# Glassdoor Salaries Spider (`getdataforme/glassdoor-salaries-spider`) Actor Scrape comprehensive salary data directly from Glassdoor. This tool extracts detailed compensation metrics, including mean base pay, salary percentiles (P10-P90), and job counts, for specific roles and countries.... - **URL**: https://apify.com/getdataforme/glassdoor-salaries-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README --- ## Glassdoor Salaries Spider The Glassdoor Salaries Spider is a powerful data extraction tool designed to scrape comprehensive salary data directly from Glassdoor. It allows users to gather detailed compensation information for specific job roles and geographical locations, providing invaluable insights for market research and business intelligence. ### ✨ Features * **Comprehensive Salary Data:** Extracts mean base pay, salary percentiles (P10, P25, P50, P75, P90), and job count for specified roles. * **Geographical Targeting:** Easily restrict data collection to specific countries, ensuring localized and relevant market data. * **High Reliability:** Built to handle the structure and variability of job listing pages, ensuring consistent and reliable data extraction. * **Structured Output:** Provides a clean, JSON-formatted output containing all key metrics, making it ready for immediate analysis. * **Scalable Performance:** Optimized for batch processing, allowing users to run multiple queries efficiently. ### ⚙️ Input Parameters The spider requires configuration parameters to define the scope of the salary search. | Parameter | Type | Required | Description | Example | | :--- | :--- | :--- | :--- | :--- | | `Jobname` | string | No | The specific job title you wish to research (e.g., "Data Scientist"). | `python developer` | | `Country` | string | No | The country for which salary data should be retrieved. | `United States` | ### 💻 Example Usage #### Input JSON To find salary data for "Product Manager" in "Canada": ```json { "Jobname": "Product Manager", "Country": "Canada" } ```` #### Output JSON The resulting data is an array of objects, each representing a salary entry for a specific employer: ```json [ { "job_title": "Python Developer", "employer_name": "Google Inc.", "mean_base_pay": 149065.99, "currency": "USD", "overall_rating": 4.4, "job_count": 5626, "pay_period": "ANNUAL", "percentiles": { "P10": 107879.95, "P25": 133644.92, "P50": 169669.22, "P75": 216848.58, "P90": 269337.94 }, "employer_logo_url": "https://media.glassdoor.com/sql/9079/google-squarelogo-1441130773284.png", "actor_id": "jWWzkSabNU2gLZL71", "run_id": "3KgOD1mvdpA3mlNAU" } ] ``` ### 🚀 Use Cases This actor is invaluable for professionals and businesses in several fields: - **Market Research:** Quickly benchmark salary expectations for specific roles across different industries and regions. - **Competitive Intelligence:** Analyze the compensation packages offered by direct competitors to adjust internal pay scales. - **Recruitment & HR:** Determine competitive salary ranges when drafting job descriptions or negotiating offers. - **Academic Research:** Gather large datasets for studies on labor economics, wage gaps, and industry trends. - **Business Strategy:** Inform pricing models or investment decisions by understanding the cost of talent in specific markets. ### 🛠️ Installation and Usage 1. **Search:** Search for "Glassdoor Salaries Spider" in the Apify Store. 2. **Configure:** Click "Run" and configure the input parameters (`Jobname` and `Country`) according to your research needs. 3. **Execute:** Click "Start" to begin the data extraction process. 4. **Monitor:** Monitor the progress and logs within the Apify platform. 5. **Export:** Once complete, export the results in your preferred format (JSON, CSV, or Excel) for analysis. ### 📊 Output Format Details The output is a JSON array where each object represents a salary entry for a single employer. Key fields include: - `job_title`: The job title searched for. - `employer_name`: The name of the company. - `mean_base_pay`: The average base salary reported. - `currency`: The currency used (e.g., USD, CAD). - `overall_rating`: The average rating of the employer. - `job_count`: The number of job listings found for that employer/role. - `pay_period`: The frequency of pay (e.g., ANNUAL). - `percentiles`: An object containing salary ranges (P10, P25, P50, P75, P90), providing a detailed view of salary distribution. ### ⚠️ Error Handling and Best Practices - **Rate Limiting:** To ensure stable operation and avoid IP bans, we recommend running the spider in batches. If you encounter rate limiting errors, wait 15 minutes before retrying. - **Scope:** For the most accurate results, use specific job titles and countries. Broad or vague inputs may yield incomplete data. - **Data Freshness:** Salary data is dynamic. While we strive for accuracy, the data reflects the state of Glassdoor at the time of scraping. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! *** # Actor input Schema ## `Jobname` (type: `string`): The jobname for the spider. ## `Country` (type: `string`): The country for the spider. ## Actor input object example ```json { "Jobname": "python developer", "Country": "country" } ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # 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("getdataforme/glassdoor-salaries-spider").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("getdataforme/glassdoor-salaries-spider").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 getdataforme/glassdoor-salaries-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/glassdoor-salaries-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Glassdoor Salaries Spider", "description": "Scrape comprehensive salary data directly from Glassdoor. This tool extracts detailed compensation metrics, including mean base pay, salary percentiles (P10-P90), and job counts, for specific roles and countries....", "version": "0.0", "x-build-id": "kBoyd5Xba59u27rde" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~glassdoor-salaries-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-glassdoor-salaries-spider", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~glassdoor-salaries-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-glassdoor-salaries-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~glassdoor-salaries-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-glassdoor-salaries-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "Jobname": { "title": "Jobname", "type": "string", "description": "The jobname for the spider.", "default": "python developer" }, "Country": { "title": "Country", "type": "string", "description": "The country for the spider.", "default": "country" } } }, "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 } } } } } } } } } } ```