We extract food databases, macro breakdowns, serving variations, and exercise metrics from MyFitnessPal. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Food Items objects from myfitnesspal.com. All fields typed and schema-versioned.
"food_id": "83729104", "name": "Grilled Chicken Breast", "brand": "Generic", "serving_size": "4 oz", "calories": 185, "carbs_g": 0, "fat_g": 4, "protein_g": 35, "verified": true
| # | food_id | name | brand | serving_size | calories | carbs_g |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Micronutrients objects from myfitnesspal.com. All fields typed and schema-versioned.
"food_id": "83729104", "sodium_mg": 85, "potassium_mg": 290, "cholesterol_mg": 95, "vitamin_a_pct": 0, "vitamin_c_pct": 0, "iron_pct": 6, "saturated_fat_g": 1.2
| # | food_id | sodium_mg | potassium_mg | cholesterol_mg | vitamin_a_pct | vitamin_c_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Serving Variations objects from myfitnesspal.com. All fields typed and schema-versioned.
"food_id": "83729104", "serving_description": "100 g", "serving_weight_grams": 100, "calories": 165, "multiplier": 0.88, "is_default": false, "unit_type": "metric", "measurement_system": "metric"
| # | food_id | serving_description | serving_weight_grams | calories | multiplier | is_default |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Exercise Database objects from myfitnesspal.com. All fields typed and schema-versioned.
"exercise_id": "9281", "name": "Running (8 mph)", "category": "Cardio", "mets_value": 11.5, "calories_per_min_kg": 0.2, "muscle_group": "Full Body", "equipment_required": "None"
| # | exercise_id | name | category | mets_value | calories_per_min_kg | equipment_required |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Brands & Restaurants objects from myfitnesspal.com. All fields typed and schema-versioned.
"brand_id": "B9102", "name": "Chipotle", "item_count": 482, "category": "Restaurant", "verified_status": true, "location_type": "National Chain", "popular_items": "['Chicken Burrito Bowl', 'Guacamole']", "url": "https://www.myfitnesspal.com/nutrition-facts-calories/chipotle"
| # | brand_id | name | item_count | category | verified_status | location_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our MyFitnessPal scraper handles every layer of the platform: food search results, macro breakdowns, micronutrient profiles, and serving size arrays — with JavaScript rendering and anti-bot circumvention built in.
Extract food names, brands, calories, and macronutrients (carbs, fat, protein) across millions of user-generated and verified entries.
Capture sodium, potassium, cholesterol, vitamins, calcium, iron, and specific fat breakdowns (saturated, trans) for detailed nutritional analysis.
Extract all available serving sizes (grams, ounces, cups, custom portions) and their respective calorie multipliers per food item.
Filter and extract only foods with the MyFitnessPal 'Verified' checkmark to ensure high-quality, accurate nutritional data.
Scrape complete menus from specific restaurant chains or FMCG brands, capturing their entire nutritional catalogue.
Extract calorie burn rates, MET values, and categorisation for thousands of cardio and strength training exercises.
Extract public recipe ingredients, preparation steps, and aggregated nutritional totals from the MyFitnessPal blog and community.
Run one-off bulk exports or configure continuous pipelines at weekly or monthly cadences with change-detection diffing.
Extract regional food databases and measurement units (metric vs imperial) based on geolocation proxies.
Brief in. Clean data out.
Provide search terms, brand names, or category URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for myfitnesspal.com.
Schema validation, null-rate checks, calorie-to-macro ratio outlier detection, and sample data before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
MyFitnessPal employs strict rate limits and pagination traps to protect its database. Here's how we stay resilient.
MyFitnessPal uses Cloudflare and custom rate-limiting to block volumetric scraping. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to bypass WAF protections.
MyFitnessPal caps search results at a maximum page depth. We circumvent this by dynamically generating hyper-specific search queries, iterating through alphabetised brand lists, and using category filters to extract the full corpus.
User-generated food entries often have inconsistent formatting, missing micronutrients, or malformed serving sizes. Our parsers normalise these inputs, apply data-type enforcement, and flag mathematically impossible macro-to-calorie ratios.
For massive food catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost, storage bloat, and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing macro fields, schema drift, and coverage drops — and respond before you notice.
New fitness and diet tracking applications populate their initial food databases to solve the cold-start problem and provide immediate value to users.
Restaurant chains and FMCG brands monitor competitor nutritional profiles, tracking menu changes and macro shifts over time.
Nutritionists and public health researchers analyse macro trends, serving size inflation, and dietary shifts across specific food categories.
Cooking websites use verified ingredient data to automatically calculate and display accurate nutritional information for user-submitted recipes.
Food manufacturers benchmark their products against category averages to optimise formulations for protein-to-calorie ratios.
Machine learning teams train natural language processing models to recognise food entities, estimate portion sizes, and predict macronutrients from text descriptions.
"MyFitnessPal holds the largest crowdsourced and verified food database globally — but extracting structured macros requires bypassing aggressive rate limits and pagination traps."
Most teams underestimate the investment required: reliable MyFitnessPal scraping requires residential proxies, handling complex search pagination, and parsing highly inconsistent user-generated food entries. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our myfitnesspal.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US/UK/EU regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About myfitnesspal.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from MyFitnessPal is generally permissible under applicable law. DataFlirt targets only public, non-authenticated food databases, macro information, and exercise metrics. We do not extract personal user diaries, private recipes, or circumvent authentication walls. Clients should review Terms of Service and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains so DOM changes don't break the pipeline. We monitor for WAF blocks in real time and trigger pool rotation automatically.
Yes. The MyFitnessPal database contains millions of user-generated entries with inaccurate macros. We can configure the pipeline to filter exclusively for items bearing the verified checkmark, ensuring high data fidelity.
We extract the full nutritional label available on the public page. This includes calories, carbs, fat, and protein, as well as sodium, potassium, cholesterol, vitamins, calcium, iron, and specific fat breakdowns (saturated vs trans).
No. DataFlirt strictly adheres to extracting publicly available data. We do not scrape authenticated user accounts, private food logs, weight history, or premium-gated content.
Full catalogue refreshes at weekly or monthly cadences complete within a defined window depending on size. For targeted brand or restaurant menus, we can run daily diff pipelines to capture menu changes quickly.
Our smallest packages start at a defined set of search terms or brand lists (typically yielding 50,000-100,000 food items) with monthly delivery. For full database extraction, we price based on volume and compute requirements. Contact us with your use case for a scoped quote.
Absolutely. We provide a sample run of up to 1,000 food items or 5 specific brand menus as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and macro accuracy before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off food database dump or a continuous tracking feed for restaurant menus — we scope, build, and operate the pipeline. Tell us what you need.
