# Twitter Trends Scraper (`scrapeengine/twitter-trends-scraper`) Actor Scrapes getdaytrends.com with live row-by-row dataset saving, retries, and Apify Proxy fallback to RESIDENTIAL when the selected proxy is rejected. - **URL**: https://apify.com/scrapeengine/twitter-trends-scraper.md - **Developed by:** [ScrapeEngine](https://apify.com/scrapeengine) (community) - **Categories:** Automation, Lead generation, Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $19.99/month + usage To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors ## 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 ### Twitter Trends Scraper Twitter Trends Scraper is a production-ready scraper that collects trending topics and tweet volume snapshots from getdaytrends.com and streams them directly into your Apify dataset. It automates live and historical trend captures with robust retries and smart proxy fallback, eliminating manual checks and flaky workflows. Built for marketers, developers, data analysts, and researchers, it enables reliable, scalable monitoring of worldwide or country-specific trends with clean, structured outputs. ### What data / output can you get? This actor pushes one dataset item per trend with a consistent schema optimized for analysis and time‑series tracking. | Data type | Description | Example value | | --- | --- | --- | | time | UTC timestamp when the trend snapshot was parsed | 2026-04-07T11:30:12.345678Z | | timePeriod | Label for the snapshot window (e.g., Live, hour-based, or historical) | Live; 1 hour ago; Yesterday; Week Ago; Month Ago; Year Ago; Day 3 | | trend | Trend text (topic or hashtag) as listed on getdaytrends.com | #Navratri2025; Russell Wilson; Chiefs | | volume | Tweet volume label as displayed on getdaytrends.com | 65.3K tweets; 10.0K tweets; Under 10K tweets | Bonus: The run also saves a summary to the key-value store as RUN_SUMMARY.json with: - runStartedAt - proxyUsed - stats.total_items - stats.per_timePeriod - stats.failed_urls You can view and export the dataset in multiple formats (CSV, JSON, Excel) from the Apify platform. ### Key features - ⚡ Live, row‑by‑row dataset saving Results are pushed to the Apify dataset as they’re parsed, providing immediate visibility and low memory usage. - 🧭 Flexible time snapshots Capture Live and historical windows using granular flags: hour1–hour23, Yesterday, Week Ago, Month Ago, Year Ago, and Day 3. - 🌍 Country-specific or worldwide coverage Choose a country (e.g., US, GB, IN) for localized trends or leave the country empty for worldwide coverage. - 🔁 Resilient HTTP with retries Built-in retry logic with exponential backoff handles transient failures gracefully. - 🛡️ Smart proxy handling with automatic fallback Uses your selected proxy settings and falls back to Apify RESIDENTIAL automatically if the chosen proxy is rejected. - 🧩 Robust parsing with multiple XPath strategies Multiple selectors improve stability across layout variations on getdaytrends.com. - 🧑‍💻 Developer-ready, Python-powered Runs on apify/actor-python:3.11 with clean, maintainable code for straightforward extension. - 💾 Run summary for auditing Saves RUN_SUMMARY.json with timing, proxy used, and per‑timePeriod stats for traceability. ### How to use Twitter Trends Scraper - step by step 1) Sign in to Apify Log in to your Apify account to run Twitter Trends Scraper from the Apify platform. 2) Open the actor and configure time flags Toggle the time windows you need: live, hour1–hour23, yesterday, weekAgo, monthAgo, yearAgo, or day3. 3) Choose the country (optional) Set the country field to a supported code (e.g., US, GB, IN) for localized results. Leave it empty for worldwide trends. 4) Configure proxy (optional) Use the Proxy configuration input. If your selected proxy is rejected, the actor will automatically fall back to Apify RESIDENTIAL. 5) Start the run Click Start. The actor will fetch your selected pages, retry on failures, and push each parsed trend row directly to the dataset. 