Brazilian residential	250 req/hr per IP
South states	Brazilian residential	250 req/hr per IP

Cross-state IPs work fine for any state target because the OLX bot logic does not enforce intra-Brazil geographic consistency. The geographic check is at the country level only.

Crawling categories and pagination

OLX exposes roughly 80 top-level categories and 400 sub-categories across the Brazilian site. The full category tree is published at https://www.olx.com.br/api/categories and changes slowly enough that you can cache it weekly. For each category, the search API allows pagination up to roughly 100 pages of 50 ads each, for a maximum of 5,000 ads per query.

For deeper coverage of large categories like used cars or real estate, decompose by state and then by city. The cities of Sao Paulo, Rio de Janeiro, Belo Horizonte, Salvador, Brasilia, Curitiba, and Recife collectively cover roughly 40% of total OLX listings; the remaining listings spread across hundreds of smaller cities.

async def crawl_state_category(state: str, category_id: int, proxy_pool, max_pages: int = 100):
    all_ads = []
    for page in range(1, max_pages + 1):
        proxy = proxy_pool.next()
        ads = await search_olx(category_id, state, page, proxy)
        if not ads:
            break
        all_ads.extend(ads)
    return all_ads

A national daily snapshot across all states and categories is roughly 10-15 million ad reads. That is achievable on a moderately-sized mobile proxy pool with parallelism across 20-50 concurrent sessions. Plan for 8-12 hours of wall-clock time for the full sweep.

Phone number redaction and contact extraction

OLX masks seller phone numbers behind a click-to-reveal flow. The seller_phone_status field in the search response indicates whether a phone is published, but the actual number is only revealed by hitting the contact API with an authenticated session. From a privacy perspective, treat the phone number as personal data and avoid collecting it unless you have a clear lawful basis and a documented use case.

For analytics that need to dedupe sellers across many ads, hash a stable seller identifier (the OLX user_id is published in the ad detail) rather than collecting raw phone numbers. The hashed user_id is sufficient for seller concentration analysis without creating personal-data exposure.

Schema for OLX classifieds snapshots

CREATE TABLE olx_ad_snapshot (
    snapshot_at TIMESTAMP NOT NULL,
    list_id BIGINT NOT NULL,
    state VARCHAR(2) NOT NULL,
    city VARCHAR(64),
    category_id INT,
    subject TEXT,
    price_brl DECIMAL(12,2),
    seller_user_id_hash VARCHAR(64),
    image_count INT,
    created_at TIMESTAMP,
    is_active BOOLEAN,
    PRIMARY KEY (snapshot_at, list_id)
);
CREATE INDEX olx_state_cat_idx ON olx_ad_snapshot(state, category_id);

For longitudinal classifieds analytics, the most valuable derived metric is time-on-market: the number of days an ad stays active before being delisted (because the item sold or the seller withdrew). Compute this from the diff between consecutive snapshots. Time-on-market by category, by price band, and by state is the headline insight in any classifieds intelligence product.

For broader pattern guidance on classifieds-style scrapers, see our residential proxy provider ranking and our Mercado Libre Mexico scraping guide.

Detecting and routing around CAPTCHA challenges on OLX

When OLX flags your traffic, the response is usually a Cloudflare or vendor interrogation page rather than a clean HTTP error. Your scraper needs to detect this content-type swap explicitly. Look for the signature cf-mitigated header, the presence of __cf_chl_ cookies, or HTML containing Just a moment.... Treat any of these as a soft block.

def is_challenged(response) -> bool:
    if response.status_code in (403, 503):
        return True
    if "cf-mitigated" in response.headers:
        return True
    if "__cf_chl_" in response.headers.get("set-cookie", ""):
        return True
    body = response.text[:2000].lower()
    return "just a moment" in body or "checking your browser" in body

When you detect a challenge, do not retry on the same IP for at least 30 minutes. Mark that IP as cooling and route subsequent requests to a different IP in your pool. Aggressive retries on a flagged IP cause the cooling window to extend and can lead to long-term blacklisting of your subnet. For pages that absolutely must be fetched, have a fallback path that uses a headless browser with a real Brazil residential IP. The browser path costs more per page but solves the small percentage of challenges that the API path cannot handle. Most production setups maintain a 95/5 split between the lightweight HTTP path and the browser fallback path.

Working with BRL pricing and FX normalization

Pricing on OLX is denominated in BRL, and any cross-market analysis requires careful FX normalization. The naive approach of converting at scrape time using a live FX feed introduces noise into your trend lines because exchange rate movements get conflated with real price changes. Store the price in local BRL and apply FX conversion at query time using a daily reference rate.

CREATE TABLE fx_rates (
    rate_date DATE NOT NULL,
    base_ccy VARCHAR(3) NOT NULL,
    quote_ccy VARCHAR(3) NOT NULL,
    rate DECIMAL(18,8) NOT NULL,
    PRIMARY KEY (rate_date, base_ccy, quote_ccy)
);

Source the daily rates from a reliable feed such as the European Central Bank reference rates or your bank wholesale feed. Avoid scraping retail FX rates because they include the bank spread and produce inconsistent comparisons. For analyses that span multiple years, also account for currency revaluation events that occasionally happen in emerging markets.

Comparing OLX to other regional marketplaces

Marketplace	Country focus	Catalogue scale	Bot strictness
OLX	Brazil	Large	High
Mercado Libre	Adjacent markets	Medium	Medium
Webmotors	Adjacent markets	Smaller	Lower

Cross-marketplace analyses help separate platform-specific dynamics from genuine market trends. If a price drops on OLX but stays flat across the comparable competitors, that is a platform-driven event rather than a market-wide signal. Your scraping pipeline should ingest from at least three platforms in any market where you intend to publish category insights.

Operational monitoring and alerting

Every production scraper needs three monitoring layers regardless of target. The first is per-IP success rate over a 5-minute window, alerting if any IP drops below 80%. The second is parser error rate, alerting if more than 1% of fetched pages fail to extract the canonical fields. The third is data freshness, alerting if your downstream consumers see snapshots more than 24 hours old.

import time
from collections import deque

class IPHealthTracker:
    def __init__(self, window_seconds: int = 300):
        self.window = window_seconds
        self.events = {}

    def record(self, ip: str, success: bool):
        bucket = self.events.setdefault(ip, deque())
        now = time.time()
        bucket.append((now, success))
        while bucket and bucket[0][0] < now - self.window:
            bucket.popleft()

    def success_rate(self, ip: str) -> float:
        bucket = self.events.get(ip)
        if not bucket:
            return 1.0
        successes = sum(1 for _, ok in bucket if ok)
        return successes / len(bucket)

Wire this into Prometheus or your existing observability stack so the on-call engineer sees IP degradation as it happens rather than after the daily snapshot fails. For long-running operations against OLX, IP rotation triggered by the health tracker is more reliable than fixed rotation schedules.

Legal and compliance considerations for Brazil

Public product, price, and availability data are generally treated as fair to scrape in most jurisdictions, but Brazil has its own consumer protection and personal data frameworks. Confine your collection to non-personal data: SKU identifiers, prices, descriptions, ratings as aggregates, and seller display names. Avoid collecting individual buyer reviews with names, phone numbers, or email addresses attached, and avoid pulling any data behind a login.

For commercial deployment, document your basis for processing, your data retention period, and your purpose limitation. Most data protection regimes treat scraped public data more favorably when there is a clear lawful basis and the data is not used for direct marketing to identified individuals. The W3C Web Annotation guidance and similar published frameworks remain useful starting points for documenting your approach.

Pipeline orchestration and scheduling

For any non-trivial scraping operation, a dedicated orchestration layer is the difference between a script you babysit and a service that runs unattended. The two strong open-source choices in 2026 are Prefect 3 and Dagster. Both handle the patterns you need: DAG dependencies, retries, observability, secret management, and dynamic fan-out across IPs and categories.

from prefect import flow, task

@task(retries=3, retry_delay_seconds=60)
def fetch_category(category_id: int, page: int):
    return crawl_one_page(category_id, page)

@task
def store_pages(pages: list):
    write_to_db(pages)

@flow(name="olx-daily-sweep")
def daily_sweep(category_ids: list):
    futures = []
    for cid in category_ids:
        for page in range(1, 50):
            futures.append(fetch_category.submit(cid, page))
    pages = [f.result() for f in futures]
    store_pages(pages)

Run the flow on a 6-hour or 24-hour schedule depending on how dynamic the underlying catalogue is. For seasonal markets like apparel where pricing changes daily, a 6-hour cadence catches the meaningful movements without driving up proxy costs unnecessarily. For long-tail categories like books or industrial supplies, daily is sufficient and the cost saving is meaningful.

Sample analytics queries on the collected dataset

Once your snapshots are landing reliably, the analytics layer is where the value materializes. A few queries that consistently come up across OLX datasets:

-- Top 50 SKUs by price drop in the last 7 days
SELECT sku, MIN(selling_price) - MAX(selling_price) AS price_drop
FROM snapshot
WHERE snapshot_at > now() - interval '7 days'
GROUP BY sku
ORDER BY price_drop ASC
LIMIT 50;

-- Stock-out frequency per category
SELECT category_id,
       SUM(CASE WHEN in_stock = 0 THEN 1 ELSE 0 END)::float / COUNT(*) AS oos_rate
FROM snapshot
WHERE snapshot_at > now() - interval '30 days'
GROUP BY category_id
ORDER BY oos_rate DESC;

-- New SKUs first seen in the last 14 days
SELECT sku, MIN(snapshot_at) AS first_seen
FROM snapshot
GROUP BY sku
HAVING MIN(snapshot_at) > now() - interval '14 days'
ORDER BY first_seen DESC;

These three queries alone power most of the dashboards a category manager wants. Add a brand share view, a seller concentration view, and a campaign-frequency view and you have a competitive intelligence product. The collection layer is the prerequisite; the analytics layer is where you create defensible value.

Building robust deduplication across noisy listings

The standard approach uses a three-pass funnel. The first pass groups by exact match on EAN or GTIN where present. The second pass groups by normalized title plus brand using a TF-IDF cosine similarity threshold of 0.85. The third pass groups by image hash similarity using perceptual hashing.

import imagehash
from PIL import Image

def perceptual_hash(image_path: str) -> str:
    img = Image.open(image_path)
    return str(imagehash.phash(img, hash_size=16))

Tune the similarity thresholds against a hand-labeled gold set of 500 to 1,000 known duplicate clusters. Without a gold set, you will either over-merge (collapsing distinct variants) or under-merge (leaving the same product in many groups).

Versioning your scraper for catalogue evolution

Every ecommerce site evolves its catalogue structure regularly. Stamp every snapshot row with the scraper version that produced it. When you deploy a new version of the parser, increment the version number. Downstream analytics can filter by version when they need consistent semantics across a time range, or join across versions when they want long-running trend analysis.

Building robust deduplication across noisy listings

When you scrape any ecommerce marketplace at scale, the long-tail catalogue is full of near-duplicate listings. The same physical product appears under different titles, different sellers, slight variations in pack size, and slightly different image sets. For analytics that try to compute brand share or category trends, deduplication is mandatory and it is harder than it looks. The standard approach uses a three-pass funnel. The first pass groups by exact match on EAN or GTIN where present. The second pass groups by normalized title plus brand using a TF-IDF cosine similarity threshold of 0.85. The third pass groups by image hash similarity using perceptual hashing.

import imagehash
from PIL import Image

def perceptual_hash(image_path: str) -> str:
    img = Image.open(image_path)
    return str(imagehash.phash(img, hash_size=16))

Caching strategy and incremental crawls

Full daily snapshots scale linearly with catalogue size, which becomes expensive at multi-million SKU scale. Most production deployments shift from full snapshots to incremental refreshes after the initial ramp. The pattern uses three signals to decide what to refetch on each cycle. The first signal is freshness deadline. Every SKU has a maximum staleness budget, and any SKU older than its budget gets refreshed. The second signal is volatility. SKUs that have changed price recently get higher refresh priority. The third signal is business priority. SKUs that downstream users actually query get higher refresh priority than dormant SKUs that nobody has looked at in months.

This kind of priority-driven scheduler reduces total request volume by 60-80% compared to blind full snapshots, while keeping the data fresh on the SKUs that actually matter to the business.

FAQ

Can I scrape all LATAM OLX countries with the same code?
The API contract is mostly consistent across OLX Argentina, OLX Colombia, and OLX Peru, but the parameter names and the category trees differ. Build a per-country adapter rather than assuming one code path covers everything. Brazilian-specific date parsing (DD/MM/YYYY) and currency formatting (R$ prefix) need country-specific handlers.

Is the OLX API officially documented?
No. The endpoints described here are the unauthenticated APIs that the public web site uses. They have been stable for several years but they are not contractually supported. Build defensively: log unknown response fields, alert on schema drift, and keep the parser version-stamped.

How does OLX handle ads from professional sellers vs. private sellers?
The ad object includes a professional_ad boolean flag that distinguishes dealer or business listings from individual private sellers. For analytics, separating professional from private listings is essential because the price dynamics and turnover characteristics are very different.

Can I track an ad across multiple snapshots to see if the price changed?
Yes. The list_id is stable for the life of the ad. Comparing the price field across consecutive snapshots gives you the price-change history. About 15-20% of ads see at least one price change before the ad is delisted.

What about OLX motors vs. general merchandise?
OLX Brazil operates a sub-property at autos.olx.com.br for vehicles with additional automotive-specific filters and an inventory model that overlaps with Webmotors. The same API patterns work but with vehicle-specific category IDs and additional fields like make, model, year, and mileage.

To build a broader LATAM classifieds intelligence stack, browse the ecommerce scraping category for tooling reviews, proxy comparisons, and framework deep dives that pair with the patterns above.

May 7, 2026

How to scrape Auchan Drive France grocery data
How to scrape Auchan Drive France grocery data

Scrape Auchan Drive France and you tap into one of the largest hypermarket grocery catalogues in Europe, served through a click-and-collect model that varies prices by store. Auchan operates a federation of physical hypermarkets and Drive pickup points across France, with each Drive store maintaining its own catalogue and its own price list within the corporate pricing framework. The scraping landscape is shaped by three things: postcode-driven store selection that gates every product page, a JSON catalogue API that returns store-specific availability, and a Cloudflare front end that profiles non-French traffic aggressively.

This guide focuses on Auchan Drive specifically, which is the click-and-collect property at www.auchan.fr/drive. Auchan also operates a separate home delivery property and a hypermarket banner catalogue with different APIs.

The store-selection prerequisite

Every Auchan product page requires a store context before it returns prices. The store context is set by selecting a postal code or a specific Drive location, which sets a mag cookie that all subsequent requests must include. Without the store cookie, you get either redirects to the homepage or a stripped-down view with no pricing.
```
import httpx

async def select_store(postcode: str, proxy: str):
    async with httpx.AsyncClient(proxy=proxy, follow_redirects=True, timeout=20) as c:
        # Hit the store selector endpoint to get a session
        r = await c.get(f"https://www.auchan.fr/store-selector?postalCode={postcode}")
        # The mag cookie should now be set
        return c.cookies
```
Different postcodes resolve to different Drive stores. If you want catalogue coverage across all of France, you need to enumerate the Drive store list and rotate through stores in your scraping pipeline. The store list is published as a static JSON at https://www.auchan.fr/api/stores that returns roughly 200 active Drive locations.

The Auchan product API

Once a store is selected, product details come from the catalogue API at https://www.auchan.fr/api/v1/products/<gtin>. The endpoint returns the canonical product object along with the store-specific price, promotion, and stock state.
```
async def fetch_product(gtin: str, store_cookies, proxy: str):
    url = f"https://www.auchan.fr/api/v1/products/{gtin}"
    async with httpx.AsyncClient(proxy=proxy, cookies=store_cookies, timeout=20) as c:
        r = await c.get(url, headers={"Accept": "application/json"})
        if r.status_code == 200:
            return r.json()
        return None
```
The response includes gtin, name, brand, price, pricePerUnit, unit, inStock, promotion, nutritionalInfo, and categoryPath. The pricePerUnit field is the most analytically useful for grocery work because grocery products are sold in highly variable pack sizes and the per-unit price is the only fair comparison across formats.

Crawling the category tree

Auchan’s category tree is exposed at https://www.auchan.fr/api/v1/categories. It returns a nested JSON of department, sub-department, and category nodes. Each terminal category has a slug that is used in listing URLs.

