Blockchain Transaction Processor

📌 Overview

This project is designed to fetch, filter, and process transactions from the TRON blockchain using the TRON Full Node. It efficiently retrieves and processes transaction data, filters transactions related to blacklist events, and stores the results in a CSV file for further analysis.

🚀 Features

• 📡 Fetches transaction data from TRON blocks via API requests.
• 🔄 Converts TRON addresses from hex to base58 format.
• ✅ Filters transactions to include only successful ones.
• 🔍 Detects blacklist-related transactions (Added/RemovedBlackList events).
• 💾 Caches transaction data in memory to improve performance.
• 📊 Saves processed transactions into CSV files for further analysis.
• ⚡ Supports multi-threaded processing for efficiency.

🛠 Installation

Ensure you have Python 3 installed. Then install the required dependencies:

pip install requests pandas tqdm numpy base58 psutil

🎯 Usage

Running the Script

To run the script and start processing blocks:

python script.py

Configuration

Modify the following parameters as needed:

• batch_size: Number of blocks to process in each batch.
• block_last: Starting block number.
• max_workers: Number of concurrent threads for processing.

🔎 Filtering Transactions

The script filters transactions based on predefined event signatures:

• AddedBlackList: 42e160154868087d6bfdc0ca23d96a1c1cfa32f1b72ba9ba27b69b98a0d819dc
• RemovedBlackList: d7e9ec6e6ecd65492dce6bf513cd6867560d49544421d0783ddf06e76c24470c

💾 Saving Transactions

Processed transactions are saved in CSV format, with filenames based on block ranges (e.g., aggregated_transactions-<block_last>.csv).

🗑 Clearing Cache

To clear the in-memory cache, the script calls:

cache.clear_cache()

This ensures efficient memory usage during long runs.

⚡ Multi-threading Optimization

The script dynamically determines optimal thread usage based on CPU and memory availability:

num_cores = os.cpu_count()
available_memory = psutil.virtual_memory().available / (1024 * 1024)
max_workers = min(num_cores * 2, available_memory // 100)

📜 Example Workflow

cache = BlockTransactionCache()
block_last = 58000000
batch_size = 10000
while block_last < 58500000:
    process_batch(cache, block_last, batch_size)
    block_last += batch_size

This iterates through blockchain data in batches, processes transactions, and writes results to CSV.

📦 Dependencies

• 🐍 Python 3
• 📡 requests - for API requests
• 🗃 pandas - for data handling
• 🔢 numpy - for numerical operations
• 📊 tqdm - for progress tracking
• 🔗 base58 - for address conversion
• 🖥 psutil - for system resource management