# Lidl Product Scraper (`incognito_mode/lidl-product-scraper`) Actor Scrape every Lidl (lidl.nl) product from the official API: name, brand, price, Lidl Plus price, discount, size, category and image. Clean JSON/CSV. National pricing, no login. Needs a Dutch (NL) proxy. Failed lookups are never billed. - **URL**: https://apify.com/incognito\_mode/lidl-product-scraper.md - **Developed by:** [Elena Vance](https://apify.com/incognito_mode) (community) - **Categories:** E-commerce, Developer tools, Integrations - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.00 / 1,000 product results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Lidl Product Scraper — Prices, Lidl Plus Member Prices, Discounts & Images from Lidl.nl **Turn the entire Lidl Netherlands (lidl.nl) assortment into clean, structured JSON or CSV: product name, brand, regular price, **Lidl Plus member price**, offer/discount, pack size, category, image, and the product link — one tidy record per product.** Browse the whole catalog by category, or search by keyword, and get back data ready to drop into a spreadsheet, database, price-comparison site, or app. No login, no account — and **you are never billed for failed requests**. > **Good to know:** the default **Automatic (datacenter) proxy** works fine and > keeps costs low — no Dutch proxy required. (If you ever see blocked requests, > switch to a Dutch residential proxy.) Lidl uses a single **national** price > list, so one run captures the price every shopper in the Netherlands sees. --- ### Why this Actor - **Lidl Plus member prices, captured.** When a product has a Lidl Plus loyalty price, the Actor surfaces it as the current `price`, flags the record with `isLidlPlusPrice: true`, and keeps the regular price in `priceBeforeOffer` — so you see both the member price *and* what non-members pay, in one record. - **National pricing in one run.** Lidl NL publishes one price list for the whole country — no per-store fan-out, no store IDs. One run = the price everyone sees. - **Offers and discounts, detected for you.** Each record carries the current `price`, the pre-discount `priceBeforeOffer`, an `isOffer` flag, and a `discountLabel` (e.g. `-25%`) — filter the whole assortment down to this week's deals in one expression. - **The full assortment, or a focused slice.** Leave everything default to scrape every leaf category, list specific category IDs, or pass a search keyword. - **Complete product detail.** Brand, description as clean text, pack size, category, the product image, and the canonical lidl.nl product URL — one normalized record. - **You never pay for failures.** Timeouts, blocks, and removed products are reported in the run summary — **not** written to your dataset and **not billed**. - **Fast and lightweight.** Runs are quick and compute stays minimal. - **Clean, consistent EUR output.** Prices as euro floats, whitespace-normalized text, JSON-safe values throughout. --- ### Problems this Actor solves | If you are… | Your problem | How this Actor solves it | |---|---|---| | **A price-comparison / deals site** | Keeping a grocery price database current by hand is impossible | Schedule a daily run; ingest every product with regular price, Lidl Plus price, and offer as JSON | | **A market researcher / analyst** | Grocery-price and inflation tracking needs structured, repeatable data | Dated, normalized records per product — export straight to pandas, Sheets, or BI | | **A category manager at another retailer** | Benchmarking a competitor's assortment and pricing is slow and partial | Full category-level coverage, including loyalty (Lidl Plus) pricing, in a single run | | **An app / chatbot / agent developer** | You need Lidl catalog data without building and babysitting a scraper | Pay per record on demand; a stable, normalized schema you can rely on | | **A deal hunter / content creator** | Finding the best weekly discounts means clicking through the site | Pull the assortment and filter on `isOffer` / `discountLabel` / `isLidlPlusPrice` | --- ### What data you get Each available product becomes one dataset record: | Field | Description | |---|---| | `id` / `erpNumber` | Lidl's stable article number (the record's unique id) | | `title` | Product name | | `brand` | Brand name, when Lidl flags it as shown (else `null`) | | `description` | Product description as clean text (short + supplemental, when present) | | `categoryId` / `categoryName` | Category id and title for the product | | `price` | Current price as a EUR float — the **Lidl Plus member price** when one exists, otherwise the regular price | | `currency` | Always `EUR` | | `priceBeforeOffer` | The pre-discount / regular price when the product is on