Listing endpoints look like https://www.auchan.fr/api/v1/products?categoryId=<id>&page=<n>&size=50. The pagination cap is 1,000 products per category, which is enough for most categories but requires faceted decomposition for the largest ones (epicerie sucree, boissons).
```
async def crawl_category(category_id: int, store_cookies, proxy_pool, max_pages: int = 20):
    all_items = []
    for page in range(1, max_pages + 1):
        proxy = proxy_pool.next()
        url = "https://www.auchan.fr/api/v1/products"
        params = {"categoryId": category_id, "page": page, "size": 50}
        async with httpx.AsyncClient(proxy=proxy, cookies=store_cookies, timeout=20) as c:
            r = await c.get(url, params=params, headers={"Accept": "application/json"})
            if r.status_code != 200:
                break
            data = r.json()
            items = data.get("products", [])
            if not items:
                break
            all_items.extend(items)
    return all_items
```
For a daily catalogue snapshot across all stores, the work scales linearly with the number of stores you cover. A national snapshot covering 200 stores at 50,000 SKUs each is 10M product reads per day. That is achievable on a small mobile proxy pool with reasonable parallelism, but it requires careful work scheduling.

Multi-store price comparison

The most analytically interesting Auchan dataset is the price variation across stores for the same SKU. Even within Auchan, prices for identical products differ by store based on local competitive pressure, promotional schedules, and regional sourcing. For brand owners, this surfaces where Auchan is using a SKU as a loss leader versus where they are pricing at full margin.
```
CREATE TABLE auchan_snapshot (
    snapshot_at TIMESTAMP NOT NULL,
    store_id VARCHAR(32) NOT NULL,
    gtin VARCHAR(14) NOT NULL,
    price_eur DECIMAL(10,2),
    price_per_unit_eur DECIMAL(12,4),
    unit VARCHAR(8),
    in_stock BOOLEAN,
    promotion_text TEXT,
    PRIMARY KEY (snapshot_at, store_id, gtin)
);
```
A price-variance view across stores reveals interesting patterns. Stores in dense urban Paris consistently price higher than stores in suburban regions. Stores near a major Lidl or Aldi competitor consistently price 5-10% lower on the categories where the competitor is strongest.

Proxy strategy for Auchan

Auchan’s bot detection sits on Cloudflare with additional behavioral analysis. French residential or mobile IPs are required for stable scraping. EU residential pools sometimes work but the success rate is meaningfully lower than France-specific inventory.

For workloads under 5,000 product reads per day, a small French residential pool with sticky sessions of 10-20 minutes is sufficient. For higher volumes, a dedicated mobile port on Orange or SFR is the cleaner path. The mobile IP costs more but its trust score is high enough to sustain 10+ requests per second for hours without challenges.

Rate limits and request shaping

Auchan does not publish rate limits but the observed behavior is consistent: roughly 2 requests per second per IP is the safe sustained rate. Bursts up to 10 requests per second work for short periods. Beyond that you hit Cloudflare challenges that take 10-30 minutes to clear per IP.

The request shaping that performs best on Auchan is store-by-store sequential scraping rather than SKU-by-SKU parallel scraping. Each store has its own session cookie, and rotating store cookies on every request adds overhead without improving throughput. Stick with one store cookie per IP for the duration of that store’s catalogue sweep, then rotate.

Detecting and routing around CAPTCHA challenges on Auchan

When Auchan flags your traffic, the response is usually a Cloudflare or vendor interrogation page rather than a clean HTTP error. Your scraper needs to detect this content-type swap explicitly. Look for the signature cf-mitigated header, the presence of __cf_chl_ cookies, or HTML containing Just a moment.... Treat any of these as a soft block.
```
def is_challenged(response) -> bool:
    if response.status_code in (403, 503):
        return True
    if "cf-mitigated" in response.headers:
        return True
    if "__cf_chl_" in response.headers.get("set-cookie", ""):
        return True
    body = response.text[:2000].lower()
    return "just a moment" in body or "checking your browser" in body
```
When you detect a challenge, do not retry on the same IP for at least 30 minutes. Mark that IP as cooling and route subsequent requests to a different IP in your pool. Aggressive retries on a flagged IP cause the cooling window to extend and can lead to long-term blacklisting of your subnet.

For pages that absolutely must be fetched, have a fallback path that uses a headless browser with a real France residential IP. The browser path costs more per page but solves the small percentage of challenges that the API path cannot handle. Most production setups maintain a 95/5 split between the lightweight HTTP path and the browser fallback path.

Working with EUR pricing and FX normalization

Pricing in France is denominated in EUR, and any cross-market analysis requires careful FX normalization. The naive approach of converting at scrape time using a live FX feed introduces noise into your trend lines because exchange rate movements get conflated with real price changes. Store the price in local EUR and apply FX conversion at query time using a daily reference rate.
```
CREATE TABLE fx_rates (
    rate_date DATE NOT NULL,
    base_ccy VARCHAR(3) NOT NULL,
    quote_ccy VARCHAR(3) NOT NULL,
    rate DECIMAL(18,8) NOT NULL,
    PRIMARY KEY (rate_date, base_ccy, quote_ccy)
);
```
Source the daily rates from a reliable feed such as the European Central Bank reference rates or your bank’s wholesale feed. Avoid scraping retail FX rates because they include the bank’s spread and produce inconsistent comparisons.

Comparing Auchan to other regional marketplaces

Marketplace Country focus Catalogue scale Bot strictness

Auchan Drive France Large High

Carrefour Drive Adjacent markets Medium Medium

Leclerc Drive Adjacent markets Smaller Lower

Cross-marketplace analyses help separate platform-specific dynamics from genuine market trends. If a price drops on Auchan Drive but stays flat across the comparable competitors, that is a platform-driven event rather than a market-wide signal. Your scraping pipeline should ingest from at least three platforms in any market where you intend to publish category insights.

Operational monitoring and alerting

Every production scraper needs three monitoring layers regardless of target. The first is per-IP success rate over a 5-minute window, alerting if any IP drops below 80%. The second is parser error rate, alerting if more than 1% of fetched pages fail to extract the canonical fields. The third is data freshness, alerting if your downstream consumers see snapshots more than 24 hours old.
```
import time
from collections import deque

class IPHealthTracker:
    def __init__(self, window_seconds: int = 300):
        self.window = window_seconds
        self.events = {}

    def record(self, ip: str, success: bool):
        bucket = self.events.setdefault(ip, deque())
        now = time.time()
        bucket.append((now, success))
        while bucket and bucket[0][0] < now - self.window:
            bucket.popleft()

    def success_rate(self, ip: str) -> float:
        bucket = self.events.get(ip)
        if not bucket:
            return 1.0
        successes = sum(1 for _, ok in bucket if ok)
        return successes / len(bucket)
```
Wire this into Prometheus or your existing observability stack so the on-call engineer sees IP degradation as it happens rather than after the daily snapshot fails.

Legal and compliance considerations for France

Public product, price, and availability data are generally treated as fair to scrape in most jurisdictions, but France has its own consumer protection and personal data frameworks. Confine your collection to non-personal data: SKU identifiers, prices, descriptions, ratings as aggregates, and seller display names. Avoid collecting individual buyer reviews with names, phone numbers, or email addresses attached, and avoid pulling any data behind a login.

For commercial deployment, document your basis for processing, your data retention period, and your purpose limitation. Most data protection regimes treat scraped public data more favorably when there is a clear lawful basis and the data is not used for direct marketing to identified individuals. The W3C Web Annotation guidance and similar published frameworks remain useful starting points for documenting your approach.

Pipeline orchestration and scheduling

For any non-trivial scraping operation, a dedicated orchestration layer is the difference between a script you babysit and a service that runs unattended. The two strong open-source choices in 2026 are Prefect 3 and Dagster. Both handle the patterns you need: DAG dependencies, retries, observability, secret management, and dynamic fan-out across IPs and categories.
```
from prefect import flow, task

@task(retries=3, retry_delay_seconds=60)
def fetch_category(category_id: int, page: int):
    return crawl_one_page(category_id, page)

@task
def store_pages(pages: list):
    write_to_db(pages)

@flow(name="auchan-daily-sweep")
def daily_sweep(category_ids: list):
    futures = []
    for cid in category_ids:
        for page in range(1, 50):
            futures.append(fetch_category.submit(cid, page))
    pages = [f.result() for f in futures]
    store_pages(pages)
```
Run the flow on a 6-hour or 24-hour schedule depending on how dynamic the underlying catalogue is. For seasonal markets like apparel, a 6-hour cadence catches the meaningful movements without driving up proxy costs unnecessarily.

Sample analytics queries on the collected dataset

Once your snapshots are landing reliably, the analytics layer is where the value materializes. A few queries that consistently come up across Auchan datasets:
```
-- Top 50 SKUs by price drop in the last 7 days
SELECT sku, MIN(selling_price) - MAX(selling_price) AS price_drop
FROM snapshot
WHERE snapshot_at > now() - interval '7 days'
GROUP BY sku
ORDER BY price_drop ASC
LIMIT 50;

-- Stock-out frequency per category
SELECT category_id,
       SUM(CASE WHEN in_stock = 0 THEN 1 ELSE 0 END)::float / COUNT(*) AS oos_rate
FROM snapshot
WHERE snapshot_at > now() - interval '30 days'
GROUP BY category_id
ORDER BY oos_rate DESC;
```
Add a brand share view, a seller concentration view, and a campaign-frequency view and you have a competitive intelligence product. The collection layer is the prerequisite; the analytics layer is where you create defensible value.

Building robust deduplication across noisy listings

When you scrape any ecommerce marketplace at scale, the long-tail catalogue is full of near-duplicate listings. The same physical product appears under different titles, different sellers, slight variations in pack size, and slightly different image sets. For analytics that try to compute brand share or category trends, deduplication is mandatory and it is harder than it looks.

The standard approach uses a three-pass funnel. The first pass groups by exact match on EAN or GTIN where present. The second pass groups by normalized title plus brand using a TF-IDF cosine similarity threshold of 0.85. The third pass groups by image hash similarity using perceptual hashing. Each pass merges groups produced by the previous pass.
```
import hashlib
from PIL import Image
import imagehash

def perceptual_hash(image_path: str) -> str:
    img = Image.open(image_path)
    return str(imagehash.phash(img, hash_size=16))

def normalize_title(title: str) -> str:
    title = title.lower()
    for token in ["[free shipping]", "[same day]", "(new)", "*sale*"]:
        title = title.replace(token, "")
    return " ".join(title.split())
```
Tune the similarity thresholds against a hand-labeled gold set of 500 to 1,000 known duplicate clusters. Without a gold set, you will either over-merge (collapsing distinct variants) or under-merge (leaving the same product in many groups). Both failure modes break downstream analytics.

Versioning your scraper for catalogue evolution

Every ecommerce site evolves its catalogue structure regularly. New attribute fields appear, old fields are deprecated, category trees are reorganized, and pricing display logic changes. Your scraper code has to evolve with these changes, and a versioning pattern that keeps old data interpretable is critical.

The pattern that works best is to stamp every snapshot row with the scraper version that produced it. When you deploy a new version of the parser, increment the version number. Downstream analytics can filter by version when they need consistent semantics across a time range, or join across versions when they want long-running trend analysis.
```
ALTER TABLE snapshot ADD COLUMN scraper_version VARCHAR(16);
CREATE INDEX scraper_version_idx ON snapshot(scraper_version);
```
Pair this with a small registry table that documents what each scraper version did differently. When a downstream user asks why a particular metric jumped on a specific date, the version registry usually has the answer.

Caching strategy and incremental crawls

Full daily snapshots scale linearly with catalogue size, which becomes expensive at multi-million SKU scale. Most production deployments shift from full snapshots to incremental refreshes after the initial ramp. The pattern uses three signals to decide what to refetch on each cycle.

The first signal is freshness deadline. Every SKU has a maximum staleness budget (say 24 hours), and any SKU older than its budget gets refreshed.

The second signal is volatility. SKUs that have changed price recently get higher refresh priority because they are more likely to change again. SKUs that have been stable for weeks can drop to a longer refresh interval.

The third signal is business priority. SKUs that downstream users actually query (tracked by query logs) get higher refresh priority than dormant SKUs that nobody has looked at in months.
```
def schedule_refresh(sku_id: int, last_changed_at, last_queried_at, last_fetched_at) -> int:
    """returns priority score; higher = refresh sooner"""
    age = (now - last_fetched_at).total_seconds() / 3600
    volatility = 10 if (now - last_changed_at).days < 7 else 1
    relevance = 5 if (now - last_queried_at).days < 1 else 1
    return age * volatility * relevance
```
This kind of priority-driven scheduler reduces total request volume by 60-80% compared to blind full snapshots, while keeping the data fresh on the SKUs that actually matter to the business.

Common pitfalls when scraping Auchan Drive

Three issues recur. The first is store-level price variance. Auchan Drive prices are set per fulfillment store, not nationally. The same SKU can vary by 5-15% between Lille and Marseille. A scraper that does not pin a store_id averages across stores and loses the geographic price signal that makes Drive data analytically interesting. Always store the drive_id alongside every price.

The second is unit-of-sale ambiguity. Fresh produce is sold by weight (per kilo) but packaged in approximate units. The product page shows both prix au kilo and prix par piece. The realized cost depends on the actual weight at checkout. For trend analysis, pin to prix au kilo and ignore the per-piece estimate.

The third is loyalty-program price drift. Auchan Waaoh card holders see different prices for some promotional SKUs. Anonymous scraping captures the public price. Cardholder scraping requires session cookies that expire every few hours. Decide which population your analysis serves and stay consistent.

FAQ

Why does the same product show different prices on different Auchan Drive stores?
Auchan Drive stores set their own pricing within a corporate framework. Local competition (proximity to a Leclerc, Lidl, or Carrefour) and regional sourcing costs both influence the per-store price. Multi-store snapshots are the only way to see the full pricing landscape.

Does Auchan offer an affiliate or partner API?
Auchan participates in affiliate networks that expose a subset of the catalogue with commission tracking, but the affiliate feeds are not designed for competitive intelligence. They lag the live catalogue by 24-48 hours and exclude promotions. For real-time data, scraping the public catalogue is the only path.

Can I scrape Auchan from a Belgian or Spanish residential IP?
Auchan operates in multiple European countries with separate sites. Auchan Belgium uses a different domain (auchan.be) and a different catalogue. For French data specifically, French IPs are strongly preferred. Belgian or Spanish IPs work for short bursts but degrade quickly.

How fresh are the prices in the API response?
Auchan refreshes prices in batches. The catalogue API reflects the current published price with a CDN cache lifetime of 60-120 seconds. For most monitoring use cases that is real-time enough. For high-frequency competitor tracking, hitting the API every 5-10 minutes catches all meaningful changes.

Are there legal considerations specific to scraping French ecommerce sites?
France enforces GDPR strictly and has additional consumer protection rules through the DGCCRF. Limit your collection to non-personal product, price, and availability data. Avoid scraping any personal data, any logged-in pages, and any data that includes individual customer reviews with identifiable information.

How often do Drive prices change?
Most SKUs reprice weekly on Sunday night for the new promotional period. Fresh produce and fish reprice daily. Snapshots every Monday morning capture the canonical weekly state.

Does Auchan Drive expose stock counts?
Only as a binary in_stock flag and an occasional ‘low stock’ badge. True quantity is hidden. For demand-signal analytics, treat the in_stock flag as the only reliable inventory feature.

To build a broader European grocery intelligence stack, browse the ecommerce scraping category for tooling reviews, proxy comparisons, and framework deep dives that pair with the patterns above.
May 7, 2026

Marketplace	Country focus	Catalogue scale	Bot strictness
Auchan Drive	France	Large	High
Carrefour Drive	Adjacent markets	Medium	Medium
Leclerc Drive	Adjacent markets	Smaller	Lower

How to scrape Rakuten Japan in 2026

Scrape Rakuten Japan and you encounter a marketplace shape that does not exist anywhere else in the world. Rakuten Ichiba is a federation of more than 50,000 individual seller storefronts hosted under the Rakuten umbrella, each with its own brand, layout, and pricing logic. Unlike Amazon Japan where every listing is normalized into a single product page, Rakuten lets every merchant publish their own product page for the same item, which means the same EAN can have hundreds of listings across hundreds of sellers. The scraping landscape is shaped by three things: the official Rakuten Ichiba Item API which is generous but rate-limited, the per-merchant storefronts which require HTML scraping for the long tail, and a Japanese-language catalogue that requires careful encoding handling.

