Ethereum: Process Async Results in Another Thread for Efficient App Architecture

As the demand for decentralized applications (dApps) continues to grow, it’s essential to optimize their architecture to ensure seamless performance. One critical aspect of building efficient dApps is handling asynchronous data processing and visualization. In this article, we’ll explore how to process async results in another thread using Python 3.7, leveraging the Ethereum API for trading data.

Why Async Processing?

When dealing with real-time data, such as trades on the Binance API, async processing is crucial for maintaining a responsive user experience. The traditional approach of blocking all threads until data is processed can lead to significant performance issues and laggy interactions. By offloading async tasks to another thread, we can:

• Improves responsiveness

• Reduces latency

• Scale for high traffic or concurrent use

The Ethereum API for Trade Data

To get started, let’s establish a connection to the Binance API using Python 3.7. We’ll use the requests library to send asynchronous HTTP requests and retrieve trade data.

import requests


execute get_binance_trades(symbol, limit):

    """Retrieve trade data from Binance API"

    url = f"

    response = requests . get ( url ) ;

    return response . json ( )

Process Async Results in Another Thread

To process async results, we will create a separate thread using the threading library. We will use the asyncio library for asynchronous I/O operations.

async import


class TradeProcessor:

    set __init__(self):

        self.trades = []


    async def fetch_trades(self, symbol):

        """Get trade data from Binance API and add to list"

        trades = await get_binance_trades ( symbol , 10 ) 
Retrieve up to 10 trades

        self . trades . extend ( trades )


    async def process_trades ( self ): .

        """Process and display trade date"

        while True:

            if not self.trades:

                break

            print(f"Processing {len(self.trades)} trades...")

            
Visualize trade data using Plotly

            import plotly.graph_objs and go

            fig = go . Figure ( data = [ go . Scatter ( x = self . trades [ : 20 ] , y = self . trades [ : 20 ])])

            fig.update_layout(title="Trade Data", set_title="Timestamp", layout_title="Price");

            print(fig)

            await asyncio.sleep(5) 
Wait for 5 seconds before processing next batch

Architecture App

Our app architecture consists of two main components:

• Trade Processor: This class is responsible for fetching trade data from the Binance API and adding it to a list. The process_trades method processes the trades in batches using another thread.

• Web App: We’ll create a web interface that displays the processed trade data, using Plotly to visualize the results.

import dash

from Dash Import DCC , html , no_update

import plotly.graph_objs and go


app = dash.Dash(__name__)


app.layout = html.Div([

    html.H1("Trade Data App"),

    dcc.Graph(id="trade-graph", figure=go.Figure(data=[go.Scatter(x=[], y=[])])),

])


@app.callback(

    [html.P for _ in range(20)],

    [dash.dependencies.Output("trade-graph", "figure")],

    [dash.dependencies.Input("trade-graph", "update-plotly")]

)

def update_graph(updated_plotly):

    
Update the trade graph using Plotly

    trades = app.data["trades"]

    fig = go.Figure(data=[go.Scatter(x=trades[:20], y=trades[:20])])

    fig.update_layout(title="Trade Data", set_title="Timestamp", layout_title="Price");

    return fig


if __name__ == "__main__":

    app.run_server(debug=True)

In this example, we’ve created a simple web interface that displays the processed trade data.