# Walmart Reviews Scraper - All Reviews by URL, ID or Keyword (`khadinakbar/walmart-reviews-scraper`) Actor Scrape Walmart.com customer reviews for any product by URL, item ID, or keyword search. Deep-paginates every review - rating, title, text, author, verified-purchase, helpful votes, photos, pros/cons, and date - plus a per-product rating summary. MCP-ready. $0.003 per review. - **URL**: https://apify.com/khadinakbar/walmart-reviews-scraper.md - **Developed by:** [Khadin Akbar](https://apify.com/khadinakbar) (community) - **Categories:** E-commerce, MCP servers, Automation - **Stats:** 1 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 review extracteds This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Walmart Reviews Scraper Scrape **Walmart.com customer reviews** for any product — by product URL, item ID, or keyword search. The actor deep-paginates **every review** per product (not just the first page) and returns one clean JSON record per review, plus an optional per-product rating summary. Built on the same battle-tested anti-bot stack as our `walmart-data-extractor` (real Chromium + Apify Residential US + session rotation), with a managed **SerpApi fallback** so a transient Walmart block returns real reviews instead of an empty run. MCP-ready for AI agents. ### What you get - **Every review, deep-paginated** — up to your `maxReviewsPerProduct` cap, not the ~10 a single page shows. - **Rich per-review fields** — rating, title, body text, author, verified-purchaser flag, helpful / unhelpful votes, customer photos, structured pros/cons, syndication flag, and submission date. - **Per-product summary row** — overall rating, total review count, and a 1–5 star histogram (toggle with `includeProductSummary`). - **Sort & filter** — newest, most helpful, highest/lowest rating, or most relevant; plus an exact client-side star-rating filter (e.g. mine only 1-star complaints). ### When to use this actor - Brand & CX teams tracking **Walmart review sentiment** over time. - Walmart Marketplace sellers monitoring **competitor SKU complaints**. - Researchers and **AI shopping agents** pulling a structured review corpus for a product. **When NOT to use it:** if you want product attributes (price, stock, seller, variants, specs) rather than reviews, use the **`walmart-data-extractor`** actor instead. This actor is reviews-first. ### Output Each review is one dataset record (`_type: "review"`): | Field | Type | Description | |-------|------|-------------| | `reviewId` | string | Stable Walmart review ID | | `itemId` | string | Walmart numeric item ID the review belongs to | | `productName` | string | Product the review is for | | `productUrl` | string | Canonical product page URL | | `rating` | number | Star rating this reviewer gave (1–5) | | `title` | string | Review headline | | `text` | string | Full review body | | `author` | string | Reviewer display name | | `verifiedPurchaser` | boolean | Verified-purchaser flag | | `helpfulVotes` | number | "Helpful" upvotes | | `unhelpfulVotes` | number | "Not helpful" downvotes | | `photos` | string[] | Customer-uploaded photo URLs | | `pros` / `cons` | string[] | Structured pros/cons when present | | `syndicated` | boolean | Syndicated from a partner site | | `submittedAt` | string | Submission time | | `reviewSource` | string | `scraped` or `serpapi` | | `scrapedAt` | string | ISO 8601 timestamp | When `includeProductSummary` is on, one extra row per product (`_type: "product"`) carries `overallRating`, `totalReviewCount`, `ratingBreakdown` ({ "1"..."5": n }), and `brand`. #### Example review record ```json { "_type": "review", "reviewId": "d1f0e2a3-1234-5678-9abc-def012345678", "itemId": "1820546583", "productName": "Apple AirPods Pro (2nd Generation)", "productUrl": "https://www.walmart.com/ip/1820546583", "rating": 5, "title": "Best earbuds I've owned", "text": "Noise cancellation is incredible and they pair instantly...", "author": "MusicFan22", "verifiedPurchaser": true, "helpfulVotes": 14, "unhelpfulVotes": 1, "photos": ["https://i5.walmartimages.com/asr/review-photo.jpeg"], "pros": ["Sound quality", "Battery life"], "cons": [], "syndicated": false, "submittedAt": "2026-03-14T09:22:00.000Z", "reviewSource": "scraped", "scrapedAt": "2026-06-18T07:30:00.000Z" } ```` ### Pricing (pay per event) | Event | Price | |-------|-------| | Actor start | $0.00005 | | **Review extracted** | **$0.003 / review** | | Product summary row | $0.001 / product | Both