This guide focuses on Rakuten Ichiba (the marketplace), which is the dominant property under rakuten.co.jp. Rakuten Travel, Rakuten Books, and Rakuten Mobile use related but distinct APIs.

The official Rakuten Ichiba Item API

Rakuten publishes a free public API at https://app.rakuten.co.jp/services/api/IchibaItem/Search/20220601 that searches the entire Ichiba catalogue. Registration is required to obtain an applicationId, which serves as the rate-limit token. The free tier allows roughly 1 request per second per applicationId, which sounds restrictive but is enough for substantial workloads if you parallelize across multiple registered application IDs.

import httpx
import asyncio

API_URL = "https://app.rakuten.co.jp/services/api/IchibaItem/Search/20220601"

async def search_ichiba(keyword: str, app_id: str, page: int = 1, hits: int = 30):
    params = {
        "applicationId": app_id,
        "keyword": keyword,
        "page": page,
        "hits": hits,
        "sort": "-updateTimestamp",
        "format": "json",
    }
    async with httpx.AsyncClient(timeout=20) as c:
        r = await c.get(API_URL, params=params)
        if r.status_code == 200:
            return r.json().get("Items", [])
        return []

The API returns up to 100 hits per request, paginated to 100 pages, for a maximum of 10,000 hits per query. To exceed that, decompose the query by genre, price band, or shop. The genreId parameter accepts the Rakuten genre tree which is published separately at the IchibaGenre Search API.

Each item response includes itemCode (the canonical Rakuten SKU as <shopcode>:<itemnumber>), itemName, itemPrice in JPY, shopCode, shopName, reviewCount, reviewAverage, imageFlag, and a list of small/medium/large image URLs. For most analytical use cases, the API alone is sufficient and you do not need to scrape HTML.

When you still need HTML scraping

The official API does not expose seller storefront banners, custom seller categorizations, or the freeform descriptions that Rakuten merchants write on their own pages. For brand monitoring use cases that need to verify how a specific seller is presenting a product, you have to scrape the merchant storefront HTML.

Storefront URLs look like https://item.rakuten.co.jp/<shopcode>/<itemnumber>/. The HTML is encoded as Shift-JIS or UTF-8 depending on the merchant template, so always read the Content-Type header and decode accordingly. Encoding bugs are the most common silent failure in Rakuten scraping projects.

import httpx
from bs4 import BeautifulSoup

async def fetch_storefront(shop_code: str, item_number: str, proxy: str):
    url = f"https://item.rakuten.co.jp/{shop_code}/{item_number}/"
    async with httpx.AsyncClient(proxy=proxy, timeout=20) as c:
        r = await c.get(url)
        if r.status_code != 200:
            return None
        ctype = r.headers.get("content-type", "").lower()
        if "shift_jis" in ctype or "shift-jis" in ctype:
            text = r.content.decode("shift_jis", errors="replace")
        else:
            text = r.text
        soup = BeautifulSoup(text, "lxml")
        return {
            "title": soup.select_one("title").get_text(strip=True) if soup.select_one("title") else None,
            "html_size": len(text),
        }

Storefront scraping requires Japanese residential proxies. Rakuten’s CDN serves significantly slower paths to non-Japanese visitors, and merchant storefronts often refuse non-JP traffic outright if they have enabled the country lock setting in their seller dashboard.

Genre-based catalogue discovery

Rakuten’s genre tree is the most reliable way to enumerate the catalogue. The IchibaGenre Search API returns the genre hierarchy starting from a root node. By recursively walking the tree, you can map every leaf genre and then issue Item Search queries against each leaf to collect SKUs.

GENRE_API = "https://app.rakuten.co.jp/services/api/IchibaGenre/Search/20140222"

async def fetch_children(genre_id: int, app_id: str):
    params = {"applicationId": app_id, "genreId": genre_id, "format": "json"}
    async with httpx.AsyncClient(timeout=20) as c:
        r = await c.get(GENRE_API, params=params)
        if r.status_code == 200:
            return r.json().get("children", [])
        return []

The genre tree has roughly 50,000 leaf nodes. Walking it once takes about 14 hours at 1 request per second. Cache the result in a database and refresh quarterly. The structure changes slowly enough that quarterly is sufficient for most use cases.

Multi-tenant pricing and seller analytics

Because the same EAN can be sold by multiple Rakuten merchants, the same physical product often appears at very different prices across the marketplace. For brand owners, this matters because it surfaces parallel imports, gray market listings, and unauthorized resellers. For competitive intelligence, it surfaces the price floor that the most aggressive seller is willing to defend.

The pattern is to group items by EAN (when published in the API response) or by a fuzzy match on itemName and merchant brand. Grouping by EAN is cleaner but only works for SKUs where the merchant published it. For the long tail, fuzzy match by normalized title and brand is the practical fallback.

Field	Source	Reliability
itemCode	API	Canonical SKU per merchant
EAN/JAN	API attributes	About 60% coverage
itemName	API	Always present, often noisy
itemPrice	API	Authoritative
shopCode	API	Canonical merchant ID

For grouping across merchants, build a normalization pass that strips merchant tags from the title (sellers add [Free shipping] and [Same day ship] markers liberally), normalizes character widths, and removes common Japanese boilerplate.

Detecting and routing around CAPTCHA challenges on Rakuten

When Rakuten flags your traffic, the response is usually a Cloudflare or vendor interrogation page rather than a clean HTTP error. Your scraper needs to detect this content-type swap explicitly. Look for the signature cf-mitigated header, the presence of __cf_chl_ cookies, or HTML containing Just a moment.... Treat any of these as a soft block.

def is_challenged(response) -> bool:
    if response.status_code in (403, 503):
        return True
    if "cf-mitigated" in response.headers:
        return True
    if "__cf_chl_" in response.headers.get("set-cookie", ""):
        return True
    body = response.text[:2000].lower()
    return "just a moment" in body or "checking your browser" in body

For pages that absolutely must be fetched, have a fallback path that uses a headless browser with a real Japan residential IP. The browser path costs more per page but solves the small percentage of challenges that the API path cannot handle. Most production setups maintain a 95/5 split between the lightweight HTTP path and the browser fallback path.

Working with JPY pricing and FX normalization

Pricing in Japan is denominated in JPY, and any cross-market analysis requires careful FX normalization. The naive approach of converting at scrape time using a live FX feed introduces noise into your trend lines because exchange rate movements get conflated with real price changes. Store the price in local JPY and apply FX conversion at query time using a daily reference rate.

CREATE TABLE fx_rates (
    rate_date DATE NOT NULL,
    base_ccy VARCHAR(3) NOT NULL,
    quote_ccy VARCHAR(3) NOT NULL,
    rate DECIMAL(18,8) NOT NULL,
    PRIMARY KEY (rate_date, base_ccy, quote_ccy)
);

Comparing Rakuten to other regional marketplaces

Marketplace	Country focus	Catalogue scale	Bot strictness
Rakuten Ichiba	Japan	Large	High
Amazon Japan	Adjacent markets	Medium	Medium
Yahoo Shopping	Adjacent markets	Smaller	Lower

Cross-marketplace analyses help separate platform-specific dynamics from genuine market trends. If a price drops on Rakuten Ichiba but stays flat across the comparable competitors, that is a platform-driven event rather than a market-wide signal. Your scraping pipeline should ingest from at least three platforms in any market where you intend to publish category insights.

Operational monitoring and alerting

import time
from collections import deque

class IPHealthTracker:
    def __init__(self, window_seconds: int = 300):
        self.window = window_seconds
        self.events = {}

    def record(self, ip: str, success: bool):
        bucket = self.events.setdefault(ip, deque())
        now = time.time()
        bucket.append((now, success))
        while bucket and bucket[0][0] < now - self.window:
            bucket.popleft()

    def success_rate(self, ip: str) -> float:
        bucket = self.events.get(ip)
        if not bucket:
            return 1.0
        successes = sum(1 for _, ok in bucket if ok)
        return successes / len(bucket)

Wire this into Prometheus or your existing observability stack so the on-call engineer sees IP degradation as it happens rather than after the daily snapshot fails.

Legal and compliance considerations for Japan

Public product, price, and availability data are generally treated as fair to scrape in most jurisdictions, but Japan has its own consumer protection and personal data frameworks. Confine your collection to non-personal data: SKU identifiers, prices, descriptions, ratings as aggregates, and seller display names. Avoid collecting individual buyer reviews with names, phone numbers, or email addresses attached, and avoid pulling any data behind a login.

Pipeline orchestration and scheduling

from prefect import flow, task

@task(retries=3, retry_delay_seconds=60)
def fetch_category(category_id: int, page: int):
    return crawl_one_page(category_id, page)

@task
def store_pages(pages: list):
    write_to_db(pages)

@flow(name="rakuten-daily-sweep")
def daily_sweep(category_ids: list):
    futures = []
    for cid in category_ids:
        for page in range(1, 50):
            futures.append(fetch_category.submit(cid, page))
    pages = [f.result() for f in futures]
    store_pages(pages)

Run the flow on a 6-hour or 24-hour schedule depending on how dynamic the underlying catalogue is. For seasonal markets like apparel, a 6-hour cadence catches the meaningful movements without driving up proxy costs unnecessarily.

Sample analytics queries on the collected dataset

Once your snapshots are landing reliably, the analytics layer is where the value materializes. A few queries that consistently come up across Rakuten datasets:

-- Top 50 SKUs by price drop in the last 7 days
SELECT sku, MIN(selling_price) - MAX(selling_price) AS price_drop
FROM snapshot
WHERE snapshot_at > now() - interval '7 days'
GROUP BY sku
ORDER BY price_drop ASC
LIMIT 50;

-- Stock-out frequency per category
SELECT category_id,
       SUM(CASE WHEN in_stock = 0 THEN 1 ELSE 0 END)::float / COUNT(*) AS oos_rate
FROM snapshot
WHERE snapshot_at > now() - interval '30 days'
GROUP BY category_id
ORDER BY oos_rate DESC;

Add a brand share view, a seller concentration view, and a campaign-frequency view and you have a competitive intelligence product. The collection layer is the prerequisite; the analytics layer is where you create defensible value.

Building robust deduplication across noisy listings

import hashlib
from PIL import Image
import imagehash

def perceptual_hash(image_path: str) -> str:
    img = Image.open(image_path)
    return str(imagehash.phash(img, hash_size=16))

def normalize_title(title: str) -> str:
    title = title.lower()
    for token in ["[free shipping]", "[same day]", "(new)", "*sale*"]:
        title = title.replace(token, "")
    return " ".join(title.split())

Versioning your scraper for catalogue evolution

The pattern that works best is to stamp every snapshot row with the scraper version that produced it. When you deploy a new version of the parser, increment the version number. Downstream analytics can filter by version when they need consistent semantics across a time range, or join across versions when they want long-running trend analysis.

ALTER TABLE snapshot ADD COLUMN scraper_version VARCHAR(16);
CREATE INDEX scraper_version_idx ON snapshot(scraper_version);

Pair this with a small registry table that documents what each scraper version did differently. When a downstream user asks why a particular metric jumped on a specific date, the version registry usually has the answer.

Caching strategy and incremental crawls

The first signal is freshness deadline. Every SKU has a maximum staleness budget (say 24 hours), and any SKU older than its budget gets refreshed.

The second signal is volatility. SKUs that have changed price recently get higher refresh priority because they are more likely to change again. SKUs that have been stable for weeks can drop to a longer refresh interval.

The third signal is business priority. SKUs that downstream users actually query (tracked by query logs) get higher refresh priority than dormant SKUs that nobody has looked at in months.

def schedule_refresh(sku_id: int, last_changed_at, last_queried_at, last_fetched_at) -> int:
    """returns priority score; higher = refresh sooner"""
    age = (now - last_fetched_at).total_seconds() / 3600
    volatility = 10 if (now - last_changed_at).days < 7 else 1
    relevance = 5 if (now - last_queried_at).days < 1 else 1
    return age * volatility * relevance

This kind of priority-driven scheduler reduces total request volume by 60-80% compared to blind full snapshots, while keeping the data fresh on the SKUs that actually matter to the business.

Common pitfalls when scraping Rakuten Ichiba

Three issues dominate Rakuten Ichiba scraping. The first is shop-vs-product attribution. Rakuten is fundamentally a marketplace of independent shops, each with its own URL pattern, stock policy, and shipping terms. The same JAN code can appear in 50+ shops at different prices. Aggregating to the JAN level without preserving shop_id loses the price-dispersion signal that makes Rakuten data interesting.

The second is point-multiplier inflation. Rakuten campaigns layer point bonuses (5x, 10x, even 20x on Super Sale days). The visible price is in JPY but the effective price after points can be 10-15% lower for SPU members. A scraper that ignores the point multiplier field misreports the realized cost to the buyer. Capture both price and point_multiplier and compute the effective price downstream.

The third is character encoding edge cases. Some legacy shops still serve Shift-JIS or EUC-JP rather than UTF-8. Modern Rakuten infrastructure normalizes most pages to UTF-8 but shop-hosted product descriptions can leak through with the original encoding. Detect encoding per response rather than assuming UTF-8 globally.

FAQ

Do I need a Japanese IP to use the official API?
No. The IchibaItem Search API is accessible from anywhere in the world with a valid applicationId. Only the merchant storefront HTML scraping requires Japanese IP addresses for reliable access.

How many applicationIds can I register?
Rakuten allows multiple applicationIds per developer account. The terms of service require each applicationId to correspond to a real application, and aggressive parallelization across many IDs can trigger account-level throttling. For most production use, 3 to 5 IDs is sufficient and avoids the account-level scrutiny.

Does the API include stock counts?
No. The IchibaItem response includes an availability flag but not numerical stock. For stock-level data you have to scrape the storefront HTML, where merchants sometimes publish real-time stock indicators.

What about Rakuten Books and Rakuten Travel?
Rakuten publishes separate APIs for each major property: BooksTotal Search, TravelHotel Search, and so on. They follow the same registration model and similar rate limits. The applicationId is shared across all Rakuten APIs, so a single registration unlocks the whole family.

Can I scrape Rakuten reviews via the API?
The official API does not expose review text, only aggregate scores and counts. To collect review text you have to scrape the storefront HTML, which raises personal data considerations because reviewer display names are visible. Limit your collection to anonymized aggregates or work with explicit consent if reviewer identity matters.

Is the official Rakuten Ichiba API still useful in 2026?
Yes for catalog discovery and basic price data, but rate limits make it impractical for minute-level monitoring. Most production scrapers blend the official API for breadth with targeted scraping for depth.

How do I track Rakuten Super Sale price drops accurately?
Take baseline snapshots 14 days before the event and compare against intra-event hourly snapshots. Many sellers raise prices in the week before to inflate the apparent discount.

To pair this with a broader Asia ecommerce intelligence stack, browse the ecommerce scraping category for tooling reviews, proxy comparisons, and framework deep dives that pair with the patterns above.

May 7, 2026

How to scrape Jumia Africa product listings

Scrape Jumia Africa and you are scraping the largest pan-African ecommerce platform, with separate country instances for Nigeria, Kenya, Egypt, Morocco, Ivory Coast, Senegal, Ghana, Uganda, and a handful of other markets. Jumia was founded in 2012 and went public on the NYSE in 2019, and by 2026 it remains the dominant horizontal marketplace across Sub-Saharan Africa with category mixes that lean heavily into mobile phones, home appliances, and fashion. The scraping landscape is shaped by three things: per-country subdomains with different catalogues, a pricing system that mixes Jumia direct, Jumia Mall verified sellers, and a long tail of independent merchants, and an anti-bot layer that becomes more aggressive on the larger Nigeria and Egypt domains.

This guide focuses on Jumia Nigeria as the canonical example, with notes on cross-country differences. The patterns transfer to every Jumia country instance with minor adjustments to the domain and currency.

Mapping the Jumia URL and listing structure

Each Jumia country lives at its own subdomain: www.jumia.com.ng for Nigeria, www.jumia.co.ke for Kenya, www.jumia.com.eg for Egypt, www.jumia.ci for Ivory Coast, and so on. Within each country, the URL structure is consistent: https://www.jumia.com.ng/<product-slug>.html for product detail pages and https://www.jumia.com.ng/<category-slug>/ for category listings. Product slugs include a SKU identifier embedded near the end, which is the canonical key you should store.

