# Global Stock Exchange Meta Scraper — 25 Exchanges, One Call (`nexgendata/stock-exchange-meta-scraper`) Actor One API call for global equities snapshots across 25 stock exchanges. Replaces stitching 25 individual exchange feeds. - **URL**: https://apify.com/nexgendata/stock-exchange-meta-scraper.md - **Developed by:** [Stephan Corbeil](https://apify.com/nexgendata) (community) - **Categories:** Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $200.00 / 1,000 stock or exchange snapshots 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 ## Global Stock Exchange Meta Scraper — 25 Exchanges, One Call **One API call for global equities snapshots — replaces stitching 25 individual exchange feeds.** Pulls aggregated snapshots across 25 of the world's stock exchanges in a single run. Wraps NexGenData's full suite of single-exchange screeners into one unified meta-actor with a normalized output schema — so you stop maintaining 25 separate Apify integrations and start consuming one. --- ### Why this actor exists If you cover global equities, you've felt the pain: every exchange has a different feed, a different ticker convention, a different update cadence, a different rate-limit policy. Most teams end up stitching together 10–25 different scrapers and writing brittle adapter code to glue the outputs into one dashboard. This actor does that stitching once, server-side, behind a single Apify run. You pick the exchanges you care about, the depth you want, and the output shape (one row per exchange, or one row per stock). You get back a clean, normalized dataset every time. ### Coverage — all 25 exchanges in one place | Region | Exchanges | | ----------------- | ------------------------------------------------------------------------------------------------------ | | **Asia-Pacific** | KOSPI (KR), TWSE (TW), NSE (IN), BSE (IN), TSE (JP), HKEX (HK), SGX (SG), HOSE (VN), ASX (AU) | | **Greater China** | STAR Market, ChiNext, Beijing SE, Eastmoney A-Shares, Chinese ADRs | | **Europe** | LSE (UK), Xetra / DAX (DE) | | **Americas** | TSX (CA), B3 (BR), BMV (MX) | | **MENA / Africa** | Tadawul (SA), BIST (TR), JSE (ZA) | | **SE Asia** | IDX (ID), SET (TH), Bursa Malaysia, PSE (PH) | Every exchange is backed by a dedicated NexGenData single-exchange screener (linked below) — you keep getting the freshest possible data per market. ### Use cases - **Hedge fund position-sizing** — pull a global snapshot every morning, feed it into your sizing model, know your cross-region exposure before US open. - **Cross-border ETF research** — top-N by market cap across 25 exchanges in one run becomes your candidate set for thematic ETF construction. - **Sector rotation across regions** — `per_stock` mode gives you a unified `sector` field across exchanges so you can rank semiconductors / energy / banks globally. - **Daily institutional briefings** — `per_exchange_summary` returns the top 5 gainers, top 5 losers, top 10 by market cap, advancers/decliners, and median move per exchange — drop it straight into a Slack digest or a morning newsletter. - **Cross-border M&A and venture monitoring** — see which markets are seeing breakouts in real time, not a week after the fact. ### Two output modes #### `per_exchange_summary` (default — best for dashboards & briefings) One dataset row per selected exchange: ```json { "exchange": "TSE_JAPAN", "country": "Japan", "region": "Asia-Pacific", "currency": "JPY", "snapshot_ts": "2026-05-23T13:42:11Z", "stock_count": 50, "top_gainers": [{"symbol": "...", "name": "...", "price": 0, "change_pct": 0, "market_cap": 0}], "top_losers": [{"symbol": "...", "name": "...", "price": 0, "change_pct": 0, "market_cap": 0}], "top_by_market_cap": [...], "median_change_pct": 0.42, "advancers": 28, "decliners": 19, "unchanged": 3, "error": null } ```` #### `per_stock` (best for screeners & sector rotation) One row per stock across all selected exchanges, normalized into the same