offer or has a member price (else `null`) | | `isOffer` | `true` when the product is discounted | | `isLidlPlusPrice` | `true` when `price` is a Lidl Plus loyalty price (not the regular shelf price) | | `discountLabel` | e.g. `-25%` — from the site, or computed from old/new price | | `unitSize` | Pack / sales-unit text where Lidl provides it (e.g. `500 g`) | | `imageUrl` | Product image URL | | `productUrl` | Canonical lidl.nl product page | | `scrapedAt` | ISO 8601 timestamp of the run | | `rawData` | *Optional:* the full raw Lidl product object, when **Include raw product payload** is on | #### Example output ```jsonc { "id": "10024896", "erpNumber": "10024896", "title": "Milbona Griekse Yoghurt 500 g", "brand": "Milbona", "description": "Romige yoghurt naar Griekse stijl, 500 gram.", "categoryId": "h10071018", "categoryName": "Zuivel & eieren", "price": 0.89, // Lidl Plus member price (see isLidlPlusPrice) "currency": "EUR", "priceBeforeOffer": 1.19, // the regular (non-member) price "isOffer": true, "isLidlPlusPrice": true, "discountLabel": "-25%", "unitSize": "500 g", "imageUrl": "https://www.lidl.nl/.../milbona-griekse-yoghurt.jpg", "productUrl": "https://www.lidl.nl/p/milbona-griekse-yoghurt/p10024896", "scrapedAt": "2026-06-18T08:30:00+00:00" } ```` Inputs that could not be fetched are **not** written to the dataset (and never billed). They are listed in the run's `SUMMARY` record in the key-value store — `{ "failures": [ { "input": "…", "error": "…" } ] }` — and the run's status message tells you at a glance how many products succeeded. *** ### How to use it (60 seconds) 1. Click **Try for free / Start**. 2. Choose what to scrape: - **Everything (default):** leave the inputs empty to scrape the full Lidl assortment (all discovered leaf categories). - **Specific categories:** add one category id per line under **Category IDs**, e.g. `h10071012` (or just `10071012`). - **A keyword:** type a term like `kaas` under **Search keyword**. 3. Leave the **Automatic (datacenter) proxy** as-is — it works out of the box. (Only if you hit blocked requests, switch to a Dutch residential proxy.) 4. Click **Save & Start**. Download results as **JSON, CSV, Excel, or via API** from the **Dataset** tab; check **Key-value store → SUMMARY** for run totals and any failed requests. *** ### Input reference | Field | Type | Default | Description | |---|---|---|---| | **Category IDs** | list | *(empty)* | One Lidl category id per line (e.g. `h10071012`, or just `10071012`). Empty = scrape all leaf categories (full assortment). | | **Search keyword** | string | – | Scrape products matching a term instead of browsing categories. When set, the category list is ignored. | | **Include raw product payload** | boolean | `false` | Adds the full raw Lidl product object under `rawData`. Increases item size. | | **Proxy configuration** | object | Apify Automatic | Automatic (datacenter) proxies work fine and keep costs low. Switch to a Dutch (NL) residential proxy only if you see blocked requests. | | **Max concurrency** | integer | `4` | Parallel requests (1–20). Kept moderate to be respectful. | | **Delay between requests** | integer | `0` | Politeness delay in seconds before each request (0–10). ~0.3s is plenty; `0` is fine at moderate concurrency. | | **Max items** | integer | `0` | Stop after N product records (`0` = unlimited). | *** ### Good to know - **Category mode** covers the full assortment (or the category IDs you list); **search mode** returns products matching your keyword. Products are de-duplicated, and when a Lidl Plus member price is present it becomes the headline `price` (with the regular price preserved in `priceBeforeOffer`). - Some categories — notably Lidl's weekly-rotating **"Eten & Drinken"** food range — are small or temporarily empty; an empty category is normal, not an error. *** ### Pricing — what a run costs This Actor uses transparent **pay-per-event** pricing with a built-in **volume discount**: a small Actor-start fee, a fixed price per successful product record for the first tier of results in a run, and a cheaper rate for every result beyond the volume threshold. **No subscription, no minimums, and failures are never charged.** The exact per-result rate is shown on the Actor's **Pricing** tab. - **Failed requests are free.** Timeouts, blocks, and removed products are reported in the summary, never billed. - **Bigger runs cost less per item.