Ethereum: Python ThreadPoolExecutor Closes Connection Before Task Completion

When using the ThreadPoolExecutor class in Python, it’s essential to manage connections properly to avoid issues with concurrent task execution. In this article, we’ll explore a common issue that may arise when retrieving cryptocurrency historical data from Binance API and store it in PostgreSQL database using ThreadPoolExecutor.

Background: Why Connection Closes

The ThreadPoolExecutor class creates separate threads for each task, which can lead to connections closing prematurely if not managed correctly. This is because:

• Each task (e.g., fetching historical price data) may open a connection to the Binance API.

• The thread responsible for closing the connection when done with its task may exit before all tasks are finished.

The Issue: Closing Connection Before Task Completion

In your script, you’re trying to retrieve cryptocurrency historical data and store it in PostgreSQL database using ThreadPoolExecutor as follows:

import config

from binance.client import Client

import psycopg2


def fetch_data():

    
Binance API request

    client = Client(config.BINANCE_API_KEY, config.BINANCE_API_SECRET)

    response = client.get_order_book()

    

    
Store data in PostgreSQL database

    conn = psycopg2.connect(

        host=config.DB_HOST,

        user=config.DB_USER,

        password=config.DB_PASSWORD,

        database=config.DB_NAME

    )

    cur = conn.cursor()

    for item in response['bids']:

        cur.execute("INSERT INTO historical_data (symbol, price) VALUES (%s, %s)", (item['id'], item['price']))

    conn.commit()

    conn.close()


def main():

    threads = []

    for _ in range(config.NUM_THREADS):

        thread = Thread(target=fetch_data)

        threads.append(thread)

        thread.start()


if __name__ == '__main__':

    main()

In this example, the fetch_data function creates a connection to PostgreSQL database and stores data in it. However, the ThreadPoolExecutor creates multiple threads for fetching historical price data from Binance API. As each thread closes its connection after completing its task, the next thread tries to execute another task without waiting for the previous one to finish.

Solution: Using concurrent.futures

To fix this issue, you can use the concurrent.futures module, which provides a high-level interface for asynchronously executing callables. Here’s an updated code snippet:

import config

from binance.client import Client

import psycopg2

from competitor.futures import ThreadPoolExecutor


def fetch_data():

    
Binance API request

    client = Client(config.BINANCE_API_KEY, config.BINANCE_API_SECRET)

    response = client.get_order_book()

    

    
Store data in PostgreSQL database

    conn = psycopg2.connect(

        host=config.DB_HOST,

        user=config.DB_USER,

        password=config.DB_PASSWORD,

        database=config.DB_NAME

    )

    cur = conn.cursor()

    for item in response['bids']:

        try:

            cur.execute("INSERT INTO historical_data (symbol, price) VALUES (%s, %s)", (item['id'], item['price']))

        except psycopg2.Error as e:

            print(f"Error occurred: {e}")

    conn.commit()

    conn.close()


def main():

    with ThreadPoolExecutor(max_workers=config.NUM_THREADS) as executor:

        executor.map(fetch_data, range(config.NUM Threads))


if __name__ == '__main__':

    main()

In this updated code snippet, we use ThreadPoolExecutor to manage the threads and execute tasks asynchronously. The fetch_data function is called with a range of indices from 0 to config.NUM Threads - 1. This ensures that each thread processes its tasks concurrently without waiting for other threads to finish.

Conclusion

By using `competitor.

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