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Introducing Streaming Support in Subxtpy: Real-Time Blockchain Data in Python

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    Philomath Ingeniare
    Twitter

In the world of blockchain development, accessing real-time data is crucial for building responsive and interactive applications. Whether you’re monitoring transactions, tracking events, or analyzing live blockchain metrics, having immediate access to data streams can significantly enhance your application’s capabilities.

Building on our mission to empower Python developers in the Substrate ecosystem, we’re excited to announce a major new feature in subxtpy: Streaming Support. This addition brings real-time blockchain data directly to your Python applications, opening up new possibilities for developers and data scientists alike.

Unlocking Real-Time Blockchain Interaction

Subxtpy now allows you to subscribe to live blockchain events, such as new blocks and extrinsics, directly within your Python code. This streaming capability is essential for applications that require up-to-the-moment data, such as:

  • Live Data Dashboards: Visualize blockchain activity as it happens.
  • Automated Trading Bots: React to market changes with minimal latency.
  • Event Monitoring Systems: Trigger actions based on specific on-chain events.
  • Analytics and Research Tools: Collect and analyze data streams for insights.

Key Streaming Features

1. Subscribe to New Blocks

With subxtpy, you can now subscribe to new blocks as they are finalized on the blockchain. This feature allows your application to react immediately to changes in the blockchain state.

import asyncio
from subxtpy import SubxtClient

async def main():
    client = await SubxtClient.from_url("wss://rpc.polkadot.io:443")

    async for block in client.subscribe_new_blocks():
        print(f"Block #{block['block_number']} ({block['block_hash']}):")
        # Process block data here

asyncio.run(main())

2. Access Extrinsics in Real-Time

Not only can you receive notifications of new blocks, but you can also access the extrinsics (transactions) contained within them. This feature is essential for monitoring specific transactions or activities on the blockchain.

async for block in client.subscribe_new_blocks():
    for extrinsic in block['extrinsics']:
        pallet = extrinsic['pallet']
        call = extrinsic['call']
        print(f"  {pallet}/{call}")
        # Process extrinsic data here

3. Detailed Extrinsic Information

Subxtpy provides detailed information about each extrinsic, including:

  • Fields: The specific data associated with the extrinsic.
  • Signed Extensions: Metadata such as nonce, mortality, and transaction fees.

This information is crucial for applications that need to understand the context and details of each transaction.

Why Streaming Support Matters

Enhanced Responsiveness

By accessing data as soon as it’s available, your applications can respond faster to blockchain events. This responsiveness is vital for applications like trading platforms, alert systems, and interactive user interfaces.

Simplified Development

Subxtpy’s streaming support abstracts the complexities of handling asynchronous data streams, allowing you to focus on your application’s core logic. With straightforward async iterators, integrating streaming data becomes as simple as writing a for loop.

Expanded Use Cases

The ability to stream blockchain data in real-time opens up new possibilities, including:

  • Real-Time Analytics: Perform on-the-fly analysis of blockchain data.
  • Monitoring and Alerts: Set up triggers for specific events or conditions.
  • Dynamic Data Visualization: Create dashboards that update in real-time.

Getting Started with Streaming in Subxtpy

Installation

Ensure you have subxtpy installed in your Python environment:

pip install subxtpy

Example: Monitoring New Blocks

Below is a complete example of how to use subxtpy’s streaming support to monitor new blocks and print detailed information about each extrinsic:

import asyncio
from subxtpy import SubxtClient

async def main():
    client = await SubxtClient.from_url("wss://rpc.polkadot.io:443")

    async for block in client.subscribe_new_blocks():
        print(f"Block #{block['block_number']} ({block['block_hash']}):")
        for extrinsic in block['extrinsics']:
            pallet = extrinsic['pallet']
            call = extrinsic['call']
            print(f"  {pallet}/{call}")
            print("    Fields:")
            print(f"      {extrinsic['fields']}")
            print("    Signed Extensions:")
            for ext in extrinsic['signed_extensions']:
                name = ext['name']
                value = ext['value']
                print(f"      {name}: {value}")
            print()

asyncio.run(main())

Integrating Streaming into Your Applications

Asynchronous Programming with Asyncio

Subxtpy leverages Python’s asyncio library for asynchronous operations. This approach ensures that your application remains responsive and efficient, even when handling large volumes of data.

Error Handling and Robustness

Subxtpy includes comprehensive error handling, converting Rust errors into Python exceptions. This design allows you to write robust applications that can gracefully handle network issues or other runtime errors.

Compatibility with Python Ecosystem

Because subxtpy is built for Python, it integrates seamlessly with popular libraries for data analysis, visualization, and machine learning. This compatibility enables you to build powerful tools that leverage the strengths of the Python ecosystem.

Benefits to the Substrate Ecosystem

Broadened Developer Participation

By providing streaming support in Python, subxtpy lowers the barrier to entry for developers who prefer Python over Rust. This inclusivity fosters a more diverse and innovative community within the Substrate ecosystem.

Accelerated Development of Tools and Services

Real-time data access empowers developers to create advanced tools and services, such as analytics platforms, monitoring tools, and automated systems, enriching the Substrate ecosystem’s capabilities.

Enhanced Data Analysis Opportunities

Python’s rich set of data analysis libraries, combined with subxtpy’s streaming support, enables data scientists to perform real-time analysis of blockchain data, leading to deeper insights and more informed decision-making.