Ethereum: Processing asynchronous results in another thread for efficient application architecture

As the demand for decentralized applications (dApps) continues to grow, it is critical to optimize their architecture to ensure seamless operation. One of the most important aspects of creating effective dApps is handling asynchronous data processing and visualization. In this article, we’ll look at how to process asynchronous results in another thread with Python 3.7 using the Ethereum Trade Data API.

Why Asynchronous Processing?

When dealing with real-time data, such as transactions on the Binance API, asynchronous processing is critical to maintaining a responsive user experience. The traditional approach of blocking all threads until the data is processed can lead to significant performance issues and interaction latency. Advanced asynchronous tasks in the second thread, we can:

• Improve responsiveness

• Reduce latency

• Scale for high traffic or concurrent use

Ethereum API for Trading Data

First, let’s connect to the Binance API using Python 3.7. We will use the requests' library to send asynchronous HTTP requests and receive trading data.

import requests
def get_binance_trades(symbol, limit):
"""Extract trade data from Binance API"""
url = f"
response = requests.get(url)
return response.json()

To process asynchronous results, we will create a separate thread using the threading'' library. For asynchronous I/O operations, we will use theasynciolibrary.

import asyncio
class TradeProcessor:
def __init__(self):
self.trades = []
async def fetch_trades(self, symbol):
"""Extract trade data from Binance API and add to list"""
trades = await get_binance_trades(symbol, 10) 




Extract up to 10 trades
self.trades.extend(trades)
async def process_trades(self):
"""Process and visualize trade data"""
while True:
if not self.trades:
break
print(f"Processing {len(self.trades)} trades...")

Visualization of trading data using Plotly
import plotly.graph_objs as go
fig = go.Figure(data=[go.Scatter(x=self.trades[:20], y=self.trades[:20])])
fig.update_layout(title="Trade Data", xaxis_title="Timestamp", yaxis_title="Price")
print(fig)
await asyncio.sleep(5) 
Wait 5 seconds before processing the next packet

• Trade Processor

  : This class is responsible for extracting trade data from the Binance API and adding it to the list. The process_trades` method processes trades in batches using the second thread.

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

import dash
from dash import dcc, html, no_update
import plotly.graph_objs as 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 your trading chart with Plotly
trades = app.data["trades"]
fig = go.Figure(data=[go.Scatter(x=trades[:20], y=trades[:20])])
fig.update_layout(title="Trade Data", xaxis_title="Timestamp", yaxis_title="Price")
return fig
if __name__ == "__main__":
app.run_server(debug=True)

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