Jumia exposes a server-rendered HTML front end with a JavaScript-hydrated layer for filters and recommendations. There is no fully public JSON API, but the product detail pages embed a <script type="application/ld+json"> block that carries Schema.org Product data including price, availability, brand, and SKU. Parsing this JSON-LD block is dramatically more reliable than scraping the visible HTML because the schema is stable while the visible markup changes regularly.

import httpx
from bs4 import BeautifulSoup
import json

HEADERS = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36",
    "Accept-Language": "en-NG,en;q=0.9",
}

def parse_product(html: str) -> dict:
    soup = BeautifulSoup(html, "lxml")
    for script in soup.find_all("script", type="application/ld+json"):
        try:
            data = json.loads(script.string)
        except (json.JSONDecodeError, TypeError):
            continue
        if isinstance(data, dict) and data.get("@type") == "Product":
            return {
                "sku": data.get("sku"),
                "name": data.get("name"),
                "brand": (data.get("brand") or {}).get("name"),
                "price": (data.get("offers") or {}).get("price"),
                "currency": (data.get("offers") or {}).get("priceCurrency"),
                "availability": (data.get("offers") or {}).get("availability"),
                "rating": (data.get("aggregateRating") or {}).get("ratingValue"),
            }
    return {}

async def fetch_product(url: str, proxy: str) -> dict:
    async with httpx.AsyncClient(proxy=proxy, headers=HEADERS, timeout=20) as c:
        r = await c.get(url)
        if r.status_code != 200:
            return {}
        return parse_product(r.text)

Jumia’s JSON-LD includes the offer price but not the seller breakdown. To get the per-seller information, you have to parse the seller block from the HTML directly. The seller block is structured around a data-merchant-name attribute that is consistent across countries.

Country-specific proxy strategy for Jumia

Jumia’s bot detection becomes meaningfully stricter on the larger country instances. Nigeria and Egypt see the highest scrutiny, both because of the volume of legitimate scraping that already happens (price intelligence vendors, brand monitoring tools, affiliate networks) and because of the volume of fraud activity that uses the same patterns. Kenya and Ivory Coast see lighter enforcement.

Your proxy strategy should match the country you are scraping. For Nigeria and Egypt, use residential or mobile IPs in-country. MTN, Airtel, and 9mobile mobile pools work well for Nigeria. Vodafone and Etisalat residential pools work for Egypt. For the smaller markets, pan-African residential pools or even GCC-region pools sometimes work because Jumia’s geo-classification is less granular for low-volume countries.

Country	Recommended proxy origin	Tolerance per IP
Nigeria	Nigerian mobile or residential	100 req/hr per IP
Egypt	Egyptian residential	100 req/hr per IP
Kenya	Kenyan or pan-African residential	200 req/hr per IP
Morocco	Moroccan or French residential	200 req/hr per IP
Ivory Coast	Pan-African or French residential	250 req/hr per IP
Senegal	Pan-African or French residential	250 req/hr per IP

The cost differential between in-country residential and pan-African residential pools is significant for Nigerian inventory specifically. For most production workloads, the cost is justified by the success rate uplift. For lighter monitoring workloads under 1,000 SKUs per day, pan-African pools sometimes work even for Nigeria with careful rate limiting.

Crawling category trees and pagination

Category listings on Jumia are paginated with a ?page=N query parameter. The maximum reachable page depends on the category but typically caps at 50 pages of 40 products each. To go deeper, decompose by sub-category, brand facet, or price band. The category pages also embed a JSON-LD ItemList block that gives you structured access to the listing.

async def crawl_category(base_url: str, max_pages: int, proxy_pool):
    items = []
    for page in range(1, max_pages + 1):
        proxy = proxy_pool.next()
        url = f"{base_url}?page={page}"
        async with httpx.AsyncClient(proxy=proxy, headers=HEADERS, timeout=20) as c:
            r = await c.get(url)
            if r.status_code != 200:
                continue
            soup = BeautifulSoup(r.text, "lxml")
            cards = soup.select("article.prd")
            if not cards:
                break
            for card in cards:
                items.append({
                    "sku": card.get("data-sku"),
                    "name": card.select_one("h3.name").get_text(strip=True) if card.select_one("h3.name") else None,
                    "price": card.select_one("div.prc").get_text(strip=True) if card.select_one("div.prc") else None,
                    "url": "https://www.jumia.com.ng" + card.select_one("a.core")["href"] if card.select_one("a.core") else None,
                })
    return items

For very broad categories like Phones and Tablets in Nigeria, the visible pagination only covers the first 2,000 SKUs. The remaining tail requires faceted decomposition. Build a recursive crawler that subdivides any category exceeding 2,000 results into brand and price-band buckets until each bucket fits within the pagination cap.

Tracking Jumia Mall vs. third-party merchant signal

Jumia distinguishes between Jumia Mall (verified sellers with quality guarantees) and ordinary third-party merchants. The distinction matters for analytics because Jumia Mall pricing is often the more stable signal while third-party pricing is more volatile. The seller card on the product page exposes a Jumia Mall badge that you can detect by looking for the mall-badge CSS class.

For brand monitoring use cases, separate the dataset into Mall and non-Mall slices. The Mall slice gives you the canonical price point that the brand wants to maintain. The non-Mall slice gives you the gray-market and parallel-import price activity that often signals supply chain shifts or unauthorized resellers.

Rate limits, retries, and session management

Jumia does not publish rate limits, but observed behavior is consistent across countries. A single IP can sustain about 1 request per 2 seconds for an hour before triggering a soft block that returns either a 429 or a Cloudflare challenge. After a 10-30 minute cooldown the IP is usable again. The cooldown extends if you continue retrying through the block.

import asyncio
async def safe_request(url: str, proxy_pool, max_retries: int = 3):
    for attempt in range(max_retries):
        proxy = proxy_pool.next()
        try:
            async with httpx.AsyncClient(proxy=proxy, headers=HEADERS, timeout=20) as c:
                r = await c.get(url)
                if r.status_code == 200:
                    return r
                if r.status_code in (429, 503):
                    await asyncio.sleep(60)
                    continue
        except httpx.HTTPError:
            pass
        await asyncio.sleep(5 * (attempt + 1))
    return None

The exponential backoff matters more on Jumia than on some other African ecommerce sites because the block escalation is relatively slow but the cooldown extends quickly under retries. A patient retry pattern outperforms an aggressive one.

Working with multi-currency Jumia datasets

Each Jumia country uses its own local currency: NGN for Nigeria, KES for Kenya, EGP for Egypt, MAD for Morocco, XOF for Ivory Coast and Senegal. For pan-African analyses, normalize to USD or EUR using daily FX rates rather than scrape-time conversions. NGN in particular has had significant devaluation events in recent years, and any cross-time analysis needs to account for that.

Detecting and routing around CAPTCHA challenges on Jumia

When Jumia flags your traffic, the response is usually a Cloudflare interrogation page rather than a clean HTTP error. Your scraper needs to detect this content-type swap explicitly. Look for the signature cf-mitigated header, the presence of __cf_chl_ cookies, or HTML containing Just a moment.... Treat any of these as a soft block.

def is_challenged(response) -> bool:
    if response.status_code in (403, 503):
        return True
    if "cf-mitigated" in response.headers:
        return True
    if "__cf_chl_" in response.headers.get("set-cookie", ""):
        return True
    body = response.text[:2000].lower()
    return "just a moment" in body or "checking your browser" in body

For pages that absolutely must be fetched (a specific SKU your client cares about), have a fallback path that uses a headless browser with a real Nigeria residential IP. The browser path costs more per page but solves the small percentage of challenges that the API path cannot handle. Most production setups maintain a 95/5 split: 95% of requests go through the lightweight HTTP and JSON path, 5% fall through to the browser path on challenge.

Working with NGN pricing and FX normalization

Pricing in Nigeria is denominated in NGN, and any cross-market analysis requires careful FX normalization. The naive approach of converting at scrape time using a live FX feed introduces noise into your trend lines because exchange rate movements get conflated with real price changes. The correct pattern is to store the price in local NGN and apply FX conversion at query time using a daily reference rate.

CREATE TABLE fx_rates (
    rate_date DATE NOT NULL,
    base_ccy VARCHAR(3) NOT NULL,
    quote_ccy VARCHAR(3) NOT NULL,
    rate DECIMAL(18,8) NOT NULL,
    PRIMARY KEY (rate_date, base_ccy, quote_ccy)
);

Source the daily rates from a reliable feed such as the European Central Bank reference rates or your bank’s wholesale feed. Avoid scraping retail FX rates because they include the bank’s spread and produce inconsistent comparisons. For analyses that span multiple years, also account for currency revaluation events that occasionally happen in emerging markets.

Comparing Jumia to other regional marketplaces

Marketplace	Country focus	Catalogue scale	Bot strictness
Jumia	Nigeria	Large	High
Konga	Adjacent markets	Medium	Medium
Jiji	Adjacent markets	Smaller	Lower

Cross-marketplace analyses help separate platform-specific dynamics from genuine market trends. If a price drops on Jumia but stays flat across the comparable competitors, that is a platform-driven event rather than a market-wide signal. Your scraping pipeline should ingest from at least three platforms in any market where you intend to publish category insights.

Operational monitoring and alerting

import time
from collections import deque

class IPHealthTracker:
    def __init__(self, window_seconds: int = 300):
        self.window = window_seconds
        self.events = {}

    def record(self, ip: str, success: bool):
        bucket = self.events.setdefault(ip, deque())
        now = time.time()
        bucket.append((now, success))
        while bucket and bucket[0][0] < now - self.window:
            bucket.popleft()

    def success_rate(self, ip: str) -> float:
        bucket = self.events.get(ip)
        if not bucket:
            return 1.0
        successes = sum(1 for _, ok in bucket if ok)
        return successes / len(bucket)

Wire this into Prometheus or your existing observability stack so the on-call engineer sees IP degradation as it happens rather than after the daily snapshot fails. For long-running operations against Jumia, IP rotation triggered by the health tracker is more reliable than fixed rotation schedules.

Legal and compliance considerations for Nigeria

Public product, price, and availability data are generally treated as fair to scrape in most jurisdictions, but Nigeria has its own consumer protection and personal data frameworks that overlay any general analysis. Confine your collection to non-personal data: SKU identifiers, prices, descriptions, ratings as aggregates, and seller display names. Avoid collecting individual buyer reviews with names, phone numbers, or email addresses attached, and avoid pulling any data behind a login.

For commercial deployment of a scraper that targets Jumia, document your basis for processing, your data retention period, and your purpose limitation. Most data protection regimes treat scraped public data more favorably when there is a clear lawful basis and the data is not used for direct marketing to identified individuals. The W3C Web Annotation guidance and similar published frameworks remain useful starting points for documenting your approach.

Pipeline orchestration and scheduling

from prefect import flow, task

@task(retries=3, retry_delay_seconds=60)
def fetch_category(category_id: int, page: int):
    return crawl_one_page(category_id, page)

@task
def store_pages(pages: list):
    write_to_db(pages)

@flow(name="jumia-daily-sweep")
def daily_sweep(category_ids: list):
    futures = []
    for cid in category_ids:
        for page in range(1, 50):
            futures.append(fetch_category.submit(cid, page))
    pages = [f.result() for f in futures]
    store_pages(pages)

Sample analytics queries on the collected dataset

Once your snapshots are landing reliably, the analytics layer is where the value materializes. A few queries that consistently come up across Jumia datasets:

-- Top 50 SKUs by price drop in the last 7 days
SELECT sku, MIN(selling_price) - MAX(selling_price) AS price_drop
FROM snapshot
WHERE snapshot_at > now() - interval '7 days'
GROUP BY sku
ORDER BY price_drop ASC
LIMIT 50;

-- Stock-out frequency per category
SELECT category_id,
       SUM(CASE WHEN in_stock = 0 THEN 1 ELSE 0 END)::float / COUNT(*) AS oos_rate
FROM snapshot
WHERE snapshot_at > now() - interval '30 days'
GROUP BY category_id
ORDER BY oos_rate DESC;

-- New SKUs first seen in the last 14 days
SELECT sku, MIN(snapshot_at) AS first_seen
FROM snapshot
GROUP BY sku
HAVING MIN(snapshot_at) > now() - interval '14 days'
ORDER BY first_seen DESC;

Versioning your scraper for catalogue evolution

Every ecommerce site evolves its catalogue structure regularly. New attribute fields appear, old fields are deprecated, category trees are reorganized, and pricing display logic changes. Your scraper code has to evolve with these changes, and a versioning pattern that keeps old data interpretable is critical. Stamp every snapshot row with the scraper version that produced it. When you deploy a new version of the parser, increment the version number. Downstream analytics can filter by version when they need consistent semantics across a time range, or join across versions when they want long-running trend analysis.

ALTER TABLE snapshot ADD COLUMN scraper_version VARCHAR(16);
CREATE INDEX scraper_version_idx ON snapshot(scraper_version);

Common pitfalls when scraping Jumia

Three issues catch most teams off guard. The first is country-domain fragmentation. Jumia operates 11 country sites (jumia.com.ng, jumia.co.ke, jumia.ci, etc), each with its own currency, language, and seller pool. The same SKU can sit on multiple country sites with different prices and different stock. A scraper that treats jumia.com.ng prices as representative of West Africa understates Ivorian prices by 10-25% on average.

The second is JumiaPay vs cash-on-delivery price drift. Some sellers offer a JumiaPay discount that is rendered only when the user signs in. Anonymous scraping captures the list price; authenticated scraping captures the discounted price. Decide which population matters for your analysis and stay consistent.

The third is flash-sale staleness. The flash_sales API serves a curated subset of SKUs with countdown timers. The countdown is computed client-side from a server-issued epoch. If you cache the response for more than 60 seconds during an active flash sale, the timer drifts and downstream consumers see incorrect end times. Bypass cache for flash-sale endpoints and accept the higher request cost during campaign hours.

FAQ

Can I scrape all Jumia countries from a single Nigerian residential IP pool?
Technically yes, but Jumia geo-classifies the visitor IP and serves different catalogues per country. A Nigerian IP requesting jumia.co.ke works but raises the bot score because real Kenyan traffic does not originate from Nigerian residential networks. For each country you intend to scrape, source proxies from that country or from a regional adjacent market.

How does Jumia handle the difference between Mall and non-Mall sellers in the API?
There is no public API. In the HTML, Jumia Mall items are tagged with a CSS badge and a separate data-mall attribute on the seller card. Capture this signal at parse time and persist it in your snapshot table so downstream analyses can filter by Mall status.

Does Jumia’s price include shipping?
The price shown on the listing card is the product price exclusive of shipping. Shipping is calculated at checkout based on delivery zone and seller fulfillment method. For total cost analyses you would need to simulate add-to-basket flows, which is more complex and less reliable. Most price intelligence projects work with the listed price as the canonical signal.

Are Jumia reviews scrapable for sentiment analysis?
Reviews are visible on product pages and accessible through HTML scraping. They include the reviewer’s display name (usually a first name and initial) and the review text. From a privacy perspective, treat the display name as personal data and avoid storing it in long-term datasets. The review text and rating are the analytically useful fields.

Does Jumia run a sitemap I can use for SKU discovery?
Jumia exposes sitemaps at /sitemap.xml for each country. They list categories and product URLs but the per-product entries are typically only a sample of the full catalogue. For exhaustive SKU discovery, combine sitemap parsing with category and seller crawls.

Can one scraper cover all 11 Jumia country sites?
Architecturally yes, but you need per-country proxy pools. A Lagos residential IP that hits jumia.co.ke succeeds at low volume but is throttled aggressively above ~200 requests per hour.

How do I reconcile Jumia data with Konga or other competitors?
Use brand+model+capacity as the join key for electronics. EAN/UPC coverage is patchy, so fall back to fuzzy title matching with a confidence score before merging analytics.

To build a broader Africa ecommerce intelligence stack, browse the ecommerce scraping category for tooling reviews, proxy comparisons, and framework deep dives that pair with the patterns above.

May 7, 2026

How to scrape Noon UAE ecommerce in 2026
How to scrape Noon UAE ecommerce in 2026

Scrape Noon UAE and you are scraping the dominant home-grown ecommerce platform in the Gulf, with separate marketplaces for the United Arab Emirates, Saudi Arabia, and Egypt. Noon was launched in 2017 as a Mohammed bin Rashid initiative to compete with Amazon’s regional expansion, and by 2026 it has captured a meaningful share of GCC ecommerce GMV through Noon Daily for grocery, Noon Food for delivery, and the core noon.com marketplace for general merchandise. The scraping landscape is shaped by three things: dual-language content (Arabic and English), country-specific catalogues with overlapping but not identical SKUs, and a Cloudflare-front-end that aggressively profiles non-GCC traffic.

