Ethereum: python ThreadPoolExecutor terminates connection before completing task

Ethereum: Python ThreadPoolExecutor closes connection before task completion

When using the ThreadPoolExecutor class in Python, it is important to manage connections properly to avoid issues with concurrent execution of tasks. In this article, we will explore a common issue that can occur when retrieving historical cryptocurrency data from the Binance API and storing it in a PostgreSQL database using ThreadPoolExecutor.

Background: Why Connections Get Dropped

The ThreadPoolExecutor class creates separate threads for each task, which can lead to premature connection closure if not managed properly. This is because:

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

• The thread responsible for closing the connection when it is finished with its task may exit before all tasks have completed.

Problem: Closing connection before task completion

In your script, you are trying to fetch historical cryptocurrency data and store it in a 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 as 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 a PostgreSQL database and stores the data in it. However, ThreadPoolExecutor creates multiple threads to fetch historical price data from the Binance API. As each thread closes its connection after completing its task, the next thread tries to execute the second task without waiting for the previous one to complete.

Solution: Using concurrent.futures

To solve this problem, you can use the concurrent.futures module, which provides a high-level interface for asynchronous call execution. Here is the updated code snippet:

import config
from binance.client import Client
import psycopg2
from concurrent.futures import ThreadPoolExecutor
def fetch_data():

Binance API request
client = client(config.BINANCE_API_KEY, config.BINANCE_API_SECRET)
response = client.get_order_book()

Save data to 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 as 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 threads and execute tasks asynchronously. The fetch_data function is called with an index range of 0 to config.NUM Threads - 1. This ensures that each thread processes its tasks concurrently without waiting for other threads to finish.

Conclusion

Using `concurrent.