6) Monitor progress Follow logs to see how many URLs were processed, item counts per timePeriod, and which proxy was used. 7) Download results Open the Dataset tab for your run to view, filter, and export items in CSV, JSON, or Excel formats. Pro tip: Combine multiple time flags (e.g., live + hour1 + yesterday) to build richer time series in a single run. Audit runs using RUN_SUMMARY.json in the key‑value store. ### Use cases | Use case name | Description | | --- | --- | | Marketing trend tracking | Identify trending topics to guide campaigns and content calendars with up-to-date snapshots. | | Competitor and influencer monitoring | Track spikes and shifts in attention by comparing hourly and daily snapshots. | | Editorial planning | Align coverage with emerging conversations using time-stamped trend captures. | | Data analysis & dashboards | Feed time, timePeriod, trend, and volume into BI tools for trendline visualizations. | | Academic & media research | Collect reproducible public snapshots for studies on attention dynamics and information diffusion. | | Automation pipelines | Schedule runs and integrate dataset outputs into downstream APIs or workflows for alerts and enrichment. | ### Why choose Twitter Trends Scraper? Purpose-built for reliability and clean outputs, this actor focuses on structured trend snapshots without overhead. - 🎯 Precision output: Minimal, consistent fields (time, timePeriod, trend, volume) ideal for analytics. - 🔁 Built-in resilience: Automatic retries with exponential backoff to reduce transient failures. - 🛡️ Proxy robustness: Falls back to Apify RESIDENTIAL automatically if your chosen proxy is rejected. - 🌍 Global or localized: Select a country or scrape worldwide for flexible coverage. - 🧑‍💻 Developer friendly: Python-based actor with row‑by‑row pushes for streaming‑friendly workflows. - 💾 Auditable runs: RUN_SUMMARY.json captures stats and proxy used for each execution. - 🧱 Stable parsing: Multiple XPath strategies minimize breakage from layout changes. In short: a production‑ready alternative to brittle extensions or ad‑hoc scripts. ### Is it legal / ethical to use Twitter Trends Scraper? Yes—when used responsibly. This actor scrapes publicly available pages on getdaytrends.com and does not require login or access to private resources. Guidelines for compliant use: - Only collect publicly available information. - Respect the target website’s terms of service and applicable laws (e.g., GDPR, CCPA). - Avoid scraping private, gated, or user‑specific data. - Verify compliance with your legal team for edge cases. ### Input parameters & output format #### Example input ```json { "live": true, "hour1": true, "yesterday": true, "weekAgo": false, "monthAgo": false, "yearAgo": false, "day3": false, "country": "US", "proxy": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountry": "US" } } ```` #### Input parameters | Field | Type | Description | Default | Required | | --- | --- | --- | --- | --- | | live | boolean | Scrape the current live trends. | not specified | No | | hour1 | boolean | Scrape trends from 1 hour ago. | not specified | No | | hour2 | boolean | Scrape trends from 2 hours ago. | not specified | No | | hour3 | boolean | Scrape trends from 3 hours ago. | not specified | No | | hour4 | boolean | Scrape trends from 4 hours ago. | not specified | No | | hour5 | boolean | Scrape trends from 5 hours ago. | not specified | No | | hour6 | boolean | Scrape trends from 6 hours ago. | not specified | No | | hour7 | boolean | Scrape trends from 7 hours ago. | not specified | No | | hour8 | boolean | Scrape trends from 8 hours ago. | not specified | No | | hour9 | boolean | Scrape trends from 9 hours ago. | not specified | No | | hour10 | boolean | Scrape trends from 10 hours ago. | not specified | No | | hour11 | boolean | Scrape trends from 11 hours ago. | not specified | No | | hour12 | boolean | Scrape trends from 12 hours ago. | not specified | No | | hour13 | boolean | Scrape trends from 13 hours ago. | not specified | No | | hour14 | boolean | Scrape trends from 14 hours ago. | not specified | No | | hour15 | boolean | Scrape trends from 15 hours ago. | not specified | No | | hour16 | boolean | Scrape trends from 16 hours ago. | not specified | No | | hour17 | boolean | Scrape trends from 17 hours ago. | not specified | No | | hour18 | boolean | Scrape trends from 18 hours ago. | not specified | No | | hour19 | boolean | Scrape trends from 19 hours ago. | not specified | No | | hour20 | boolean | Scrape trends from 20 hours ago. | not specified | No | | hour21 | boolean | Scrape trends from 21 hours ago. | not specified | No | | hour22 | boolean | Scrape trends from 22 hours ago. | not specified | No | | hour23 | boolean | Scrape trends from 23 hours ago. | not specified | No | | yesterday | boolean | Scrape yesterday's full-day summary. | not specified | No | | weekAgo | boolean | Scrape trends from 1 week ago. | not specified | No | | monthAgo | boolean | Scrape trends from 1 month ago. | not specified | No | | yearAgo | boolean | Scrape trends from 1 year ago. | not specified | No | | day2 | boolean | Scrape yesterday's full-day summary (legacy option, same as 'yesterday'). | not specified | No | | day3 | boolean | Scrape the summary from 2 days ago. | not specified | No | | country | string | Select a country to scrape country-specific Twitter trends. Leave empty for worldwide trends. | "" | No | | proxy | object | Defaults to no proxy. If the selected proxy is rejected, the actor falls back to Apify RESIDENTIAL automatically. | not specified | No | Notes: - The country field accepts a predefined list of two‑letter codes (see input editor), including "" (Worldwide), US, GB, IN, and many more. Leave it empty for worldwide trends. - The proxy object uses Apify’s standard proxy editor (supports Apify Proxy and custom URLs). #### Output format Each item pushed to the dataset has the following fields: ```json [ { "time": "2026-04-07T11:30:12.345678Z", "timePeriod": "Live", "trend": "#Navratri2025", "volume": "23.8K tweets" }, { "time": "2026-04-07T10:30:12.345678Z", "timePeriod": "1 hour ago", "trend": "Chiefs", "volume": "78.7K tweets" } ] ``` Field notes: - volume may be an empty string when the page does not display a volume label. ### FAQ #### Does this scrape Twitter directly? No. The actor scrapes public trend listings from getdaytrends.com and outputs structured records for each trend. #### What fields are returned in the dataset? Each dataset item includes time, timePeriod, trend, and volume. This minimal schema is designed for clean analytics and time‑series use. #### Can I capture historical snapshots? Yes. Enable hour1–hour23, yesterday, weekAgo, monthAgo, yearAgo, or day3 to capture historical snapshots alongside Live. #### Does it support country-specific trends? Yes. Use the country field for localized trends or leave it empty for worldwide coverage. The actor logs whether it’s scraping a specific country or worldwide. #### How does proxy fallback work? If your selected proxy (custom or Apify Proxy) is rejected, the actor automatically falls back to Apify RESIDENTIAL. The run logs the proxy in use and stores proxyUsed in RUN\_SUMMARY.json. #### Is login or cookies required? No. The scraper accesses public pages on getdaytrends.com and does not require authentication. #### How reliable is it if some pages fail? The actor performs HTTP retries with exponential backoff and logs failures. Items are pushed row‑by‑row, so partial progress is preserved even if some URLs fail. #### Where can I find run diagnostics? A RUN\_SUMMARY.json file is saved in the key‑value store with runStartedAt, proxyUsed, total item counts, per‑timePeriod stats, and any failed URLs. #### Is there a free trial? Yes. This actor includes trial minutes to test the workflow before subscribing. Check the Apify listing for current trial allocation and pricing details. ### Closing CTA / Final thoughts Twitter Trends Scraper is built to capture clean, time‑stamped trend snapshots from getdaytrends.com