TikTok Video Data Scraper(by URL)

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TikTok Video Data Scraper(by URL)

vew5uoxb/tiktok-gen-ju-shi-pin-url-huo-qu-shu-ju

No coding required. Extract TikTok video data by URL in bulk: likes, comments, views, author followers. Auto-generate spreadsheets for content analysis and influencer research.

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What is TikTok Video Data Scraper？

A complete guide for automated data scraping script development, covering file structure, SDK core functions, code examples, and FAQs.

Core Function Usage Guide

1. Environment Parameter Retrieval

Retrieve external configuration parameters at script startup (e.g., target website URL, search keywords):

python

# Retrieve all input parameters as a dictionary
parameters = SDK.Parameter.get_input_json_dict()

# Example: Assuming website URL and keywords are provided
# Returns: {"website": "example.com", "keyword": "news"}

Use Case: When scraping data from different websites, pass different parameters without modifying the code.

2. Runtime Logging

Log messages at different levels during script execution, displayed in the mid-platform interface:

python

SDK.Log.debug("Connecting to target website...")           # Debug info
SDK.Log.info("Successfully retrieved 10 news items")       # Normal process log
SDK.Log.warn("Network is slow, may affect collection speed")  # Warning
SDK.Log.error("Cannot access target website, check network")  # Error

Log Level	Description
`debug`	Most detailed debugging info, suitable for development
`info`	Normal process log, recommended for key steps
`warn`	Warning message, indicates potential issues without stopping
`error`	Error message, indicates critical issues requiring attention

3. Result Return

After scraping data, return it to the mid-platform system in two steps:

Step 1: Set Table Headers (Must Execute First)

Define the table structure (similar to setting Excel column headers):

python

headers = [
    {
        "label": "News Title",     # Column name (user-visible)
        "key": "title",           # Column key (used in code)
        "format": "text",         # Data type
    },
    {
        "label": "Publish Time",
        "key": "publish_time",
        "format": "text",
    },
    {
        "label": "News Category",
        "key": "category",
        "format": "text",
    },
]

res = CoreSDK.Result.set_table_header(headers)

Field Description:

Field	Description
`label`	Column header displayed in the table (user-visible)
`key`	Unique data identifier (used in code, lowercase English + underscore recommended)
`format`	Data type:`text` / `integer` / `boolean` / `array` / `object`

Step 2: Push Data Row by Row

After setting headers, start pushing scraped data:

python

news_data = [
    {"title": "AI Breakthrough", "publish_time": "2023-10-01", "category": "Tech"},
    {"title": "Stock Market Trends", "publish_time": "2023-10-01", "category": "Finance"},
]

for i, news in enumerate(news_data):
    obj = {
        "title": news.get('title'),
        "publish_time": news.get('publish_time'),
        "category": news.get('category'),
    }
    res = CoreSDK.Result.push_data(obj)
    SDK.Log.info(f"Pushed data item {i+1}: {news.get('title')}")

Important Reminders:

The dictionary keys in pushed data must exactly match the header definitions
Data must be pushed row by row (bulk push not supported)
Recommend logging after each push to track execution progress

Complete Code Example

python

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import asyncio
import os

from sdk import CoreSDK

async def run():
    try:
        # 1. Get startup parameters
        config = SDK.Parameter.get_input_json_dict()
        website = config.get("website", "Default website")
        SDK.Log.info(f"Starting data collection from: {website}")

        # 2. Set result table headers
        headers = [
            {"label": "Title", "key": "title", "format": "text"},
            {"label": "Time", "key": "publish_time", "format": "text"},
            {"label": "Category", "key": "category", "format": "text"},
            {"label": "View Count", "key": "view_count", "format": "integer"},
        ]
        CoreSDK.Result.set_table_header(headers)

        # 3. Simulate data collection (replace with actual scraping code)
        collected_data = [
            {"title": "Sample News 1", "publish_time": "2023-10-01 10:00", "category": "Tech", "view_count": 1000},
            {"title": "Sample News 2", "publish_time": "2023-10-01 11:00", "category": "Finance", "view_count": 500},
        ]

        # 4. Push data
        for data in collected_data:
            obj = {
                "title": data.get("title"),
                "publish_time": data.get("publish_time"),
                "category": data.get("category"),
                "view_count": data.get("view_count", 0),
            }
            res = CoreSDK.Result.push_data(obj)

        # 5. Complete
        SDK.Log.info("Data collection task completed!")

    except Exception as e:
        SDK.Log.error(f"Script execution error: {e}")
        error_result = {
            "error": str(e),
            "error_code": "500",
            "status": "failed"
        }
        CoreSDK.Result.push_data(error_result)
        raise

if __name__ == "__main__":
    asyncio.run(run())

How to Use TikTok Video Data Scraper？

Stage	Description
1. Receive Instructions	Get input parameters (e.g., target URL, collection quantity)
2. Proxy Setup	Configure proxy server to access restricted websites
3. Auto Execution	Automatically scrape target page information based on parameters
4. Report Results	Convert unstructured data to standard format, generate table

FAQ

Why specify version numbers?

To ensure the same package versions are used across different environments (dev, test, prod), avoiding inconsistent behavior or compatibility issues caused by version differences.

What happens if no version is specified?

The system installs the latest version, which may be incompatible with the script. Recommend fixing versions for core dependencies.

How to add new dependencies?

Add a new line in requirements.txt in the format package==version or package, then re-upload the zip archive.

What if installation fails?

Check network connectivity or try switching Python package mirrors. Contact system admin if issues persist.

Are there file location requirements?

The three SDK files (sdk.py, sdk_pb2.py, sdk_pb2_grpc.py) must be placed in the script root directory (the folder containing main).

How to import SDK?

Use SDK or CoreSDK directly in code to call related functions.

Must pushed data keys match headers?

Yes. Keys used when pushing data must exactly match those defined in headers (case-sensitive).

Pricing

Failed results don't count

Rating

5.0

Developer

vew5uoxb

Worker Stats

38 Total runs

Success rate: 100.00%

Last updated: May 20, 2026

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