How to Use Flink SQL, Streamlit, and Kafka: Part 2

Building on a Streamlit market-data application that streams stock bid prices from an Alpaca websocket through Kafka and Flink SQL in Confluent Cloud, the post explains how Python’s asyncio enables a Kafka producer and consumer to operate concurrently in the same single-threaded application. Two coroutines are coordinated with asyncio.gather(), while asyncio.sleep() yields control to the event loop after consumer polling so other tasks can continue, unlike blocking time.sleep(). It also describes configuring a Flink SQL table with a single Kafka partition for a simplified demonstration, then rendering the latest 20 price records as a Streamlit Altair line chart using ranked window-end timestamps. Deployment involved linking Streamlit to a GitHub repository and managing secrets separately for local and hosted environments, while Alpaca free-tier connection limits led the author to use recorded SPY Kafka data at a specified offset rather than provide a fully live public app. The project highlighted practical challenges involving Flink table behavior, asynchronous programming, visualization, and deployment while demonstrating a workflow for building data-streaming applications with Kafka, Flink SQL, and Streamlit.

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