reliably and at scale. With flexible time windows, optional country selection, resilient retries, and automatic proxy fallback, it’s ideal for analysts, marketers, developers, and researchers who need dependable trend data. Run it on Apify, stream structured results to your dataset, and integrate the output into your analytics or automation pipelines. Start extracting smarter trend insights today. # Actor input Schema ## `live` (type: `boolean`): Scrape the current live trends. ## `hour1` (type: `boolean`): Scrape trends from 1 hour ago. ## `hour2` (type: `boolean`): Scrape trends from 2 hours ago. ## `hour3` (type: `boolean`): Scrape trends from 3 hours ago. ## `hour4` (type: `boolean`): Scrape trends from 4 hours ago. ## `hour5` (type: `boolean`): Scrape trends from 5 hours ago. ## `hour6` (type: `boolean`): Scrape trends from 6 hours ago. ## `hour7` (type: `boolean`): Scrape trends from 7 hours ago. ## `hour8` (type: `boolean`): Scrape trends from 8 hours ago. ## `hour9` (type: `boolean`): Scrape trends from 9 hours ago. ## `hour10` (type: `boolean`): Scrape trends from 10 hours ago. ## `hour11` (type: `boolean`): Scrape trends from 11 hours ago. ## `hour12` (type: `boolean`): Scrape trends from 12 hours ago. ## `hour13` (type: `boolean`): Scrape trends from 13 hours ago. ## `hour14` (type: `boolean`): Scrape trends from 14 hours ago. ## `hour15` (type: `boolean`): Scrape trends from 15 hours ago. ## `hour16` (type: `boolean`): Scrape trends from 16 hours ago. ## `hour17` (type: `boolean`): Scrape trends from 17 hours ago. ## `hour18` (type: `boolean`): Scrape trends from 18 hours ago. ## `hour19` (type: `boolean`): Scrape trends from 19 hours ago. ## `hour20` (type: `boolean`): Scrape trends from 20 hours ago. ## `hour21` (type: `boolean`): Scrape trends from 21 hours ago. ## `hour22` (type: `boolean`): Scrape trends from 22 hours ago. ## `hour23` (type: `boolean`): Scrape trends from 23 hours ago. ## `yesterday` (type: `boolean`): Scrape yesterday's full-day summary. ## `weekAgo` (type: `boolean`): Scrape trends from 1 week ago. ## `monthAgo` (type: `boolean`): Scrape trends from 1 month ago. ## `yearAgo` (type: `boolean`): Scrape trends from 1 year ago. ## `day2` (type: `boolean`): Scrape yesterday's full-day summary (legacy option, same as 'yesterday'). ## `day3` (type: `boolean`): Scrape the summary from 2 days ago. ## `country` (type: `string`): Select a country to scrape country-specific Twitter trends. Leave empty for worldwide trends. ## `proxy` (type: `object`): Defaults to no proxy. If the selected proxy is rejected, the actor falls back to Apify RESIDENTIAL automatically. ## Actor input object example ```json { "country": "" } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("scrapeengine/twitter-trends-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 = {} # Run the Actor and wait for it to finish run = client.actor("scrapeengine/twitter-trends-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 '{}' | apify call scrapeengine/twitter-trends-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapeengine/twitter-trends-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Twitter Trends Scraper", "description": "Scrapes getdaytrends.com with live row-by-row dataset saving, retries, and Apify Proxy fallback to RESIDENTIAL when the selected proxy is rejected.", "version": "0.2", "x-build-id": "6HBQInqd9QzLDfWmX" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapeengine~twitter-trends-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapeengine-twitter-trends-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapeengine~twitter-trends-scraper/runs": { "post": { "operationId": "runs-sync-scrapeengine-twitter-trends-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapeengine~twitter-trends-scraper/run-sync": { "post": { "operationId": "run-sync-scrapeengine-twitter-trends-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "live": { "title": "Live", "type": "boolean", "description": "Scrape the current live