schema: ```json { "exchange": "HKEX", "country": "Hong Kong", "region": "Asia-Pacific", "currency": "HKD", "symbol": "0700.HK", "name": "Tencent Holdings", "price": 412.6, "change_pct": 1.8, "market_cap": 3960000000000, "volume": 8230000, "sector": "Communication Services", "raw": { /* original sub-actor row, untouched */ } } ``` The `raw` field always carries the exact row from the underlying single-exchange screener — useful if you need a field we haven't normalized yet. ### Input schema | Field | Type | Default | Notes | | ---------------------- | ---------- | -------------------------- | ------------------------------------------------------------------ | | `exchanges` | `string[]` | all 25 | Pick any subset. | | `outputMode` | `string` | `per_exchange_summary` | Or `per_stock`. | | `topN` | `integer` | 50 | Per exchange. Max 500. | | `maxParallel` | `integer` | 5 | Concurrent sub-actor runs. | | `subActorTimeoutSecs` | `integer` | 300 | Per-exchange timeout; failures don't poison the rest of the run. | ### How it works Approach A (call sub-actors): this meta-actor invokes each selected single-exchange screener via `Actor.call()`, fetches the resulting datasets, normalizes fields across heterogeneous sub-actor schemas into a unified shape, and pushes one item per exchange (summary mode) or one item per stock (per-stock mode) to its own dataset. Failures on individual exchanges are isolated — the row carries an `error` field and the rest of the run continues. ### Underlying single-exchange screeners (all by NexGenData) If you only care about one market, use the dedicated screener directly — same data, lower cost: - [KOSPI / KOSDAQ — Korea](https://apify.com/nexgendata/kospi-stock-screener) - [TWSE — Taiwan](https://apify.com/nexgendata/twse-stock-screener) - [NSE — India](https://apify.com/nexgendata/nse-india-stock-screener) - [BSE — India](https://apify.com/nexgendata/bse-india-stock-screener) - [B3 — Brazil](https://apify.com/nexgendata/b3-brasil-stock-screener) - [HKEX / Hang Seng — Hong Kong](https://apify.com/nexgendata/hkex-hang-seng-stock-screener) - [SGX — Singapore](https://apify.com/nexgendata/sgx-singapore-stock-screener) - [HOSE — Vietnam](https://apify.com/nexgendata/hose-vietnam-stock-screener) - [TSE — Japan](https://apify.com/nexgendata/tse-japan-stock-screener) - [LSE — UK](https://apify.com/nexgendata/lse-uk-stock-screener) - [Xetra / DAX — Germany](https://apify.com/nexgendata/xetra-germany-stock-screener) - [TSX — Canada](https://apify.com/nexgendata/tsx-canada-stock-screener) - [ASX — Australia](https://apify.com/nexgendata/asx-australia-stock-screener) - [Tadawul — Saudi Arabia](https://apify.com/nexgendata/tadawul-saudi-stock-screener) - [BMV — Mexico](https://apify.com/nexgendata/bmv-mexico-stock-screener) - [JSE — South Africa](https://apify.com/nexgendata/jse-southafrica-stock-screener) - [IDX — Indonesia](https://apify.com/nexgendata/idx-indonesia-stock-screener) - [SET — Thailand](https://apify.com/nexgendata/set-thailand-stock-screener) - [Bursa — Malaysia](https://apify.com/nexgendata/bursa-malaysia-stock-screener) - [BIST — Turkey](https://apify.com/nexgendata/bist-turkey-stock-screener) - [PSE — Philippines](https://apify.com/nexgendata/pse-philippines-stock-screener) - [STAR Market — China](https://apify.com/nexgendata/star-market-china-stock-screener) - [ChiNext — China](https://apify.com/nexgendata/chinext-china-stock-screener) - [Chinese ADRs](https://apify.com/nexgendata/chinese-adrs-stock-screener) - [Beijing Stock Exchange](https://apify.com/nexgendata/bse-beijing-stock-screener) - [Eastmoney A-Shares](https://apify.com/nexgendata/eastmoney-china-stock-screener) ### Also see - [Global Equities Screener MCP](https://apify.com/nexgendata/global-equities-screener-mcp) — Model Context Protocol server that exposes this data to AI agents (Claude, ChatGPT, Cursor) as tools. ### Pricing Pay-per-event billing. See the actor's pricing tab