** The discount tier resets per run, so one large run is cheaper than the same job split into many small ones. - **Try it free:** an Apify free account includes **$5 of monthly platform credit** — enough to try the Actor before paying anything. - **Stay in control:** set **Max items** and Apify's **maximum charge per run**; the Actor stops gracefully at your cap, keeping everything already scraped. *** ### Compared to the alternatives | | **This Actor** | Build your own scraper | Manual checking | |---|---|---|---| | Full assortment, normalized | Yes | You maintain it | Impractical | | **Lidl Plus member prices** | Yes | You build it yourself | One product at a time | | Offers & discount labels | Yes | You maintain it | One product at a time | | Works out of the box | Yes | You build and babysit it | – | | Proxy handled for you | Yes (datacenter works) | You source proxies | – | | Never billed for failures | Yes | – | – | | Export JSON / CSV / Excel / API | Yes, built-in | DIY | Copy-paste | | Setup time | ~60 seconds | Days; breaks on site changes | Hours per run | *** ### Integrate the data - **Exports:** JSON, CSV, Excel, XML from the Dataset tab — or fetch programmatically: ``` GET https://api.apify.com/v2/datasets/{datasetId}/items?format=json ``` - **Run on a schedule:** use Apify **Schedules** to refresh prices daily or weekly, and **webhooks** to push finished runs into your pipeline (Sheets, Slack, your backend). - **From code:** call the Actor with the [Apify API or SDKs](https://docs.apify.com/api) (Python / JavaScript) and read the dataset when the run finishes. - **Run summary:** every run writes a `SUMMARY` record (key-value store) with totals, successes, failures, and billing counts — ideal for monitoring automated pipelines. - **Need the category tree itself?** The companion **Lidl Category Scraper** emits one record per Lidl category (main categories and leaf subcategories) — handy for building your own category navigation or driving targeted runs of this Actor. *** ### FAQ **Do I need a Lidl account or login?** No. There is no login or account required. **What is the Lidl Plus price I see in the data?** Lidl Plus is Lidl's loyalty programme, and some products carry a members-only price. When one exists, the Actor reports it as the current `price` and sets `isLidlPlusPrice: true`, while keeping the regular shelf price in `priceBeforeOffer` — so you always have both. **Do I need a Dutch proxy?** No. The default Automatic (datacenter) proxy works fine — Lidl's API returns the national (NL) assortment and prices regardless of where the request comes from. Only if you ever see blocked requests, switch to a Dutch (NL) residential proxy. **Are prices per store?** No — Lidl Netherlands uses a single national price list, so one run reflects the price nationwide. **How do I get only discounted products?** Scrape, then filter on `isOffer == true` (or a non-null `discountLabel`). To isolate loyalty deals, filter on `isLidlPlusPrice == true`. **One of the food categories came back empty — is that a bug?** No. Lidl's "Eten & Drinken" food range rotates weekly and is often small or temporarily empty. Empty categories are expected and are not reported as failures. **How fresh is the data?** Each run fetches live data, so you get exactly what lidl.nl shows at that moment. Schedule the Actor to keep your dataset as fresh as you need. **What formats can I export?** JSON, CSV, Excel, XML — from the Console or via the Apify API. *** ### Related Actors **Pair this with the Lidl Category Scraper.** The companion **Lidl Category Scraper** emits one record per Lidl category — every main category and h-type leaf subcategory, with ids, names, and product counts. Run it **first** to map the current Lidl category tree, then feed the leaf category IDs back into this Actor's **Category IDs** input to scrape exactly the branches you want. Category first to navigate, product second to harvest. Part of a family of Dutch-supermarket product scrapers — same clean schema and pay-per-event billing across every chain, so you can mix and match for full market coverage: - **Albert Heijn Product Scraper** — Bonus discounts, national pricing. - **Plus Product Scraper** — per-store pricing. - **Dirk van den Broek Product Scraper** — store-specific pricing. - **DekaMarkt Product Scraper** — store-specific pricing. - **Hoogvliet Product Scraper** (pairs with the **Hoogvliet Category Scraper**). Same normalized output and the same "never billed for failures" guarantee across the whole family — combine them to benchmark prices across the Dutch market. *** ### Disclaimer This Actor is intended for personal and research use. You are responsible for ensuring your use complies with Lidl's terms and applicable law. Please scrape responsibly — keep concurrency moderate and delays reasonable. This project is not affiliated with, endorsed by, or sponsored by Lidl. # Actor input Schema ## `categoryIds` (type: `array`): One Lidl category id per line to scrape