trends." }, "hour1": { "title": "1 hour ago", "type": "boolean", "description": "Scrape trends from 1 hour ago." }, "hour2": { "title": "2 hours ago", "type": "boolean", "description": "Scrape trends from 2 hours ago." }, "hour3": { "title": "3 hours ago", "type": "boolean", "description": "Scrape trends from 3 hours ago." }, "hour4": { "title": "4 hours ago", "type": "boolean", "description": "Scrape trends from 4 hours ago." }, "hour5": { "title": "5 hours ago", "type": "boolean", "description": "Scrape trends from 5 hours ago." }, "hour6": { "title": "6 hours ago", "type": "boolean", "description": "Scrape trends from 6 hours ago." }, "hour7": { "title": "7 hours ago", "type": "boolean", "description": "Scrape trends from 7 hours ago." }, "hour8": { "title": "8 hours ago", "type": "boolean", "description": "Scrape trends from 8 hours ago." }, "hour9": { "title": "9 hours ago", "type": "boolean", "description": "Scrape trends from 9 hours ago." }, "hour10": { "title": "10 hours ago", "type": "boolean", "description": "Scrape trends from 10 hours ago." }, "hour11": { "title": "11 hours ago", "type": "boolean", "description": "Scrape trends from 11 hours ago." }, "hour12": { "title": "12 hours ago", "type": "boolean", "description": "Scrape trends from 12 hours ago." }, "hour13": { "title": "13 hours ago", "type": "boolean", "description": "Scrape trends from 13 hours ago." }, "hour14": { "title": "14 hours ago", "type": "boolean", "description": "Scrape trends from 14 hours ago." }, "hour15": { "title": "15 hours ago", "type": "boolean", "description": "Scrape trends from 15 hours ago." }, "hour16": { "title": "16 hours ago", "type": "boolean", "description": "Scrape trends from 16 hours ago." }, "hour17": { "title": "17 hours ago", "type": "boolean", "description": "Scrape trends from 17 hours ago." }, "hour18": { "title": "18 hours ago", "type": "boolean", "description": "Scrape trends from 18 hours ago." }, "hour19": { "title": "19 hours ago", "type": "boolean", "description": "Scrape trends from 19 hours ago." }, "hour20": { "title": "20 hours ago", "type": "boolean", "description": "Scrape trends from 20 hours ago." }, "hour21": { "title": "21 hours ago", "type": "boolean", "description": "Scrape trends from 21 hours ago." }, "hour22": { "title": "22 hours ago", "type": "boolean", "description": "Scrape trends from 22 hours ago." }, "hour23": { "title": "23 hours ago", "type": "boolean", "description": "Scrape trends from 23 hours ago." }, "yesterday": { "title": "Yesterday", "type": "boolean", "description": "Scrape yesterday's full-day summary." }, "weekAgo": { "title": "Week Ago", "type": "boolean", "description": "Scrape trends from 1 week ago." }, "monthAgo": { "title": "Month Ago", "type": "boolean", "description": "Scrape trends from 1 month ago." }, "yearAgo": { "title": "Year Ago", "type": "boolean", "description": "Scrape trends from 1 year ago." }, "day2": { "title": "Day 2 (Yesterday) - Legacy", "type": "boolean", "description": "Scrape yesterday's full-day summary (legacy option, same as 'yesterday')." }, "day3": { "title": "Day 3", "type": "boolean", "description": "Scrape the summary from 2 days ago." }, "country": { "title": "Country", "enum": [ "", "DZ", "AR", "AU", "AT", "BH", "BY", "BE", "BR", "CA", "CL", "CO", "DK", "DO", "EC", "EG", "FR", "DE", "GH", "GR", "GT", "IN", "ID", "IE", "IL", "IT", "JP", "JO", "KE", "KR", "KW", "LV", "LB", "MY", "MX", "NL", "NZ", "NG", "NO", "OM", "PK", "PA", "PE", "PH", "PL", "PT", "PR", "QA", "RU", "SA", "SG", "ZA", "ES", "SE", "CH", "TH", "TR", "UA", "AE", "GB", "US", "VE", "VN" ], "type": "string", "description": "Select a country to scrape country-specific Twitter trends. Leave empty for worldwide trends.", "default": "" }, "proxy": { "title": "Proxy configuration", "type": "object", "description": "Defaults to no proxy. If the selected proxy is rejected, the actor falls back to Apify RESIDENTIAL automatically." } } }, "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 } } } } } } } } } } ```