This guide covers Noon UAE specifically. Most patterns transfer to noon.com.sa (Saudi) and noon.com.eg (Egypt) with minor adjustments to the country code parameter and currency parsing. The target keyword for this guide is scrape Noon UAE.

How Noon’s URL and language structure works

Noon URLs include a country segment, a language segment, and the product slug. A UAE Arabic URL looks like https://www.noon.com/uae-ar/<product-slug>/<sku>/p/, and the English equivalent is https://www.noon.com/uae-en/.... The two URLs serve different language pages but resolve to the same SKU. The trailing /p/ is what tells Noon’s routing layer that this is a product detail page.

The SKU at the end of the URL is the canonical identifier. A given product is sold in UAE, Saudi, and Egypt with the same SKU prefix but different listing IDs per country. If you are building a cross-country catalogue, capture both the SKU and the country segment as a composite key.

Noon’s product API lives at https://www.noon.com/_svc/catalog/api/v3/u/<sku>/p. The endpoint accepts an X-Locale header that controls language and country. Set it to en-ae for UAE English, ar-ae for UAE Arabic, en-sa for Saudi English, and so on. The JSON shape is consistent across locales.
```
import httpx
import asyncio

NOON_API = "https://www.noon.com/_svc/catalog/api/v3/u"

def headers(locale: str):
    return {
        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.0 Safari/605.1.15",
        "Accept": "application/json",
        "X-Locale": locale,
        "X-Platform": "web",
        "Referer": f"https://www.noon.com/{locale.split('-')[1]}-{locale.split('-')[0]}/",
    }

async def fetch_product(sku: str, locale: str, proxy: str):
    url = f"{NOON_API}/{sku}/p"
    async with httpx.AsyncClient(proxy=proxy, headers=headers(locale), timeout=20) as c:
        r = await c.get(url)
        if r.status_code == 200:
            return r.json()
        return None
```
The response includes the canonical product object, an offers array with merchant pricing, an attributes array with structured specs, and a crossSellRecommendations block that you can use to discover related SKUs without crawling categories.

Why UAE residential proxies matter for Noon

Noon’s bot detection is built on Cloudflare Bot Management plus an internal scoring service that considers the visitor IP geography, the language requested, and the device fingerprint. A request from an AWS Frankfurt IP asking for X-Locale: ar-ae is immediately suspicious because real UAE Arabic-language traffic almost never originates from European data centers.

The cleanest signal you can give Noon is a UAE residential IP requesting the locale that matches its country. UAE residential pools are smaller than Turkish or Vietnamese pools because the population is smaller, but several major proxy providers list inventory specifically in UAE through Etisalat and du. Expect to pay a premium versus general residential pools.

For workloads under 5,000 SKUs per day, you can sometimes get away with rotating GCC residential IPs (any Saudi or Kuwait IP usually works as long as the locale matches). For higher volumes, dedicated UAE inventory matters because the pool size shrinks and Noon’s behavioral scoring catches the pattern of foreign IPs rapid-fire requesting Arabic content.

Pulling category and search data

Noon exposes a search API at https://www.noon.com/_svc/search/api/v3/u/search. The endpoint takes a query string, a country segment, sort, and pagination params. For category sweeps, you pass the category slug as a filter facet rather than a path param.
```
async def search_noon(query: str, locale: str, page: int, proxy: str):
    url = "https://www.noon.com/_svc/search/api/v3/u/search"
    params = {
        "q": query,
        "page": page,
        "limit": 50,
        "sort": "popularity",
    }
    async with httpx.AsyncClient(proxy=proxy, headers=headers(locale), timeout=20) as c:
        r = await c.get(url, params=params)
        if r.status_code == 200:
            return r.json().get("hits", [])
        return []
```
The search API caps result depth at roughly 1,000 hits per query, which is the typical CDN safeguard against scraping. To go deeper into a category, decompose the query by brand, price band, or attribute facets. The search response includes the available facets and counts, which gives you a recipe for subdivision.

Handling Arabic text and RTL parsing

If your downstream pipeline is going to treat product titles as English-only, you will lose half of Noon’s catalogue. Many sellers list products in both languages, but a meaningful long tail of grocery, beauty, and fashion is Arabic-only or has more detailed Arabic descriptions than English ones. Capture both languages from day one.

The simplest pattern is to make two requests per SKU, one with ar-ae and one with en-ae, and merge the results. The title, description, attributes, and seller.name fields differ between the two responses. Everything numeric (price, stock, rating) is identical. For storage, use a JSONB or JSON column that holds both language variants and let downstream consumers pick whichever they need.

For full-text search across the dataset, Postgres with the arabic text search configuration works well, as does Elasticsearch with the Arabic analyzer. Avoid stripping diacritics during ingestion because they carry semantic meaning in Arabic product names, especially for branded items.

Comparing Noon’s three country marketplaces

Country Currency Catalogue size estimate Bot scrutiny Typical proxy cost

UAE (uae) AED 8M+ SKUs High $$$

Saudi (sa) SAR 12M+ SKUs Highest $$$$

Egypt (eg) EGP 4M+ SKUs Medium $$

Saudi gets the most attention from Noon’s bot defenses because it generates the largest GMV. Egypt has the lightest defenses but the lowest catalogue depth. If you are building a Gulf-wide price intelligence product, plan for separate proxy budgets per country and don’t try to scrape all three from the same IP pool.

Rate limits and retry patterns

Noon’s rate limit thresholds are not published, but observed behavior is consistent: a single residential IP can sustain about 1 product detail request per second for 30-60 minutes before triggering a soft block that returns 403 with a Cloudflare challenge page. After a 5-15 minute cooldown the IP is usable again. Sticky-session residential proxies handle this gracefully with rotation on 403.
```
async def safe_fetch(sku: str, locale: str, proxy_pool, max_retries: int = 3):
    for attempt in range(max_retries):
        proxy = proxy_pool.next()
        try:
            data = await fetch_product(sku, locale, proxy)
            if data:
                return data
        except httpx.HTTPError:
            pass
        await asyncio.sleep(2 ** attempt + 5)
    return None
```
For large category sweeps, distribute work across IPs so no single IP exceeds the throughput threshold. A scheduling layer that tracks per-IP request counts and back-off windows pays for itself quickly.

Storing Noon snapshots

Schema for a per-country product snapshot:
```
CREATE TABLE noon_snapshot (
    snapshot_at TIMESTAMP NOT NULL,
    country VARCHAR(2) NOT NULL,
    sku VARCHAR(64) NOT NULL,
    seller_id VARCHAR(64),
    price_aed DECIMAL(12,2),
    sale_price_aed DECIMAL(12,2),
    in_stock BOOLEAN,
    rating DECIMAL(3,2),
    review_count INT,
    title_en TEXT,
    title_ar TEXT,
    PRIMARY KEY (snapshot_at, country, sku)
);
```
For Saudi and Egypt, swap the price column to local currency. If you are normalizing across countries, store both local currency and a derived AED-equivalent column updated daily from a central FX table. Don’t try to convert at scrape time because exchange-rate noise will pollute your trend lines.

Linking Noon scraping to broader GCC strategy

Noon is the largest single property in GCC ecommerce, but it is not the only one. Amazon UAE and amazon.sa are still major players, and category-specific sites like Sharaf DG for electronics matter for some verticals. Build your scraping stack with a multi-source mindset from the start, even if you are only launching with Noon. Our GCC ecommerce scraping overview collects related guides as we publish them.

For broader proxy strategy in MENA markets, see our residential proxy provider ranking, which now includes vendor-by-vendor UAE and Saudi inventory counts.

Detecting and routing around CAPTCHA challenges

When Noon flags your traffic, the response is usually a Cloudflare interrogation page rather than an HTTP 4xx. Your scraper needs to detect this content-type swap explicitly. Look for the signature cf-mitigated header, the presence of __cf_chl_ cookies, or HTML containing Just a moment.... Treat any of these as a soft block.
```
def is_challenged(response) -> bool:
    if response.status_code in (403, 503):
        return True
    if "cf-mitigated" in response.headers:
        return True
    if "__cf_chl_" in response.headers.get("set-cookie", ""):
        return True
    body = response.text[:2000].lower()
    return "just a moment" in body or "checking your browser" in body
```
When you detect a challenge, do not retry on the same IP for at least 30 minutes. Mark that IP as cooling and route subsequent requests to a different IP in your pool. Aggressive retries on a flagged IP cause the cooling window to extend and can lead to long-term blacklisting of your subnet.

For pages that absolutely must be fetched (a specific SKU your client cares about), have a fallback path that uses a headless browser with real UAE residential IP. The browser path costs more per page but solves the small percentage of challenges that the API path cannot handle. Most production setups maintain a 95/5 split: 95% of requests go through the lightweight HTTP+JSON path, 5% fall through to the browser path on challenge.

Working with AED pricing and FX normalization

Pricing in Noon is denominated in AED, and any cross-market analysis requires careful FX normalization. The naive approach of converting at scrape time using a live FX feed introduces noise into your trend lines because exchange rate movements get conflated with real price changes. The correct pattern is to store the price in local AED and apply FX conversion at query time using a daily reference rate.
```
CREATE TABLE fx_rates (
    rate_date DATE NOT NULL,
    base_ccy VARCHAR(3) NOT NULL,
    quote_ccy VARCHAR(3) NOT NULL,
    rate DECIMAL(18,8) NOT NULL,
    PRIMARY KEY (rate_date, base_ccy, quote_ccy)
);
```
Source the daily rates from a reliable feed such as the European Central Bank reference rates or your bank’s wholesale feed. Avoid scraping retail FX rates because they include the bank’s spread and produce inconsistent comparisons. For analyses that span multiple years, also account for currency revaluation events that occasionally happen in emerging markets.

Comparing Noon to other regional marketplaces

Marketplace Country focus Catalogue scale Bot strictness

Noon UAE Large High

Amazon UAE Adjacent markets Medium Medium

Sharaf DG Adjacent markets Smaller Lower

Cross-marketplace analyses help separate platform-specific dynamics from genuine market trends. If a price drops on Noon but stays flat across the comparable competitors, that is a platform-driven event rather than a market-wide signal. Your scraping pipeline should ingest from at least three platforms in any market where you intend to publish category insights.

Operational monitoring and alerting

Every production scraper needs three monitoring layers regardless of target. The first is per-IP success rate over a 5-minute window, alerting if any IP drops below 80%. The second is parser error rate, alerting if more than 1% of fetched pages fail to extract the canonical fields. The third is data freshness, alerting if your downstream consumers see snapshots more than 24 hours old.
```
import time
from collections import deque

class IPHealthTracker:
    def __init__(self, window_seconds: int = 300):
        self.window = window_seconds
        self.events = {}

    def record(self, ip: str, success: bool):
        bucket = self.events.setdefault(ip, deque())
        now = time.time()
        bucket.append((now, success))
        while bucket and bucket[0][0] < now - self.window:
            bucket.popleft()

    def success_rate(self, ip: str) -> float:
        bucket = self.events.get(ip)
        if not bucket:
            return 1.0
        successes = sum(1 for _, ok in bucket if ok)
        return successes / len(bucket)
```
Wire this into Prometheus or your existing observability stack so the on-call engineer sees IP degradation as it happens rather than after the daily snapshot fails. For long-running operations against Noon, IP rotation triggered by the health tracker is more reliable than fixed rotation schedules.

Legal and compliance considerations for UAE

Public product, price, and availability data are generally treated as fair to scrape in most jurisdictions, but UAE has its own consumer protection and personal data frameworks that overlay any general analysis. Confine your collection to non-personal data: SKU identifiers, prices, descriptions, ratings as aggregates, and seller display names. Avoid collecting individual buyer reviews with names, phone numbers, or email addresses attached, and avoid pulling any data behind a login.

For commercial deployment of a scraper that targets Noon, document your basis for processing, your data retention period, and your purpose limitation. Most data protection regimes treat scraped public data more favorably when there is a clear lawful basis and the data is not used for direct marketing to identified individuals. The W3C Web Annotation guidance and similar published frameworks remain useful starting points for documenting your approach.

Pipeline orchestration and scheduling

For any non-trivial scraping operation, a dedicated orchestration layer is the difference between a script you babysit and a service that runs unattended. The two strong open-source choices in 2026 are Prefect 3 and Dagster. Both handle the patterns you need: DAG dependencies, retries, observability, secret management, and dynamic fan-out across IPs and categories.
```
from prefect import flow, task

@task(retries=3, retry_delay_seconds=60)
def fetch_category(category_id: int, page: int):
    return crawl_one_page(category_id, page)

@task
def store_pages(pages: list):
    write_to_db(pages)

@flow(name="Noon-daily-sweep")
def daily_sweep(category_ids: list):
    futures = []
    for cid in category_ids:
        for page in range(1, 50):
            futures.append(fetch_category.submit(cid, page))
    pages = [f.result() for f in futures]
    store_pages(pages)
```
Run the flow on a 6-hour or 24-hour schedule depending on how dynamic the underlying catalogue is. For seasonal markets like apparel where pricing changes daily, a 6-hour cadence catches the meaningful movements without driving up proxy costs unnecessarily. For long-tail categories like books or industrial supplies, daily is sufficient and the cost saving is meaningful.

Sample analytics queries on the collected dataset

Once your snapshots are landing reliably, the analytics layer is where the value materializes. A few queries that consistently come up across Noon datasets:
```
-- Top 50 SKUs by price drop in the last 7 days
SELECT sku, MIN(selling_price) - MAX(selling_price) AS price_drop
FROM snapshot
WHERE snapshot_at > now() - interval '7 days'
GROUP BY sku
ORDER BY price_drop ASC
LIMIT 50;

-- Stock-out frequency per category
SELECT category_id,
       SUM(CASE WHEN in_stock = 0 THEN 1 ELSE 0 END)::float / COUNT(*) AS oos_rate
FROM snapshot
WHERE snapshot_at > now() - interval '30 days'
GROUP BY category_id
ORDER BY oos_rate DESC;

-- New SKUs first seen in the last 14 days
SELECT sku, MIN(snapshot_at) AS first_seen
FROM snapshot
GROUP BY sku
HAVING MIN(snapshot_at) > now() - interval '14 days'
ORDER BY first_seen DESC;
```
These three queries alone power most of the dashboards a category manager wants. Add a brand share view, a seller concentration view, and a campaign-frequency view and you have a competitive intelligence product. The collection layer is the prerequisite; the analytics layer is where you create defensible value.

Common pitfalls when scraping noon

Three failure patterns recur across noon scrapers. The first is country code confusion. noon runs separate storefronts for UAE, Saudi Arabia, and Egypt, each with its own pricing, currency, and stock. The country selector is set via cookie and URL prefix (/uae-en, /saudi-en, /egypt-en). A scraper that drops the country prefix gets redirected by IP geolocation, which corrupts the dataset when proxies move between PoPs. Always pin the country in both the URL and the x-locale header.

The second is double-currency rendering. Saudi pages show prices in SAR but include a USD conversion in the markup for some SKUs. Naive selectors pick whichever appears first in the DOM, which flips between SAR and USD across page versions. Read the price from the structured JSON-LD block, not from visible HTML.

The third is mall-vs-marketplace confusion. noon Mall (curated brand storefronts) and noon Marketplace (third-party sellers) coexist on the same product page. The seller_type and is_noon_mall flags distinguish them. Analytics that treat all listings as one population miss the fact that Mall pricing is more stable while Marketplace pricing reprices weekly. Segment your dataset by seller type before computing trend metrics.

FAQ

Is scraping Noon legal in the UAE?
Public product data is generally considered fair to scrape in most jurisdictions, but UAE law has a strong personal data and consumer protection framework. Restrict your scraping to product, price, and seller data. Avoid pulling buyer reviews with personally identifying details, avoid scraping logged-in pages, and respect any explicit terms of service. For commercial use, consult legal counsel familiar with UAE Federal Decree-Law No. 45 of 2021.

Why do prices differ between Noon UAE and Noon Saudi for the same product?
Each country marketplace is operated as a separate entity with separate seller relationships, separate logistics, and separate pricing decisions. The same SKU can be listed by completely different sellers in the two markets at different prices. Treat each country as an independent dataset for analytical purposes.

Does Noon block VPN traffic?
Noon does not specifically block VPN traffic. It blocks any IP that fingerprints as a data center or hosting provider, which is what most consumer VPN exits look like. Residential and mobile IPs from genuine consumer ISPs in the GCC are the only reliable way to scrape at volume.