on Apify Store for the current rates. ### Support Bug reports, schema requests, or new-exchange suggestions: open an issue on the actor's Apify Store page. # Actor input Schema ## `exchanges` (type: `array`): List of exchange codes to include in this snapshot. Pick any subset of the 25 supported exchanges (KOSPI, TWSE, NSE\_INDIA, BSE\_INDIA, B3\_BRAZIL, HKEX, SGX, HOSE\_VIETNAM, TSE\_JAPAN, LSE\_UK, XETRA\_GERMANY, TSX\_CANADA, ASX\_AUSTRALIA, TADAWUL\_SAUDI, BMV\_MEXICO, JSE\_SOUTH\_AFRICA, IDX\_INDONESIA, SET\_THAILAND, BURSA\_MALAYSIA, BIST\_TURKEY, PSE\_PHILIPPINES, STAR\_CHINA, CHINEXT\_CHINA, CHINESE\_ADRS, BSE\_BEIJING, EASTMONEY\_A\_SHARES). Leave the default (all 25) to get a true global snapshot in one run. ## `outputMode` (type: `string`): Shape of the dataset rows. `per_exchange_summary` returns ONE aggregated row per exchange (top 5 gainers, top 5 losers, top 10 by market cap, advancers/decliners, median change) — ideal for daily institutional briefings and hedge-fund snapshots. `per_stock` returns a flat list of normalized stock rows across all selected exchanges (much larger output) — ideal for sector rotation, cross-border ETF construction, and screener workflows. ## `topN` (type: `integer`): How many stocks to fetch per exchange. In `per_exchange_summary` mode this controls the underlying universe used to compute aggregates (default 50 is sufficient for top-movers / index leaders). In `per_stock` mode it directly controls dataset size — 100 stocks × 25 exchanges = 2,500 rows. Maximum 500. ## `maxParallel` (type: `integer`): How many sub-actors to run concurrently. Higher = faster wall-clock but more peak memory + risk of provider rate-limits. 5 is the sweet spot for a global snapshot across 25 exchanges (~5 batches of 5). ## `subActorTimeoutSecs` (type: `integer`): Maximum seconds to wait for any one sub-actor run to complete. If a single exchange screener stalls (regional outage, captcha), it will be cut off and the error recorded in that exchange's row — other exchanges continue. Default 300s (5 min) is plenty for any single screener at topN=500. ## Actor input object example ```json { "exchanges": [ "TSE_JAPAN", "HKEX", "LSE_UK" ], "outputMode": "per_exchange_summary", "topN": 50, "maxParallel": 5, "subActorTimeoutSecs": 300 } ``` # 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 = { "exchanges": [ "TSE_JAPAN", "HKEX", "LSE_UK" ], "outputMode": "per_exchange_summary", "topN": 50, "maxParallel": 5, "subActorTimeoutSecs": 300 }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/stock-exchange-meta-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 = { "exchanges": [ "TSE_JAPAN", "HKEX", "LSE_UK", ], "outputMode": "per_exchange_summary", "topN": 50, "maxParallel": 5, "subActorTimeoutSecs": 300, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/stock-exchange-meta-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 '{ "exchanges": [ "TSE_JAPAN", "HKEX", "LSE_UK" ], "outputMode": "per_exchange_summary", "topN": 50, "maxParallel": 5, "subActorTimeoutSecs": 300 }' | apify call nexgendata/stock-exchange-meta-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/stock-exchange-meta-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Global Stock Exchange Meta Scraper — 25 Exchanges, One Call", "description": "One API call for global equities snapshots across 25 stock exchanges. Replaces stitching 25 individual exchange feeds.", "version": "0.0", "x-build-id": "EbS9MPOkpnjpU4Brf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~stock-exchange-meta-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-stock-exchange-meta-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~stock-exchange-meta-scraper/runs": { "post": { "operationId": "runs-sync-nexgendata-stock-exchange-meta-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~stock-exchange-meta-scraper/run-sync": { "post": { "operationId": "run-sync-nexgendata-stock-exchange-meta-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "exchanges": { "title": "Exchanges