pay-per-event and usage-based billing are available — pick whichever fits your job at run time. The actor logs the worst-case cost cap at the start of every run, before any charge fires. ### Input Three auto-routed modes: **1. Keyword search → top products' reviews** ```json { "mode": "search", "searchQuery": "airpods pro", "maxProducts": 5, "maxReviewsPerProduct": 100, "sortReviews": "mostRecent" } ``` **2. Direct product URLs** ```json { "mode": "productUrls", "productUrls": ["https://www.walmart.com/ip/Apple-AirPods-Pro-2/1820546583"], "maxReviewsPerProduct": 200 } ``` **3. Walmart item IDs** ```json { "mode": "itemIds", "itemIds": ["1820546583"], "ratingFilter": "1", "maxReviewsPerProduct": 50 } ``` Key options: `maxReviewsPerProduct` (1–1000), `sortReviews` (`mostRecent` | `mostHelpful` | `highestRating` | `lowestRating` | `mostRelevant`), `ratingFilter` (`all` | `5`…`1`), `includeProductSummary` (default true). Item IDs are the numbers at the end of a Walmart product URL. ### How it works Walmart runs Akamai + PerimeterX (a 9/10-difficulty anti-bot stack), so raw HTTP / Cheerio gets blocked at the TLS-fingerprint level. This actor uses **PlaywrightCrawler + real Chromium** through **Apify Residential proxies pinned to US**, with a fingerprint pool, a cookie-persistent session pool, and `SessionError`-driven session rotation: a transient block retires the flagged session and retries on a fresh US residential IP with exponential backoff (~1.5s → 30s). Reviews are parsed from Walmart's server-rendered `__NEXT_DATA__` JSON — no fragile DOM scraping. The reviews surface is deep-paginated until your cap or the product runs out of reviews; a global dedup guard stops cleanly when a page returns nothing new. **SerpApi fallback:** if every rotated session is blocked and the live scrape yields zero reviews, the targeted item IDs are routed to SerpApi's managed `walmart_product_reviews` engine so the run still returns real data. Recovered reviews go through the same cap/charge/dedup path and the run exits `SUCCEEDED`. ### Using it from an AI agent (MCP) The actor is exposed as `apify--walmart-reviews-scraper` in the Apify MCP server. Give the agent a product URL or item ID and ask for reviews — it returns structured JSON it can summarize, classify, or aggregate. Narrow `maxReviewsPerProduct` and `ratingFilter` to keep token use and cost predictable. ### FAQ **Does it need login or cookies?** No. Reviews are public; no Walmart account is required. **How many reviews can I get per product?** Up to 1000 via `maxReviewsPerProduct`. Most products expose far fewer than that. **Why did a run return a single diagnostic row?** Walmart blocked every rotated session and the SerpApi fallback was unavailable for that input — usually a transient block. Retry, or lower `maxProducts`. **Can I get product price/specs too?** Use the companion `walmart-data-extractor` actor for full product fields. ### Legal This actor collects only **publicly available** review data from Walmart.com. Use it in compliance with Walmart's Terms of Service and applicable laws (including data-protection regulations such as GDPR/CCPA where relevant). You are responsible for how you use the scraped data. Do not use it to republish copyrighted review text verbatim at scale, harass reviewers, or process personal data unlawfully. This actor is not affiliated with or endorsed by Walmart Inc. # Actor input Schema ## `mode` (type: `string`): How to choose which products to pull reviews for. 'search' runs a keyword search and harvests reviews for the top products (bounded by Max products). 'productUrls' pulls reviews for the exact Walmart product URLs you supply. 'itemIds' pulls reviews for raw numeric Walmart item IDs. Defaults to 'search'. ## `searchQuery` (type: `string`): Free-text product query run on Walmart's search engine (e.g., 'airpods pro', 'instant pot duo 7-in-1'). Used only when mode = search; the top products (up to Max products) have their reviews scraped. Leave blank for productUrls / itemIds modes. NOT a product URL — for a specific product use productUrls. ## `productUrls` (type: `array`): Walmart product detail URLs to pull reviews for (e.g., 'https://www.walmart.com/ip/Apple-AirPods-Pro-2/1820546583'). Used when mode = productUrls. Each URL's reviews are deep-paginated up to Max reviews per product. The product page is only opened to resolve the item ID — the reviews come from Walmart's reviews surface. ## `itemIds` (type: `array`): Raw