How does Noon handle the Friday-Saturday weekend?
Noon catalogue updates and pricing changes continue through the GCC weekend, often with promotional bursts on Friday afternoons. If you are tracking pricing dynamics, your snapshot cadence should not skip weekends.

Can I use Noon’s affiliate API instead of scraping?
Noon runs an affiliate program through Tradedoubler and other networks. The affiliate APIs provide product data for approved affiliates but the catalogue coverage and refresh frequency are limited compared to scraping the public site. For competitive intelligence use cases, scraping remains the higher-fidelity option.

Does noon block residential proxies from non-GCC countries?
Yes, with increasing severity. Casual product lookups from EU or US residential IPs usually succeed, but sustained scraping is throttled within 24 hours. UAE, Saudi, or Egyptian residential or mobile IPs are required for production volume.

How does noon handle price changes during White Friday?
Prices update in waves every 1-3 hours during the campaign. Pre-campaign baselines should be captured at least 7 days before the event to expose true discount depth.

If you are scoping a scraping infrastructure for this market, browse the ecommerce scraping category for tooling reviews, proxy comparisons, and framework deep dives that pair with the patterns above.
May 7, 2026

Country	Currency	Catalogue size estimate	Bot scrutiny	Typical proxy cost
UAE (uae)	AED	8M+ SKUs	High	$$$
Saudi (sa)	SAR	12M+ SKUs	Highest	$$$$
Egypt (eg)	EGP	4M+ SKUs	Medium	$$

Marketplace	Country focus	Catalogue scale	Bot strictness
Noon	UAE	Large	High
Amazon UAE	Adjacent markets	Medium	Medium
Sharaf DG	Adjacent markets	Smaller	Lower

How to scrape Trendyol Turkey in 2026

Scrape Trendyol Turkey at scale and you immediately discover that the marketplace behaves differently from Western counterparts. Trendyol is the dominant ecommerce platform in Turkey, owned by Alibaba Group since 2018, and serves more than 30 million active buyers across categories from fashion to electronics to grocery delivery via Trendyol Go. The site enforces Turkey-specific pricing, lira denomination, KDV (VAT) inclusive display, and a recommendation engine that geo-personalizes feeds based on the visitor IP. If you fetch Trendyol from a US data center IP, you get a stripped-down catalogue with no inventory data and frequent CAPTCHA challenges. If you fetch from a Turkish residential or mobile IP, you get the same payload a real Istanbul shopper sees.

This guide walks through everything you need to scrape Trendyol Turkey product, seller, and price data reliably in 2026. The patterns apply whether you are running price intelligence for a retail brand, building a competitive monitor for a Turkish seller, or feeding a category-level dataset into a machine learning pipeline.

Why Trendyol needs Turkey-resident proxies

Trendyol uses a CDN configuration that classifies the visitor IP into one of three buckets before serving content: domestic Turkish residential, domestic mobile, or international. International visitors get a slow path with aggressive rate limiting, frequent Cloudflare interstitials, and a noticeable degradation in the JSON payloads exposed to the browser. The most obvious symptom is missing seller information and missing stock counts when you scrape from the wrong country.

Use a Turkish residential or mobile proxy and the JSON endpoints behind the product card return full payloads that include the merchant ID, fulfillment warehouse, regional inventory, and KDV-inclusive prices. The cost difference between a Turkish residential pool and a US data center pool is real, but the data quality difference is larger. For most operations the math works out in favor of paying for clean Turkish IPs.

Mapping the Trendyol URL and JSON structure

Trendyol product URLs follow a predictable pattern that includes the brand slug, product slug, and a numeric product content ID. A typical URL looks like https://www.trendyol.com/<brand>/<product-slug>-p-<contentId>. The contentId is the stable identifier you want to capture in your database because the slug portion changes when sellers rename products.

Behind the scenes, Trendyol product pages hydrate from a JSON endpoint at https://public.trendyol.com/discovery-web-productgw-service/api/productDetail/<contentId>. This endpoint returns price, seller list, variants, ratings, and stock per variant in a single response. Hitting this endpoint directly is dramatically faster than parsing the HTML, and it is much less brittle to layout changes.

import httpx
import asyncio
from typing import Optional

TRENDYOL_API = "https://public.trendyol.com/discovery-web-productgw-service/api/productDetail"

HEADERS = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36",
    "Accept": "application/json",
    "Accept-Language": "tr-TR,tr;q=0.9,en;q=0.8",
    "Referer": "https://www.trendyol.com/",
}

async def fetch_product(content_id: int, proxy: str) -> Optional[dict]:
    url = f"{TRENDYOL_API}/{content_id}"
    async with httpx.AsyncClient(proxy=proxy, headers=HEADERS, timeout=20) as client:
        r = await client.get(url)
        if r.status_code == 200:
            return r.json()
        if r.status_code == 429:
            await asyncio.sleep(30)
            return None
        return None

The JSON returns a result object with the canonical product description, a variants array (size, color, stock, sellerId), and a merchantListings array containing every seller offering that product, the price, the cargo cost, and the campaign discount. For competitive intelligence on a single SKU across multiple sellers, this single endpoint is everything you need.

Building a category crawler that respects pagination

For category sweeps, Trendyol exposes a separate search and listing endpoint at https://public.trendyol.com/discovery-web-searchgw-service/v2/api/infinite-scroll. This endpoint accepts a category code, page number, sort option, and filter facets. The infinite-scroll naming reflects the front-end pattern, but the API is plain paginated JSON.

import httpx, asyncio, json

LISTING_API = "https://public.trendyol.com/discovery-web-searchgw-service/v2/api/infinite-scroll"

async def fetch_category_page(category_id: int, page: int, proxy: str):
    params = {
        "wc": category_id,
        "pi": page,
        "culture": "tr-TR",
        "sst": "BEST_SELLER",
        "userGenderId": "",
    }
    async with httpx.AsyncClient(proxy=proxy, headers=HEADERS, timeout=20) as client:
        r = await client.get(LISTING_API, params=params)
        if r.status_code != 200:
            return []
        data = r.json()
        return data.get("result", {}).get("products", [])

async def crawl_category(category_id: int, proxies: list[str], max_pages: int = 50):
    all_rows = []
    for page in range(1, max_pages + 1):
        proxy = proxies[page % len(proxies)]
        rows = await fetch_category_page(category_id, page, proxy)
        if not rows:
            break
        all_rows.extend(rows)
        await asyncio.sleep(2)
    return all_rows

Trendyol caps a single category sweep at roughly 200 pages of 24 products each. For very broad categories you need to subdivide by facet (price band, brand, color) to recover the long tail. The aggregations field in the response tells you which facets are available and the count of products behind each facet.

Handling pricing, KDV, and campaign discounts

Trendyol pricing is messy in the way Turkish ecommerce is messy. Every price is presented as KDV-inclusive (VAT-included), but campaign discounts, basket discounts, and seller-level promo codes mean the headline price almost never matches what the buyer actually pays at checkout. If you are building a competitive intelligence dashboard, decide upfront which price you mean by price.

The product detail JSON exposes four useful fields:

Field	Meaning
`originalPrice`	Sticker price before any discount, KDV inclusive
`sellingPrice`	Current display price after seller discount
`discountedPrice`	Price after Trendyol campaign overlay
`basketPrice`	Price visible to buyer when added to basket (sometimes lower)

For most monitoring use cases, log all four every time. Models that try to compare to competitor sites need discountedPrice because that is the visible price on the listing card. Brand teams enforcing MAP (minimum advertised price) policies need originalPrice and sellingPrice because those are the prices the seller is publishing.

Proxy strategy for Trendyol at scale

Trendyol’s bot detection is layered. The first layer is Cloudflare bot management, which fingerprints TLS, HTTP/2 frames, and header order. The second layer is application-level behavioral analysis that watches for unrealistic page navigation patterns. The third is IP reputation scoring against a Turkish baseline.

For sub-10,000 product per day workloads, a small Turkish residential pool with rotating IPs per request is enough. For 100,000+ products per day, the math shifts toward sticky sessions on mobile IPs. The mobile IP costs more per port, but a single mobile IP can usually sustain a request rate of 5-10 product detail calls per second for hours without being flagged, and the per-product cost works out lower at high volume.

Reasonable starting allocation:

1 mobile port on Türk Telekom or Turkcell: handles 50,000 product details per day
Backup of 50 rotating residential IPs in Turkey: handles category sweeps and seller crawls
Single fallback datacenter pool in Frankfurt: useful only for non-personalized public endpoints like sitemap discovery

For a deeper look at how different proxy categories behave under ecommerce scraping loads, see our residential vs mobile proxy comparison for ecommerce and our proxy provider ranking for 2026.

Avoiding common Trendyol scraping mistakes

The first mistake is treating the productCode in the URL as the product identifier. Trendyol uses three different identifiers in different parts of the system: the SEO slug, the contentId in the URL, and the merchant SKU inside the JSON. Always store the contentId as your primary key. The slug changes, the merchant SKU changes per seller, only the contentId is stable across the catalogue.

The second mistake is parsing the rendered HTML rather than calling the JSON endpoints. Trendyol re-renders the product card layout regularly. The HTML breaks every few months and your scraper needs maintenance. The JSON endpoints are the contract used by Trendyol’s own front end, and they are far more stable.

The third mistake is ignoring the seller dimension. A product page can list 30+ sellers offering the same SKU at different prices and shipping conditions. If you only capture the buy-box winner, you miss the entire competitive landscape on the listing. The merchantListings array is the source of truth for seller-level price intelligence.

Storing Trendyol data for analytics

For most workloads, a wide table per product snapshot works well in DuckDB or PostgreSQL. The schema should track the four price fields above, plus seller, stock per variant, ratings, review count, and the campaign banner if any. Take snapshots at a frequency aligned to your decision cadence. For dynamic-pricing competitors, every 4-6 hours captures meaningful change. For weekly category reports, a daily snapshot is enough.

CREATE TABLE trendyol_product_snapshot (
    snapshot_at TIMESTAMP NOT NULL,
    content_id BIGINT NOT NULL,
    seller_id BIGINT NOT NULL,
    original_price DECIMAL(12,2),
    selling_price DECIMAL(12,2),
    discounted_price DECIMAL(12,2),
    basket_price DECIMAL(12,2),
    in_stock INT,
    rating DECIMAL(3,2),
    review_count INT,
    campaign_text TEXT,
    PRIMARY KEY (snapshot_at, content_id, seller_id)
);
CREATE INDEX trendyol_content_idx ON trendyol_product_snapshot(content_id);

A 100k-product daily snapshot table will grow to roughly 30M rows per year. DuckDB handles that comfortably on a laptop. PostgreSQL handles it comfortably on a single node. Either way, partition by snapshot_at weekly or monthly to keep query plans tight.

Detecting and routing around CAPTCHA challenges

When Trendyol flags your traffic, the response is usually a Cloudflare interrogation page rather than an HTTP 4xx. Your scraper needs to detect this content-type swap explicitly. Look for the signature cf-mitigated header, the presence of __cf_chl_ cookies, or HTML containing Just a moment.... Treat any of these as a soft block.

def is_challenged(response) -> bool:
    if response.status_code in (403, 503):
        return True
    if "cf-mitigated" in response.headers:
        return True
    if "__cf_chl_" in response.headers.get("set-cookie", ""):
        return True
    body = response.text[:2000].lower()
    return "just a moment" in body or "checking your browser" in body

For pages that absolutely must be fetched (a specific SKU your client cares about), have a fallback path that uses a headless browser with real Turkey residential IP. The browser path costs more per page but solves the small percentage of challenges that the API path cannot handle. Most production setups maintain a 95/5 split: 95% of requests go through the lightweight HTTP+JSON path, 5% fall through to the browser path on challenge.

Working with TRY pricing and FX normalization

Pricing in Trendyol is denominated in TRY, and any cross-market analysis requires careful FX normalization. The naive approach of converting at scrape time using a live FX feed introduces noise into your trend lines because exchange rate movements get conflated with real price changes. The correct pattern is to store the price in local TRY and apply FX conversion at query time using a daily reference rate.

CREATE TABLE fx_rates (
    rate_date DATE NOT NULL,
    base_ccy VARCHAR(3) NOT NULL,
    quote_ccy VARCHAR(3) NOT NULL,
    rate DECIMAL(18,8) NOT NULL,
    PRIMARY KEY (rate_date, base_ccy, quote_ccy)
);

Comparing Trendyol to other regional marketplaces

Marketplace	Country focus	Catalogue scale	Bot strictness
Trendyol	Turkey	Large	High
Hepsiburada	Adjacent markets	Medium	Medium
GittiGidiyor	Adjacent markets	Smaller	Lower

Cross-marketplace analyses help separate platform-specific dynamics from genuine market trends. If a price drops on Trendyol but stays flat across the comparable competitors, that is a platform-driven event rather than a market-wide signal. Your scraping pipeline should ingest from at least three platforms in any market where you intend to publish category insights.

Operational monitoring and alerting

import time
from collections import deque

class IPHealthTracker:
    def __init__(self, window_seconds: int = 300):
        self.window = window_seconds
        self.events = {}

    def record(self, ip: str, success: bool):
        bucket = self.events.setdefault(ip, deque())
        now = time.time()
        bucket.append((now, success))
        while bucket and bucket[0][0] < now - self.window:
            bucket.popleft()

    def success_rate(self, ip: str) -> float:
        bucket = self.events.get(ip)
        if not bucket:
            return 1.0
        successes = sum(1 for _, ok in bucket if ok)
        return successes / len(bucket)

Wire this into Prometheus or your existing observability stack so the on-call engineer sees IP degradation as it happens rather than after the daily snapshot fails. For long-running operations against Trendyol, IP rotation triggered by the health tracker is more reliable than fixed rotation schedules.

Legal and compliance considerations for Turkey

Public product, price, and availability data are generally treated as fair to scrape in most jurisdictions, but Turkey has its own consumer protection and personal data frameworks that overlay any general analysis. Confine your collection to non-personal data: SKU identifiers, prices, descriptions, ratings as aggregates, and seller display names. Avoid collecting individual buyer reviews with names, phone numbers, or email addresses attached, and avoid pulling any data behind a login.

For commercial deployment of a scraper that targets Trendyol, document your basis for processing, your data retention period, and your purpose limitation. Most data protection regimes treat scraped public data more favorably when there is a clear lawful basis and the data is not used for direct marketing to identified individuals. The W3C Web Annotation guidance and similar published frameworks remain useful starting points for documenting your approach.

Pipeline orchestration and scheduling

from prefect import flow, task

@task(retries=3, retry_delay_seconds=60)
def fetch_category(category_id: int, page: int):
    return crawl_one_page(category_id, page)

@task
def store_pages(pages: list):
    write_to_db(pages)

@flow(name="Trendyol-daily-sweep")
def daily_sweep(category_ids: list):
    futures = []
    for cid in category_ids:
        for page in range(1, 50):
            futures.append(fetch_category.submit(cid, page))
    pages = [f.result() for f in futures]
    store_pages(pages)

Sample analytics queries on the collected dataset

Once your snapshots are landing reliably, the analytics layer is where the value materializes. A few queries that consistently come up across Trendyol datasets:

-- Top 50 SKUs by price drop in the last 7 days
SELECT sku, MIN(selling_price) - MAX(selling_price) AS price_drop
FROM snapshot
WHERE snapshot_at > now() - interval '7 days'
GROUP BY sku
ORDER BY price_drop ASC
LIMIT 50;

-- Stock-out frequency per category
SELECT category_id,
       SUM(CASE WHEN in_stock = 0 THEN 1 ELSE 0 END)::float / COUNT(*) AS oos_rate
FROM snapshot
WHERE snapshot_at > now() - interval '30 days'
GROUP BY category_id
ORDER BY oos_rate DESC;

-- New SKUs first seen in the last 14 days
SELECT sku, MIN(snapshot_at) AS first_seen
FROM snapshot
GROUP BY sku
HAVING MIN(snapshot_at) > now() - interval '14 days'
ORDER BY first_seen DESC;

Common pitfalls when scraping Trendyol

Three failure modes account for most production incidents on Trendyol scrapers. The first is silent variant collapse. The product detail endpoint nests variant arrays inside allVariants and slicingAttributes. Naive flatteners pick the first variant and drop the rest, which means size and color price differences vanish from the dataset. Always iterate the full variant array and emit one row per content_id plus listing_id pair.

The second is timezone drift on price-change events. Trendyol’s backend timestamps are in Europe/Istanbul (UTC+3, no DST), but many cloud functions default to UTC. If you compare today’s price snapshot taken at 09:00 UTC against yesterday’s snapshot taken at 22:00 UTC you are comparing two windows separated by 11 hours, not 24. Pin the snapshot timestamp to the local Trendyol day and store both timestamps explicitly.