to query", "uniqueItems": true, "type": "array", "description": "List of exchange codes to include in this snapshot. Pick any subset of the 25 supported exchanges (KOSPI, TWSE, NSE_INDIA, BSE_INDIA, B3_BRAZIL, HKEX, SGX, HOSE_VIETNAM, TSE_JAPAN, LSE_UK, XETRA_GERMANY, TSX_CANADA, ASX_AUSTRALIA, TADAWUL_SAUDI, BMV_MEXICO, JSE_SOUTH_AFRICA, IDX_INDONESIA, SET_THAILAND, BURSA_MALAYSIA, BIST_TURKEY, PSE_PHILIPPINES, STAR_CHINA, CHINEXT_CHINA, CHINESE_ADRS, BSE_BEIJING, EASTMONEY_A_SHARES). Leave the default (all 25) to get a true global snapshot in one run.", "items": { "type": "string", "enum": [ "KOSPI", "TWSE", "NSE_INDIA", "BSE_INDIA", "B3_BRAZIL", "HKEX", "SGX", "HOSE_VIETNAM", "TSE_JAPAN", "LSE_UK", "XETRA_GERMANY", "TSX_CANADA", "ASX_AUSTRALIA", "TADAWUL_SAUDI", "BMV_MEXICO", "JSE_SOUTH_AFRICA", "IDX_INDONESIA", "SET_THAILAND", "BURSA_MALAYSIA", "BIST_TURKEY", "PSE_PHILIPPINES", "STAR_CHINA", "CHINEXT_CHINA", "CHINESE_ADRS", "BSE_BEIJING", "EASTMONEY_A_SHARES" ], "enumTitles": [ "South Korea — KOSPI / KOSDAQ", "Taiwan — TWSE", "India — NSE", "India — BSE", "Brazil — B3 (Bovespa)", "Hong Kong — HKEX (Hang Seng)", "Singapore — SGX", "Vietnam — HOSE", "Japan — TSE", "United Kingdom — LSE", "Germany — Xetra / DAX", "Canada — TSX", "Australia — ASX", "Saudi Arabia — Tadawul", "Mexico — BMV", "South Africa — JSE", "Indonesia — IDX", "Thailand — SET", "Malaysia — Bursa", "Turkey — BIST", "Philippines — PSE", "China — STAR Market", "China — ChiNext", "China — US-listed ADRs", "China — Beijing Stock Exchange", "China — Eastmoney A-Shares" ] }, "default": [ "KOSPI", "TWSE", "NSE_INDIA", "BSE_INDIA", "B3_BRAZIL", "HKEX", "SGX", "HOSE_VIETNAM", "TSE_JAPAN", "LSE_UK", "XETRA_GERMANY", "TSX_CANADA", "ASX_AUSTRALIA", "TADAWUL_SAUDI", "BMV_MEXICO", "JSE_SOUTH_AFRICA", "IDX_INDONESIA", "SET_THAILAND", "BURSA_MALAYSIA", "BIST_TURKEY", "PSE_PHILIPPINES", "STAR_CHINA", "CHINEXT_CHINA", "CHINESE_ADRS", "BSE_BEIJING", "EASTMONEY_A_SHARES" ] }, "outputMode": { "title": "Output mode", "enum": [ "per_exchange_summary", "per_stock" ], "type": "string", "description": "Shape of the dataset rows. `per_exchange_summary` returns ONE aggregated row per exchange (top 5 gainers, top 5 losers, top 10 by market cap, advancers/decliners, median change) — ideal for daily institutional briefings and hedge-fund snapshots. `per_stock` returns a flat list of normalized stock rows across all selected exchanges (much larger output) — ideal for sector rotation, cross-border ETF construction, and screener workflows.", "default": "per_exchange_summary" }, "topN": { "title": "Top N stocks per exchange", "minimum": 1, "maximum": 500, "type": "integer", "description": "How many stocks to fetch per exchange. In `per_exchange_summary` mode this controls the underlying universe used to compute aggregates (default 50 is sufficient for top-movers / index leaders). In `per_stock` mode it directly controls dataset size — 100 stocks × 25 exchanges = 2,500 rows. Maximum 500.", "default": 50 }, "maxParallel": { "title": "Max parallel sub-actor runs", "minimum": 1, "maximum": 10, "type": "integer", "description": "How many sub-actors to run concurrently. Higher = faster wall-clock but more peak memory + risk of provider rate-limits. 5 is the sweet spot for a global snapshot across 25 exchanges (~5 batches of 5).", "default": 5 }, "subActorTimeoutSecs": { "title": "Per sub-actor timeout (seconds)", "minimum": 60, "maximum": 1200, "type": "integer", "description": "Maximum seconds to wait for any one sub-actor run to complete. If a single exchange screener stalls (regional outage, captcha), it will be cut off and the error recorded in that exchange's row — other exchanges continue. Default 300s (5 min) is plenty for any single screener at topN=500.", "default": 300 } } }, "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 } } } } } } } } } } ```