Ethereum: Binance Multithread Sockets – Functions Not Called Concurrently

The Binance multithread sockets provide a way to receive real-time market data from the Binance API in parallel, allowing for efficient and concurrent access to the data. However, this can lead to issues if not managed properly.

Concurrency Considerations

When using multithreaded sockets, it’s essential to ensure that all threads are properly synchronized to prevent deadlocks and other concurrency-related issues. One common issue is when multiple threads attempt to call functions concurrently without proper synchronization, leading to unexpected behavior or errors.

In the provided code snippet, there are a few potential issues:

• Calling asyncio.gather(): The asyncio.gather() function is used to wait for all tasks to complete and then return them as an iterator. While this can be useful in certain scenarios, it may not be the most efficient way to manage concurrency in this case.

• Calling analyze(res) within a loop

  : Calling analyze(res) within a loop can lead to multiple threads calling the same function concurrently, which can cause unexpected behavior and errors.

Recommended Solution

To address these issues, we recommend using a more robust approach that leverages async/await syntax and synchronization mechanisms provided by the Binance API.

import asyncio


async def analyze(res):

    




Perform some analysis on the received data

    return await process_analysis(res)


async def main():

    client = AsyncClient(binance_url="

    tasks = []

    

    for symbol, pair in symbol_pair:

        task = asyncio.create_task(analyze(res=pair))

        tasks.append(task)

        

    results = await asyncio.gather(*tasks)

    

    
Process the results

    process_results(results)


async def main():

    
Define symbol pairs to analyze

    symbol_pair = [("AAPL", "USD"), ("BTCUSDT", "Btcusdt")]

    

    
Perform analysis and processing on each pair

    await asyncio.gather(*[analyze(res=pair) for pair in symbol_pair])



Run the main function

asyncio.run(main())

Explanation

In this revised code snippet:

• We define an analyze function that performs some analysis on the received data using process_analysis.

• In the main function, we create a list of tasks to perform the analysis concurrently using asyncio.gather. Each task calls the analyze function with the corresponding symbol and pair.

• We use asyncio.create_task to create a new task for each iteration in the loop. This allows us to manage concurrency properly.

• Finally, we process the results by calling process_results, which is not shown in this snippet but would typically be used to perform some action on the received data.

By following these guidelines and using async/await syntax, we can ensure that our code is properly synchronized and concurrent, minimizing the risk of issues and errors.