# Actor Performance Evaluator (`lukaskrivka/actor-performance-evaluator`) Actor Measure the median values of how long your Actor takes to reach certain points in its run. - **URL**: https://apify.com/lukaskrivka/actor-performance-evaluator.md - **Developed by:** [Lukรกลก Kล™ivka](https://apify.com/lukaskrivka) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 25.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 Actor Performance Evaluator is a simple tool to measure how long it takes to reach certain points in the Actor run. ### Usage 1. You need to add debug logs to your Actor in specific format: `PERF[${name}] ${number}` 2. It is up to you what the number should measure. The standard is to use `performance.now()` from `'perf_hooks'` module which gives you the time in milliseconds since the Node.js process started. This Actor automatically provides the difference from previous measured event. 3. Provide your Actor input, memory setting and how many iterations to run (recommend at least 100 for good averages). The runs should be short ones, this doesn't make much sense for Actors that run for minutes. 4. You get max, min, average and median times for each measured event and differences from previous event. ### Output example ```json [ { "eventName": "after-imports", "memoryMbs": "4096", "median": 598.87924, "mean": 633.0843160899999, "min": 481.925876, "max": 1038.968083 }, { "eventName": "after-input", "memoryMbs": "4096", "median": 784.8505485000001, "mean": 835.2474801699999, "min": 615.089415, "max": 1419.346805, "fromPreviousMean": 202.16316407999992, "fromPreviousMedian": 185.375318, "fromPreviousMin": 116.97888800000004, "fromPreviousMax": 481.7879959999999 }, { "eventName": "before-crawler-run", "memoryMbs": "4096", "median": 940.281442, "mean": 993.0418763499997, "min": 719.599819, "max": 1610.416095, "fromPreviousMean": 157.79439617999998, "fromPreviousMedian": 151.0749715, "fromPreviousMin": 104.510404, "fromPreviousMax": 369.22714999999994 } ] ```` # Actor input Schema ## `actorOrTaskId` (type: `string`): ID or name of the Actor or Task to run. Tasks enable you to choose your build etc. ## `actorInput` (type: `object`): JSON object representing the input to pass to the Actor when running it. ## `memoryConfigs` (type: `array`): List of memory configurations (in MB) to test the startup latency with. ## `iterationsPerConfig` (type: `integer`): Number of times to run the Actor for each memory configuration to get an average startup latency. ## Actor input object example ```json { "actorInput": {}, "memoryConfigs": [ "512" ], "iterationsPerConfig": 50 } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "actorOrTaskId": "", "actorInput": {} }; // Run the Actor and wait for it to finish const run = await client.actor("lukaskrivka/actor-performance-evaluator").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "actorOrTaskId": "", "actorInput": {}, } # Run the Actor and wait for it to finish run = client.actor("lukaskrivka/actor-performance-evaluator").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "actorOrTaskId": "", "actorInput": {} }' | apify call lukaskrivka/actor-performance-evaluator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=lukaskrivka/actor-performance-evaluator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Actor Performance Evaluator", "description": "Measure the median values of how long your Actor takes to reach certain points in its run.", "version": "0.0", "x-build-id": "prYdhSSRsPp0snbFV" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/lukaskrivka~actor-performance-evaluator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-lukaskrivka-actor-performance-evaluator", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/lukaskrivka~actor-performance-evaluator/runs": { "post": { "operationId": "runs-sync-lukaskrivka-actor-performance-evaluator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/lukaskrivka~actor-performance-evaluator/run-sync": { "post": { "operationId": "run-sync-lukaskrivka-actor-performance-evaluator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "actorOrTaskId": { "title": "Actor ID/Name or Task ID/name", "type": "string", "description": "ID or name of the Actor or Task to run. Tasks enable you to choose your build etc." }, "actorInput": { "title": "Actor Input (JSON)", "type": "object", "description": "JSON object representing the input to pass to the Actor when running it." }, "memoryConfigs": { "title": "Memory Configurations (MB)", "type": "array", "description": "List of memory configurations (in MB) to test the startup latency with.", "items": { "type": "string", "enum": [ "128", "256", "512", "1024", "2048", "4096", "8192", "16384", "32768" ] }, "default": [ "512" ] }, "iterationsPerConfig": { "title": "Iterations per Configuration (for statistical significance)", "type": "integer", "description": "Number of times to run the Actor for each memory configuration to get an average startup latency.", "default": 50 } } }, "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 } } } } } } } } } } ```