only those categories (e.g. h10071012 for Fruit & groenten, or just 10071012). Leave empty to scrape all leaf categories (the full assortment). ## `searchQuery` (type: `string`): Scrape products matching a search term (e.g. "kaas") instead of browsing categories. When set, the category list above is ignored. ## `includeRawData` (type: `boolean`): Adds the full raw Lidl product object to each record under 'rawData'. Increases item size; leave off unless you need fields beyond the normalized ones. ## `proxyConfiguration` (type: `object`): Proxies used for requests. Automatic (datacenter) proxies are fine and keep costs near zero. Switch to Dutch (NL) residential only if you see blocked requests. ## `maxConcurrency` (type: `integer`): Maximum number of parallel requests. Keep this moderate to stay respectful. ## `requestDelaySecs` (type: `integer`): Politeness delay applied before each request. ~0.3s is plenty; 0 is fine at moderate concurrency. ## `maxItems` (type: `integer`): Stop after producing this many product records. Set to 0 for no limit (full assortment). ## Actor input object example ```json { "categoryIds": [ "h10071012" ], "includeRawData": false, "proxyConfiguration": { "useApifyProxy": true }, "maxConcurrency": 4, "requestDelaySecs": 0, "maxItems": 0 } ``` # Actor output Schema ## `products` (type: `string`): All scraped product records (one item per product). ## `summary` (type: `string`): JSON summary of the run: totals, successes, failures, and billing counts. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "categoryIds": [], "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("incognito_mode/lidl-product-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "categoryIds": [], "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("incognito_mode/lidl-product-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "categoryIds": [], "proxyConfiguration": { "useApifyProxy": true } }' | apify call incognito_mode/lidl-product-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=incognito_mode/lidl-product-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Lidl Product Scraper", "description": "Scrape every Lidl (lidl.nl) product from the official API: name, brand, price, Lidl Plus price, discount, size, category and image. Clean JSON/CSV. National pricing, no login. Needs a Dutch (NL) proxy. Failed lookups are never billed.", "version": "0.1", "x-build-id": "r3EU7m1PXRrrZZanW" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/incognito_mode~lidl-product-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-incognito_mode-lidl-product-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/incognito_mode~lidl-product-scraper/runs": { "post": { "operationId": "runs-sync-incognito_mode-lidl-product-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/incognito_mode~lidl-product-scraper/run-sync": { "post": { "operationId": "run-sync-incognito_mode-lidl-product-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "categoryIds": { "title": "Category IDs (optional)", "type": "array", "description": "One Lidl category id per line to scrape only those categories (e.g. h10071012 for Fruit & groenten, or just 10071012). Leave empty to scrape all leaf categories (the full assortment).", "items": { "type": "string" } }, "searchQuery": { "title": "Search keyword (optional)", "type": "string", "description": "Scrape products matching a search term (e.g. \"kaas\") instead of browsing categories. When set, the category list above is ignored." }, "includeRawData": { "title": "Include raw product payload", "type": "boolean", "description": "Adds the full raw Lidl product object to each record under 'rawData'. Increases item size; leave off unless you need fields beyond the normalized ones.", "default": false }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Proxies used for requests. Automatic (datacenter) proxies are fine and keep costs near zero. Switch to Dutch (NL) residential only if you see blocked requests.", "default": { "useApifyProxy": true } }, "maxConcurrency": { "title": "Max concurrency", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum number of parallel requests. Keep this moderate to stay respectful.", "default": 4 }, "requestDelaySecs": { "title": "Delay between requests (seconds)", "minimum": 0, "maximum": 10, "type": "integer", "description": "Politeness delay applied before each request. ~0.3s is plenty; 0 is fine at moderate concurrency.", "default": 0 }, "maxItems": { "title": "Max items", "minimum": 0, "type": "integer", "description": "Stop after producing this many product records. Set to 0 for no limit (full assortment).", "default": 0 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```