numeric Walmart item IDs to pull reviews for (e.g., '1820546583'). Used when mode = itemIds. Reviews are fetched directly from 'walmart.com/reviews/product/{itemId}'. Find the item ID at the end of a product URL. For full product fields (price, specs, variants) use the separate walmart-data-extractor actor. ## `maxProducts` (type: `integer`): Upper bound on how many products from the keyword search have their reviews scraped. Applies only to mode = search; productUrls / itemIds modes scrape every product you supply. The prefill is intentionally small so Apify health checks finish fast — raise it for production. Hard cap 200. ## `maxReviewsPerProduct` (type: `integer`): Upper bound on reviews scraped per product (each review is $0.003). Reviews deep-paginate newest-first by default until this cap or the product runs out of reviews. Default 100, hard cap 1000. Set lower to sample recent sentiment cheaply; raise for a full review corpus. ## `sortReviews` (type: `string`): Order in which reviews are collected per product. 'mostRecent' (default) returns newest first — best for monitoring fresh sentiment. 'mostHelpful' returns the highest-voted reviews. 'highestRating' / 'lowestRating' surface the best/worst first. 'mostRelevant' uses Walmart's default ranking. ## `ratingFilter` (type: `string`): Keep only reviews with this exact star rating. 'all' (default) keeps every rating. '5'..'1' keep only that star bucket — useful for mining 1-star complaints or 5-star praise. Applied client-side so it is always exact regardless of Walmart's own filters. ## `includeProductSummary` (type: `boolean`): When true (default), emit one aggregate row per product (tagged \_type='product') with the overall rating, total review count, and 1-5 star histogram, alongside the individual review rows. Charged at $0.001 per product. Turn off to receive only individual review records. ## `proxyConfiguration` (type: `object`): Apify proxy configuration. Defaults to Apify Residential pinned to US — required because Walmart geo-locks content and runs Akamai + PerimeterX anti-bot. Override only if you have a specific reason (e.g., your own residential pool). Datacenter proxies are blocked by Walmart. ## Actor input object example ```json { "mode": "search", "searchQuery": "instant pot duo 7-in-1", "maxProducts": 1, "maxReviewsPerProduct": 10, "sortReviews": "mostRecent", "ratingFilter": "all", "includeProductSummary": true, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } } ``` # Actor output Schema ## `dataset` (type: `string`): All scraped customer reviews (plus per-product summary rows). Download as JSON, CSV, Excel, HTML, or RSS. ## `runSummary` (type: `string`): End-of-run JSON with review/summary counts, charges, errors, and warning samples. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "mode": "search", "searchQuery": "airpods pro", "maxProducts": 1, "maxReviewsPerProduct": 10, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } }; // Run the Actor and wait for it to finish const run = await client.actor("khadinakbar/walmart-reviews-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "mode": "search", "searchQuery": "airpods pro", "maxProducts": 1, "maxReviewsPerProduct": 10, "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountry": "US", }, } # Run the Actor and wait for it to finish run = client.actor("khadinakbar/walmart-reviews-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 '{ "mode": "search", "searchQuery": "airpods pro", "maxProducts": 1, "maxReviewsPerProduct": 10, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } }' | apify call khadinakbar/walmart-reviews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=khadinakbar/walmart-reviews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Walmart Reviews Scraper - All Reviews by URL, ID or Keyword", "description": "Scrape Walmart.com customer reviews for any product by URL, item ID, or keyword search. Deep-paginates every review - rating, title, text, author, verified-purchase, helpful votes, photos, pros/cons, and date - plus a per-product rating summary. MCP-ready. $0.003 per review.", "version": "0.1", "x-build-id": "svMZwUg7DDQ7NPOFY" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/khadinakbar~walmart-reviews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-khadinakbar-walmart-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/khadinakbar~walmart-reviews-scraper/runs": { "post": { "operationId": "runs-sync-khadinakbar-walmart-reviews-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/khadinakbar~walmart-reviews-scraper/run-sync": { "post": { "operationId": "run-sync-khadinakbar-walmart-reviews-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": { "mode": { "title": "Mode", "enum": [ "search", "productUrls", "itemIds" ], "type": "string", "description": "How to choose which products to pull reviews for. 