The third is campaign-price contamination. The originalPrice and sellingPrice fields carry the headline price, but campaigns like flashDiscount and crossDiscount apply at checkout and only appear inside the promotions array. A scraper that stores sellingPrice as the realized price will overstate revenue by 8-15% during major campaigns like Legendary Friday and Birthday Week. Compute the realized price by walking the promotions array and applying each rule in order.

FAQ

Do I need to log in to scrape Trendyol product data?
No. Product details, category listings, and seller data are all available without authentication. Login is only required if you want to scrape order history, wallet balance, or personalized recommendations. For 99% of competitive intelligence and price monitoring use cases, anonymous scraping is sufficient.

Will the HTML scrape work without proxies if I rate limit aggressively?
You can pull a few hundred product pages per day from a single non-Turkish IP without immediate bans. Beyond that you hit either Cloudflare interstitials or a soft block where the JSON endpoints start returning 403. For any sustained operation, Turkish residential or mobile IPs are required.

How fresh is the price data on the public JSON endpoint?
The productDetail endpoint reflects current selling state with a CDN cache lifetime of about 60-180 seconds. For most monitoring workloads that is real-time enough. If you need true real-time pricing, the legacy productgw-service endpoint occasionally bypasses cache, but it is undocumented and can change without notice.

Does Trendyol expose stock counts or just availability?
The product detail JSON includes a stock integer for each variant. For top-selling SKUs, sellers often inflate stock counts to keep the buy box. For mid-tier and long-tail SKUs, the stock value is usually accurate to within 10-20%. Use it for trend signals rather than absolute inventory truth.

Can I scrape Trendyol Go (grocery) using the same approach?
Trendyol Go uses a different subdomain and a different API surface focused on hyper-local fulfillment. The proxy and rate-limit principles transfer, but the endpoints and JSON shape are different. Plan for separate code paths if your project covers both retail and grocery.

How do I detect when Trendyol rotates its anti-bot challenge variant?
Watch for a sudden jump in the share of responses returning HTML rather than JSON for the same endpoint. A rotation typically lifts the HTML share above 5% within an hour and stabilizes after 24-48 hours as your fingerprint pool adapts.

What is the right cadence for category-level snapshots vs SKU-level snapshots?
Category listings refresh every 6-12 hours for most analytical use cases. SKU-level price and stock snapshots run hourly for top 1000 SKUs and every 4-6 hours for the long tail.

If you are scoping a scraping infrastructure for this market, browse the ecommerce scraping category for tooling reviews, proxy comparisons, and framework deep dives that pair with the patterns above.

May 7, 2026

Proxy Manager Showdown: BrightData vs Proxifier vs Custom (2026)

If you’re managing more than a handful of proxy endpoints, the proxy manager you pick shapes everything: rotation logic, auth overhead, observability, and whether you’re debugging at 2am because sessions leaked. Three tools dominate real proxy infrastructure in 2026: BrightData’s Proxy Manager (formerly Luminati), Proxifier, and rolling your own with a lightweight custom stack. Each solves a different problem. Using the wrong one costs hours, sometimes days.

What each tool actually does

BrightData Proxy Manager is a self-hosted Node.js daemon you run locally or on a server. It sits between your scraper and BrightData’s residential, datacenter, or mobile network, handling session persistence, rotation intervals, and request logging out of the box. You configure it through a web UI or a JSON file. It’s free to self-host — you pay for bandwidth through BrightData’s network, not the software itself.

Proxifier is a Windows/macOS desktop app that forces arbitrary processes through a proxy without those processes knowing about it. It operates at the OS socket level. No code changes required. If you need to route a legacy binary, a GUI browser, or a tool that doesn’t natively support proxy config through SOCKS5, Proxifier is the answer.

Custom proxy managers — usually a thin Python or Node.js script in front of a proxy pool — exist because neither of the above fits every use case. You control the rotation logic, session labeling, failover behavior, and logging. You also own the bugs.

Feature comparison

Feature	BrightData PM	Proxifier	Custom (Python/Node)
Rotation logic	Built-in (time/request/session)	None	You build it
Auth handling	Automatic (BrightData creds)	Manual per rule	Flexible
OS-level proxy routing	No	Yes	No
Provider-agnostic	No	Yes	Yes
Observability	Dashboard + request logs	Basic connection log	Whatever you instrument
Upfront cost	Free (BW billed separately)	$39.95 one-time	Dev time
Best for	BrightData users at scale	Routing any process	Multi-provider, custom logic

When BrightData Proxy Manager actually wins

If you’re already on BrightData’s network and running 10,000-plus requests per day, their Proxy Manager is genuinely useful. Session persistence across requests, per-country targeting, automatic retry on 429/503, and a dashboard that shows live throughput and error rates. The JSON config approach lets you version-control your proxy setup:

{
  "port": 24000,
  "zone": "residential",
  "country": "sg",
  "session_duration": 60,
  "rotate_session": true,
  "max_requests": 100
}

The catch is lock-in. BrightData PM only works with BrightData’s network. The moment you want to mix in a cheaper datacenter pool from Oxylabs or IPRoyal, which we compared in Oxylabs vs IPRoyal 2026: Mid-Tier Residential Proxy Showdown, you’re routing requests manually again. The tool also has real memory overhead — it’s a Node.js process and gets finicky on low-RAM VMs under 2GB. Expect around 300-400MB resident just to keep the daemon alive.

When Proxifier makes more sense

Proxifier’s use case is narrow, but when it fits, nothing else does. Say you’re scraping a target that requires an authenticated session via a desktop app, or testing how a specific client behaves behind a rotating mobile proxy. You can’t inject proxy settings into those processes at the code level. Proxifier intercepts socket calls at the OS layer and redirects them through whatever SOCKS5 or HTTP proxy you specify.

It works well alongside browser-based proxy switchers — you handle Chrome or Firefox with an extension (the 2026 browser tool options are compared in FoxyProxy vs Proxy SwitchyOmega vs Proxy Switcher (2026 Browser Tools)) and Proxifier handles everything else on the same machine. Split the responsibility cleanly and it mostly stays out of your way.

Proxifier doesn’t rotate. You’d need a rotating endpoint from your provider or a local rotator, then point Proxifier at that. It’s a forwarder, not a manager.

Building a custom proxy manager

Most teams reach for a custom solution when they hit one of these walls:

Multiple proxy providers (BrightData residential + a cheaper datacenter pool + mobile ISP proxies from a different vendor)
Custom session stickiness logic, for example pinning sessions per account ID rather than per time interval
Enriched logging with request URL, status code, proxy used, and latency in one place
Failover where if provider A returns 429, the request retries immediately on provider B

A minimal custom manager in Python looks like this:

import httpx
import random

PROXY_POOL = [
    "http://user:pass@gate.provider1.com:8080",
    "http://user:pass@gate.provider2.com:8080",
]

def get_proxy():
    proxy = random.choice(PROXY_POOL)
    return {"http://": proxy, "https://": proxy}

def fetch(url):
    with httpx.Client(proxies=get_proxy(), timeout=10) as client:
        return client.get(url)

That’s a starting point, not production code. Real custom managers need health-check loops, per-proxy error counters, and sticky session maps keyed to whatever your scraper treats as a “session.” Before wiring a new proxy pool into a custom stack, verify each endpoint first — the Best CLI Tools for Proxy Testing in 2026: curl, httpie, mitmproxy Patterns guide covers that verification workflow so you’re not chasing phantom failures later.

For teams doing proxy-routed API scraping inside tools like Bruno or Insomnia, there’s a separate layer to get right. Proxy Rotation in Postman / Bruno / Insomnia for API Scraping (2026) covers that integration specifically.

How to pick between the three

You’re on BrightData and doing high volume: use BrightData Proxy Manager. Don’t reinvent what they’ve already built.
You need to proxy arbitrary OS processes or desktop apps: Proxifier. Nothing else comes close for that use case.
You’re running a multi-provider pool, need custom rotation logic, or want full observability: build custom. It’s a weekend project once, then you own it.
You’re starting out with one provider and low volume: BrightData PM or a single-endpoint requests setup is fine. Don’t over-engineer it.
You need to mix browser and non-browser traffic on the same machine: Proxifier plus a browser extension handles both cleanly.

One thing worth saying clearly: a lot of teams use BrightData PM because it was the default recommendation two or three years ago. That’s not a reason to keep using it in 2026 if your proxy spend is split across providers or your rotation logic has grown past what the GUI can express. The tool is good at what it does. It’s just not good at everything.

Bottom line

BrightData Proxy Manager is the right call if you’re committed to their network at scale; Proxifier solves the OS-layer routing problem better than anything else on the market; and a custom manager is the correct move the moment your proxy logic outgrows a single provider or needs real observability. Pick based on your actual constraints, not what’s easiest to demo. DRT covers proxy infrastructure, scraping toolchains, and data collection pipelines regularly — bookmark the publication if this layer is central to your stack.

May 7, 2026

Cookie Jar Persistence Patterns for Logged-In Scrapers (2026)

Most logged-in scrapers do not fail because the parser is weak, they fail because session state gets treated like a disposable detail. cookie jar persistence is the difference between a scraper that stays warm for 21 days and one that burns a fresh login every hour, trips fraud rules, and drags your proxy bill upward. in 2026, with more sites binding sessions to device hints, IP clusters, and risk scores, the winning pattern is not just “save cookies”, it is “persist the right state with explicit invalidation rules”.

Why Persistence Matters More in 2026

Five years ago, a flat Netscape cookie file was enough for many targets. now, major retailers, SaaS dashboards, marketplaces, and B2B portals often combine cookies with local storage tokens, CSRF state, signed session metadata, and lightweight browser fingerprints. if your job restarts without restoring that bundle coherently, you get soft logged out or challenged.

The economics are blunt. a clean re-login flow through Playwright with proxy warm-up, a CAPTCHA solve, and a post-login checkpoint often costs 8 to 45 seconds. multiplied across 500 accounts, that becomes hours of dead time and a spike in anti-bot exposure.

There is also an operational angle. the more often you hit login and recovery flows, the more often you encounter MFA, device verification, or OTP detours. if you already have a stable jar strategy, you reduce how often you need the heavier recovery playbooks discussed in How to Handle 2FA / OTP Walls in Scrapers: Patterns for 2026.

What To Persist, And Where Teams Usually Get It Wrong

The common mistake is persisting only HTTP cookies from requests or httpx, while the actual logged-in state also depends on browser-side storage and request context. for browser-driven targets, persist these pieces together:

cookies, including expiry, domain, path, secure, and httpOnly flags
local storage keys used for access tokens, feature gates, or device IDs
session storage, only when the target actually reads it after restore
CSRF or anti-forgery tokens, if they are long-lived enough to reuse
account-to-proxy affinity metadata
user agent and key browser version details

If you ignore affinity metadata, the jar restores fine, but the next request exits from a different ASN or country and triggers a risk review. a cookie jar is not just a blob, it is a binding between identity, network posture, and client profile.

Here is the practical comparison most teams end up making:

Pattern	Good for	Breaks when	Typical cost profile
flat file cookie jar	simple `requests` jobs, low-value sessions	multi-worker concurrency, token drift, host crashes	cheapest, but brittle
SQLite-backed jar	single host fleets, moderate concurrency	cross-region scaling, lock contention	low cost, solid baseline
Redis session store	distributed workers, account pools, fast invalidation	poor TTL policy, missing encryption	excellent operationally, moderate complexity
browser context snapshots	Playwright-heavy targets, JS auth flows	browser version mismatch, oversized blobs	high storage, best fidelity

For Python-only HTTP clients, httpx plus a serialized Cookies object can work if the target is straightforward. for browser-led flows, Playwright storage state is usually the right primitive because it captures cookies and local storage together. requests and LWP::UserAgent can still be effective for thin authenticated endpoints, but they are weaker options once the site expects real browser continuity.

Storage Patterns That Actually Hold Up

The most reliable pattern for 2026 is tiered persistence. do not put every account session into one global store with no structure. split by account, target, and environment, then attach policy to each bucket.

use a stable account key such as target:account_id.
store the session payload plus proxy_pool_id, user_agent, created_at, last_seen_at, and risk_score.
track a short heartbeat on successful authenticated requests.
expire aggressively when the site rotates auth aggressively, otherwise keep warm and refresh opportunistically.

A realistic setup is Redis for hot sessions and SQLite for local fallback or forensic replay. Redis gives you fast invalidation and distributed access. SQLite gives you an inspectable local artifact during incident response.

Short-lived sessions should not be refreshed on every request. that is wasteful and can increase write amplification by 10x. instead, refresh on meaningful events:

after successful login
after token rotation detected in response headers or storage state
after completing a high-risk checkpoint flow
every N successful authenticated page loads, typically 10 to 25

If you are also recycling CAPTCHA solves or challenge bypass artifacts, keep those stores logically separate from the cookie jar. mixing them tends to create bad invalidation logic. the reuse economics are related, but the lifecycle is different, which is why the operational pattern in Captcha-Token Recycling: Solving Once, Reusing 50 Times (2026 Patterns) should remain its own subsystem.

Rotation, Expiry, And Invalidation Rules

A persistent jar is only useful if you are willing to kill it at the right time. too many teams let expired or poisoned sessions bounce around the queue for hours. that creates request storms and account locks.

Use three states, not two: healthy, suspect, and dead. suspect is the important one. move a jar there after one hard 401, one redirect to login, or one anti-bot interstitial that was not present on the previous request. only retry from suspect once. if it fails again, mark dead and trigger re-auth.

This is where simple metrics pay off. for one retail-monitoring fleet I have seen, adding suspect-state handling reduced wasted authenticated retries by 42 percent in the first week.

Practical invalidation rules:

kill immediately on password reset, forced logout, or explicit session revocation
downgrade to suspect on one anomalous geo mismatch
cap session age even if still working, usually 7 to 30 days depending on target
rotate browser major versions carefully, because version jumps can poison otherwise valid state

Do not ignore clock drift. signed cookies and CSRF bundles often fail when containers drift by more than a minute or two.

A Concrete Implementation Pattern

For browser-first targets, Playwright plus Redis is the current sweet spot. store Playwright storage_state, encrypt it at rest, and restore only with the same browser family and a proxy from the same pool. for simpler HTTP-only targets, httpx with a Redis-backed cookie store is lighter and cheaper.

Example, a compact Python pattern that restores a session, uses it, and writes back only after authenticated success:

import json
import redis
import httpx

r = redis.Redis(host="127.0.0.1", port=6379, decode_responses=True)
key = "session:example:acct_1837"

jar_json = r.get(key)
cookies = httpx.Cookies()
if jar_json:
    for c in json.loads(jar_json):
        cookies.set(
            c["name"],
            c["value"],
            domain=c.get("domain"),
            path=c.get("path", "/"),
        )

with httpx.Client(cookies=cookies, timeout=20.0) as client:
    resp = client.get("https://target.example.com/dashboard")
    if resp.status_code == 200 and "account overview" in resp.text.lower():
        snapshot = []
        for c in client.cookies.jar:
            snapshot.append({
                "name": c.name,
                "value": c.value,
                "domain": c.domain,
                "path": c.path,
            })
        r.setex(key, 86400, json.dumps(snapshot))

This is intentionally plain. in production, add AES-GCM encryption, version your payload schema, and attach metadata outside the raw cookie array. if you are handling 5,000 to 50,000 active sessions, add a janitor that prunes dead keys and reports anomaly rates per target.

One more blunt recommendation, do not share one persisted jar across workers without ownership rules. assign a lease for a short window, 60 to 300 seconds is typical, so two workers do not mutate the same session concurrently.

Bottom line

Treat cookie persistence as a first-class auth subsystem, not a convenience feature. for most logged-in scrapers in 2026, the best default is Playwright storage state or httpx cookies backed by Redis, with proxy affinity, explicit suspect-state handling, and hard invalidation rules. DRT-style coverage tends to focus on bypass tricks, but in practice, disciplined session storage is what keeps authenticated fleets fast, quiet, and cheap.

