Ethereum: Process async results in another thread – app architecture (python-3.7)

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

Ethereum: Process async results in another thread - app architecture (python-3.7)

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, livering The Ethereum API for trade data.

Why Async Processing?

When dealing with real-time data, Such as trades on the binance API, Async Processing is Crucial for Maining 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:

Improve Responsiveness

Reduce Latency

Scale for High Traffic or Competitor Usage

The Ethereum API for Trade Data

To Start, Let’s Establish A Connection to the Binance API Using Python 3.7. We’ll use the ‚requestslibrary to send asynchronous http requests and retrieve trade data.

Python



Import requests

Def 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’ll create a separate thread using the threading library. We’ll use the asyncio library for asynchronous i/o operations.

`Python



Import asyncio

Class Trade Processor:

    Def __init __ (loan):

        loans.trades = []

    Async Def Fetch_trades (Loan, Symbol):

        "" "Fetch 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 Visualize Trade Data" "

        While True:

            if not self.trades:

                break

            Print (f "Processing {len (self.trades)} Trades ...")



Visualize Trade Data Using Plotly

            Import plotly. graph_objs as go

            Fig = Go.Figure (Data = [Go.Catter (x = Loan.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 for 5 seconds before processing next batch

Architecture app

Our 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 mark, using plotly to visualize the results.

`Python



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.catter (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.Catter (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’ve created a simple web interface that displays the processed trade data.

data analytics winning