'search' runs a keyword search and harvests reviews for the top products (bounded by Max products). 'productUrls' pulls reviews for the exact Walmart product URLs you supply. 'itemIds' pulls reviews for raw numeric Walmart item IDs. Defaults to 'search'.", "default": "search" }, "searchQuery": { "title": "Search query", "type": "string", "description": "Free-text product query run on Walmart's search engine (e.g., 'airpods pro', 'instant pot duo 7-in-1'). Used only when mode = search; the top products (up to Max products) have their reviews scraped. Leave blank for productUrls / itemIds modes. NOT a product URL — for a specific product use productUrls." }, "productUrls": { "title": "Product URLs", "type": "array", "description": "Walmart product detail URLs to pull reviews for (e.g., 'https://www.walmart.com/ip/Apple-AirPods-Pro-2/1820546583'). Used when mode = productUrls. Each URL's reviews are deep-paginated up to Max reviews per product. The product page is only opened to resolve the item ID — the reviews come from Walmart's reviews surface.", "items": { "type": "string" } }, "itemIds": { "title": "Walmart item IDs", "type": "array", "description": "Raw numeric Walmart item IDs to pull reviews for (e.g., '1820546583'). Used when mode = itemIds. Reviews are fetched directly from 'walmart.com/reviews/product/{itemId}'. Find the item ID at the end of a product URL. For full product fields (price, specs, variants) use the separate walmart-data-extractor actor.", "items": { "type": "string" } }, "maxProducts": { "title": "Max products (search mode)", "minimum": 1, "maximum": 200, "type": "integer", "description": "Upper bound on how many products from the keyword search have their reviews scraped. Applies only to mode = search; productUrls / itemIds modes scrape every product you supply. The prefill is intentionally small so Apify health checks finish fast — raise it for production. Hard cap 200.", "default": 10 }, "maxReviewsPerProduct": { "title": "Max reviews per product", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Upper bound on reviews scraped per product (each review is $0.003). Reviews deep-paginate newest-first by default until this cap or the product runs out of reviews. Default 100, hard cap 1000. Set lower to sample recent sentiment cheaply; raise for a full review corpus.", "default": 100 }, "sortReviews": { "title": "Sort reviews", "enum": [ "mostRecent", "mostHelpful", "highestRating", "lowestRating", "mostRelevant" ], "type": "string", "description": "Order in which reviews are collected per product. 'mostRecent' (default) returns newest first — best for monitoring fresh sentiment. 'mostHelpful' returns the highest-voted reviews. 'highestRating' / 'lowestRating' surface the best/worst first. 'mostRelevant' uses Walmart's default ranking.", "default": "mostRecent" }, "ratingFilter": { "title": "Star rating filter", "enum": [ "all", "5", "4", "3", "2", "1" ], "type": "string", "description": "Keep only reviews with this exact star rating. 'all' (default) keeps every rating. '5'..'1' keep only that star bucket — useful for mining 1-star complaints or 5-star praise. Applied client-side so it is always exact regardless of Walmart's own filters.", "default": "all" }, "includeProductSummary": { "title": "Include product summary row", "type": "boolean", "description": "When true (default), emit one aggregate row per product (tagged _type='product') with the overall rating, total review count, and 1-5 star histogram, alongside the individual review rows. Charged at $0.001 per product. Turn off to receive only individual review records.", "default": true }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Apify proxy configuration. Defaults to Apify Residential pinned to US — required because Walmart geo-locks content and runs Akamai + PerimeterX anti-bot. Override only if you have a specific reason (e.g., your own residential pool). Datacenter proxies are blocked by Walmart.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } } } }, "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 } } } } } } } } } } ```