May 7, 2026

How to Scrape DoorDash Restaurants and Menus (2026)

DoorDash serves over 700,000 restaurants across the US, Canada, and Australia — and if you’re doing competitive pricing research, building a food delivery aggregator, or tracking menu trends across cities, scraping DoorDash restaurant menus is a very real engineering task. this article covers how DoorDash’s frontend and API actually work in 2026, what tools cut through their bot defenses, and how to extract structured menu and pricing data at scale without burning your IP pool.

how DoorDash serves its menu data

DoorDash is a Next.js app. the menu page you see in a browser is server-side rendered, but the actual item data comes from a graphQL endpoint: consumer-mobile-bff.doordash.com/consumer/graphql. same endpoint their mobile apps use.

the good news: that API returns clean JSON with item names, prices, descriptions, calories, and modifiers (size, add-ons, etc). the bad news: it requires authenticated session cookies and a valid x-channel-id header that rotates. unauthenticated requests get a 401 or a silent empty response. which is worse than a 401, honestly.

the HTML rendered pages also include a __NEXT_DATA__ JSON blob embedded in a <script> tag. for light scraping this is often easier to parse than reverse-engineering the graphQL schema, because it has most of the same data and doesn’t require auth headers.

import httpx
from bs4 import BeautifulSoup
import json

def extract_next_data(url: str, headers: dict) -> dict:
    r = httpx.get(url, headers=headers, follow_redirects=True)
    soup = BeautifulSoup(r.text, "html.parser")
    tag = soup.find("script", id="__NEXT_DATA__")
    if not tag:
        return {}
    return json.loads(tag.string)

headers = {
    "user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36",
    "accept-language": "en-US,en;q=0.9",
}
data = extract_next_data("https://www.doordash.com/store/mcdonalds-new-york-12345/", headers)

the __NEXT_DATA__ path to menu items is props.pageProps.storeMenuProps.menuBook.categories[]. items sit inside each category with price (in cents), name, description, and imageUrl.

anti-bot defenses you’ll actually hit

DoorDash runs Cloudflare plus an internal bot management layer that fingerprints TLS JA3/JA4, HTTP/2 settings, and browser behavior. at moderate scale (200+ requests/hour from one IP) you’ll see:

403 Forbidden with a Cloudflare challenge page
429 Too Many Requests on the graphQL endpoint
silent 200 responses returning empty menu data (the sneaky one)
captcha interstitials on restaurant listing pages

rotating proxies help, but the fingerprinting layer means bare requests from httpx or requests get flagged even with valid cookies. you need TLS fingerprinting to match a real browser’s Client Hello. curl-cffi is the standard fix in 2026 — it mimics the TLS handshake of Chrome or Safari.

from curl_cffi import requests as cffi_requests

r = cffi_requests.get(
    "https://www.doordash.com/store/some-restaurant-99999/",
    impersonate="chrome120",
    headers=headers,
)

for serious scale, headless browsers (Playwright with stealth) or commercial scraping APIs handle the fingerprinting for you. similar defenses show up across the food delivery space: if you’re also pulling from Grubhub, the approach in How to Scrape Grubhub Menu Data Across Cities (2026) covers that platform’s specific quirks in more detail.

scraping at scale: proxies, rate limits, and infrastructure

a single residential IP running 50 requests/hour stays under the radar for light single-restaurant monitoring. anything broader needs IP rotation.

approach	cost	scale	detection risk
datacenter proxies	~$1-3/GB	high throughput	high (easy fingerprint)
residential proxies	~$5-15/GB	medium-high	medium
mobile proxies	~$15-40/GB	medium	low (best for auth’d flows)
scraping API (Apify, ScraperAPI, Brightdata)	~$1-5/1000 req	elastic	low (managed for you)

for menu data without login, residential proxies at 1 request per 3-5 seconds per IP are stable. if you need to scrape authenticated cart and pricing flows (DoorDash shows different prices with DashPass vs without), mobile proxies are more reliable because they share the same IP type the DoorDash mobile app actually uses.

session management matters too. DoorDash sets dd_access_token and dd_refresh_token cookies on login. if you’re scraping at scale with real accounts, rotate accounts and cookies together, not just IPs. a valid account with a stale cookie from a different IP triggers a re-auth challenge.

Uber Eats has a similar account-cookie coupling but is generally more tolerant of IP switches. there’s a full breakdown in How to Scrape Uber Eats Restaurant Listings at Scale (2026) if you’re running a multi-platform pipeline.

structuring the output: menus, modifiers, and pricing

raw DoorDash menu data is nested. a basic burger listing can have 4 levels of modifier groups (size, toppings, sauces, add-ons), and each modifier has its own price delta. flattening this into a usable schema takes some thought upfront or you’ll regret it later.

a clean schema for analysis looks like this:

restaurant_id (DoorDash internal store ID)
restaurant_name
scraped_at (unix timestamp)
item_id, item_name, category
base_price_cents
modifier_group_name, modifier_name, modifier_price_delta_cents
item_calories, item_image_url

steps to build a clean pipeline:

fetch restaurant listing pages or search results to collect store IDs and slugs
for each store ID, fetch the menu page or graphQL endpoint
extract __NEXT_DATA__ or parse the graphQL response
flatten the nested modifier structure into rows (one row per item-modifier combo)
write to postgres or parquet with scraped_at for time-series tracking
schedule via Airflow or a simple cron every 6-24 hours depending on refresh needs

DoorDash does update prices dynamically — surge pricing on busy nights is documented — so if you’re doing price intelligence the timestamp matters more than most fields.

if your scope extends beyond the US market, How to Scrape Deliveroo Restaurant Menus UK + EU (2026) and How to Scrape Foodpanda Menu Data Asia + EU (2026) cover the regional equivalents with their own anti-bot quirks.

legal and rate-limit considerations

DoorDash’s terms of service prohibit scraping. enforcement has been inconsistent, but the hiQ vs LinkedIn precedent and its follow-on cases suggest that scraping publicly visiable data is defensible for research under the CFAA in the US. that said, this isn’t legal advice, and commercial use cases should run the ToS language by a lawyer first.

practically: DoorDash doesn’t gate its menus behind a login (you can see prices without an account). scraping publicly visible pages for research, price monitoring, or competitive analysis sits in a different risk bucket than scraping behind auth — similar to how How to Scrape Temu Product Data and Pricing in 2026 (Anti-Bot Guide) frames public product pricing.

and slow down. there’s no reason to hit 100 requests per second when 2-5 is fine for most use cases. fast and aggressive gets your proxy pool flagged and your costs up. slow and steady keeps you out of their radar entirely.

Bottom line

for most menu scraping needs, the __NEXT_DATA__ approach with curl-cffi and residential proxy rotation is the fastest path to production-ready data. if you need DashPass pricing or modifier-level accuracy at high volume, the graphQL endpoint with proper session management is worth the extra setup. DRT covers the full stack of food delivery scraping across platforms — check the sibling guides above if your pipeline spans more than one market.

May 7, 2026

How to Scrape FanDuel Odds and Lines in 2026

FanDuel is one of the hardest sportsbooks to scrape programmatically in 2026. Their odds pages load dynamically via a private REST API, they rotate Cloudflare JS challenges, and their IP detection is aggressive — geo-restricted by state on top of that. But the data is valuable: real-time lines on NFL, NBA, MLB, NHL, PGA, and dozens of other markets, often updating every few seconds during live games. This guide covers what actually works, what will get you blocked fast, and where the engineering tradeoffs land.

How FanDuel’s odds delivery actually works

FanDuel doesn’t serve odds as static HTML. The page shell loads, then the browser fires a series of fetch calls to https://sbapi.fanduel.com/api/.... Most of the useful data comes from two undocumented endpoints:

GET /api/event-page?eventId=... returns full market and runner data for a single game
GET /api/content-managed-page?page=SPORT_HOME&... returns lobby odds for a sport’s main page

Both return JSON. No API schema is published, but the response shape has been stable since late 2024.

The complication: every request requires a valid X-Auth-Token header. FanDuel generates this client-side via a fingerprinting flow that varies by state. You can’t replicate it with a static string — you either harvest it from a live browser session or reverse the generation logic, which is obfuscated and changes periodically.

Extracting the auth token and session cookies

The most reliable method is headless browser token extraction. Launch Playwright, navigate to the target state URL (e.g., fanduel.com/pennsylvania), wait for network idle, and intercept outgoing XHR calls to capture the token.

from playwright.sync_api import sync_playwright

def get_fanduel_token(state_url: str) -> dict:
    with sync_playwright() as p:
        browser = p.chromium.launch(headless=True)
        ctx = browser.new_context(
            user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
        )
        page = ctx.new_page()
        captured = {}

        def handle_request(req):
            if "sbapi.fanduel.com" in req.url:
                token = req.headers.get("x-auth-token")
                if token:
                    captured["token"] = token
                    captured["cookies"] = ctx.cookies()

        page.on("request", handle_request)
        page.goto(state_url, wait_until="networkidle")
        browser.close()
        return captured

Token lifetime is roughly 30 to 60 minutes. Rotate fresh sessions before expiry rather than refreshing mid-scrape — the silent failure mode is a 200 response with empty markets[], not a clean 401.

One thing to watch: FanDuel geo-gates their API by actual IP location, not just the URL slug. Hitting fanduel.com/new-jersey from a California IP gets a 403 or a redirect to a blocked-state page. You need a residential or mobile proxy in the target state. This is the same geo-blocking mechanic that drives up proxy costs in scraping BetMGM lines across states — budget for it before you start.

Parsing the odds response

Once you have a valid token and matching cookies, the API calls are straightforward. The event-page endpoint returns a nested structure: markets[] containing runners[], each with a winRunnerOdds.americanDisplayOdds.americanOdds field (deeply nested, yes).

Key fields per runner:

runnerId — stable identifier for a team or player outcome
runnerName — human-readable label
americanOdds — moneyline or spread price
handicap — the spread value (null for moneylines)
marketType — e.g., MATCH_ODDS, ASIAN_HANDICAP, TOTAL_SCORE
inPlay — boolean, critical if you’re tracking live odds

For spread and totals markets, handicap carries the line number. Cross-check marketType before assuming structure — FanDuel sometimes nests alternate lines inside the same market object, which will corrupt your data if you parse it naively.

If you’re building a cross-book model, you’ll want to pull from sharper books too. The approach in how to scrape Pinnacle sports lines for sharp models is a solid reference: Pinnacle’s API is more permissive and serves as a useful no-vig baseline for line movement analysis.

Anti-bot detection and what actually bypasses it

FanDuel runs Cloudflare with JS challenge mode on most state subdomains. Headless Chromium without stealth patches fails the challenge in roughly 80% of attempts as of early 2026.

Minimum viable bypass stack, in order:

Use playwright-stealth or puppeteer-extra-plugin-stealth to patch navigator properties
Disable WebDriver flags (--disable-blink-features=AutomationControlled)
Run a real Chrome binary rather than bundled Chromium where possible
Route through a residential proxy with a matching state IP
Add realistic mouse movement and short page interaction before the token extraction

FanDuel’s detection is noticeably more aggresive than DraftKings. If you’re already scraping that book, the guide on how to scrape DraftKings odds and lines covers similar stealth patterns — but FanDuel requires stricter fingerprint consistency across the full session (same IP, same user agent, same viewport from page load through API calls).

For scale above 20 to 30 concurrent sessions, you’ll burn through residential proxies fast. Mobile proxies on a rotating pool help with detection rates but cost more. That tradeoff is real and there’s no clean answer.

Bet365’s session architecture has similar complexity. The rotation and geo-routing patterns in how to scrape Bet365 odds around the world apply here too, particularly if you’re running across multiple state jurisdictions simultaneously.

Provider and approach comparison

Method	Token required	Geo-restriction	Scale	Relative cost
Headless browser (Playwright)	Auto-captured	Yes, state IP	Low-medium	Medium
Reverse-engineered direct API	Manual extraction	Yes, state IP	High	Low (after setup)
Third-party odds API (OddsAPI, Sportradar)	No	No	High	High
Managed scraping service (Bright Data, ScrapeHero)	No	Handled	High	Very high

For one-off research or model inputs, a third-party odds API is worth it. OddsAPI covers FanDuel lines and costs under $100/month at moderate volume. It doesn’t expose market depth or alternate lines, so if you need that granularity you’re back to direct access. Sportradar has full coverage but pricing is enterprise-grade.

For teams building internal tooling that needs raw market data without an intermediary, the headless extraction approach scales reasonably to around 5 to 10 state markets before operational complexity gets painful. Beyond that, the time cost of maintaining session rotation and stealth patches usually justifies a managed service.

If your scraping pipeline spans data categories beyond sports, it’s worth studying how G2 and Capterra scraping pipelines handle rate limiting and token rotation at scale — the underlying queue and retry architecture transfers well to high-frequency odds collection.

Common error codes and what they mean

Error	Likely cause	Fix
`403 Forbidden`	IP geo-mismatch or Cloudflare block	Rotate proxy, re-establish session
`401 Unauthorized`	Token expired or missing header	Re-extract from fresh browser session
`429 Too Many Requests`	Rate limit hit (~20 req/min per IP)	Back off 60s, slow cadence
`503 Service Unavailable`	High-traffic game event surge	Retry with exponential backoff
`200` with empty `markets[]`	Stale session (silent failure)	Log market count, alert on zero

The silent 200 with empty markets is the one that burns you in production. Add a check on len(response["markets"]) per response and treat zero as a session-expired signal.

Bottom line

FanDuel is scrappable in 2026 but it takes real infrastructure: headless stealth browsers, state-matched residential proxies, and solid session rotation. If you just need odds for model inputs or comparison tools, OddsAPI covers FanDuel and saves weeks of engineering overhead. If you need raw market depth and alternate lines, the direct approach outlined here is the path. DRT covers the full sportsbook scraping stack — from FanDuel and DraftKings through international books like Bet365 and Pinnacle — so check back as these APIs and detection layers evolve.

May 7, 2026

Author: Xavier Fok

How to scrape OLX Brazil and LATAM marketplaces

Mapping OLX URL and listing structure

Brazilian proxy strategy

Crawling categories and pagination

Phone number redaction and contact extraction

Schema for OLX classifieds snapshots

Detecting and routing around CAPTCHA challenges on OLX

Working with BRL pricing and FX normalization

Comparing OLX to other regional marketplaces

Operational monitoring and alerting

Legal and compliance considerations for Brazil

Pipeline orchestration and scheduling

Sample analytics queries on the collected dataset

Building robust deduplication across noisy listings

Versioning your scraper for catalogue evolution

Building robust deduplication across noisy listings

Caching strategy and incremental crawls

FAQ

How to scrape Auchan Drive France grocery data

The store-selection prerequisite

The Auchan product API

Crawling the category tree

Multi-store price comparison

Proxy strategy for Auchan

Rate limits and request shaping

Detecting and routing around CAPTCHA challenges on Auchan

Working with EUR pricing and FX normalization

Comparing Auchan to other regional marketplaces

Operational monitoring and alerting

Legal and compliance considerations for France

Pipeline orchestration and scheduling

Sample analytics queries on the collected dataset

Building robust deduplication across noisy listings

Versioning your scraper for catalogue evolution

Caching strategy and incremental crawls

Common pitfalls when scraping Auchan Drive

FAQ

How to scrape Rakuten Japan in 2026

The official Rakuten Ichiba Item API

When you still need HTML scraping

Genre-based catalogue discovery

Multi-tenant pricing and seller analytics

Detecting and routing around CAPTCHA challenges on Rakuten

Working with JPY pricing and FX normalization

Comparing Rakuten to other regional marketplaces

Operational monitoring and alerting

Legal and compliance considerations for Japan

Pipeline orchestration and scheduling

Sample analytics queries on the collected dataset

Building robust deduplication across noisy listings

Versioning your scraper for catalogue evolution

Caching strategy and incremental crawls

Common pitfalls when scraping Rakuten Ichiba

FAQ

How to scrape Jumia Africa product listings

Mapping the Jumia URL and listing structure

Country-specific proxy strategy for Jumia

Crawling category trees and pagination

Tracking Jumia Mall vs. third-party merchant signal

Rate limits, retries, and session management

Working with multi-currency Jumia datasets

Detecting and routing around CAPTCHA challenges on Jumia

Working with NGN pricing and FX normalization

Comparing Jumia to other regional marketplaces

Operational monitoring and alerting

Legal and compliance considerations for Nigeria

Pipeline orchestration and scheduling

Sample analytics queries on the collected dataset

Versioning your scraper for catalogue evolution

Common pitfalls when scraping Jumia

FAQ

How to scrape Noon UAE ecommerce in 2026

How Noon’s URL and language structure works

Why UAE residential proxies matter for Noon

Pulling category and search data

Handling Arabic text and RTL parsing

Comparing Noon’s three country marketplaces

Rate limits and retry patterns

Storing Noon snapshots