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https://github.com/j3-signalroom/supercharge_streamlit-apache_flink

Engaging, interactive visualizations crafted with Streamlit, seamlessly powered by Apache Flink in batch mode to reveal deep insights from data.
https://github.com/j3-signalroom/supercharge_streamlit-apache_flink

apache-flink apache-iceberg aws-glue-data-catalog flink iceberg kafka pyflink streamlit streamlit-dashboard

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Engaging, interactive visualizations crafted with Streamlit, seamlessly powered by Apache Flink in batch mode to reveal deep insights from data.

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README 

        
# Supercharge Your Streamlit Visualizations: Batch Processing Iceberg Data with Apache Flink

After you have all your fun running the Java-based Flink application [`DataGeneratorApp`](https://github.com/j3-signalroom/apache_flink-kickstarter/blob/main/java/README.md) to kickstart the data pipeline. This app powers up your Kafka topics—`airline.skyone` and `airline.sunset`—by generating sample records that fuel the rest of the process. Once the data flows into Kafka, launch the [`FlightImporterApp`](https://github.com/j3-signalroom/apache_flink-kickstarter/blob/main/python/README.md) Flink application. This crucial step reads the enriched data from Kafka and writes it into the `apache_kickstarter.airlines.flight` Apache Iceberg table, seamlessly preparing your data for advanced analytics and insight generation.



![datageneratorapp-flightimporterapp](.blog/images/datageneratorapp-flightimporterapp.png)



Get ready to see the magic of Flink in action! Now, it's time to share those insights with the world! One fantastic way to do that is with [`Streamlit`](https://streamlit.io/), which allows you to easily create interactive visualizations. Streamlit is intuitive, powerful, and designed with Python developers in mind, making it a breeze to turn raw data into captivating dashboards. 😉  



![iceberg-flink-streamlit-drawing](.blog/images/iceberg-flink-streamlit-drawing.png)



**Table of Contents**



+ [1.0 Power up the Apache Flink Docker containers](#10-power-up-the-apache-flink-docker-containers)

+ [2.0 Supercharge Your Streamlit Visualizations](#20-supercharge-your-streamlit-visualizations)

    - [2.1 Did you notice we prepended `uv run` to `streamlit run`?](#21-did-you-notice-we-prepended-uv-run-to-streamlit-run)

+ [3.0 Local Integration: How This App Harnesses Apache Flink](#30-local-integration-how-this-app-harnesses-apache-flink)

+ [4.0 Resources](#40-resources)



## 1.0 Power up the Apache Flink Docker containers



> **Prerequisite**

> 

> Before you can run `scripts/run-flink-locally.sh` Bash script, you need to install the [`aws2-wrap`](https://pypi.org/project/aws2-wrap/#description) utility.  If you have a Mac machine, run this command from your Terminal:

> ````bash

> brew install aws2-wrap

> ````

>

> If you are not using a Mac, make sure you have Python3.x installed on your machine, and run this command from your Terminal:

> ```bash

> pip install aws2-wrap

> ```



This section guides you through the local setup (on one machine but in separate containers) of the Apache Flink cluster in [Session mode](https://github.com/j3-signalroom/j3-techstack-lexicon/blob/main/apache-flink-glossary.md#flink-session-mode) using Docker containers with support for Apache Iceberg.  Run the `bash` script below to start the Apache Flink cluster in Session Mode on your machine:



```bash

scripts/run-flink-locally.sh  --profile=

                                             --chip=

                                             --aws-s3-bucket=

```

> Argument placeholder|Replace with

> -|-

> ``|`on` to start up your very own local Apache Cluster running in Docker containers, otherwise `off` to stop the Docker containers.

> ``|your AWS SSO profile name for your AWS infrastructue that host your AWS Secrets Manager.

> ``|if you are running on a Mac with M1, M2, or M3 chip, use `arm64`.  Otherwise, use `amd64`.

> ``|specify the name of the AWS S3 bucket used to store Apache Iceberg files.



To learn more about this script, click [here](.blog/run-flink-locally-script-explanation.md).



## 2.0 Supercharge Your Streamlit Visualizations

To access the Flink JobManager (`supercharge_streamlit-apache_flink-jobmanager-1`) container, open the interactive shell by running:



```bash

docker exec -it -w /opt/flink/python_apps supercharge_streamlit-apache_flink-jobmanager-1 /bin/bash

```



Jump right into the container and take charge! You’ll have full control to run commands, explore the file system, and tackle any tasks you need. You’ll land directly in the `/opt/flink/python_apps` directory—this is the headquarters for the Python script in the repo.



To illustrate, I created a Streamlit script that queries the `apache_kickstarter.airlines.flight` Apache Iceberg Table, harnessing Flink SQL to extract valuable insights. These insights are then brought to life through a Streamlit dashboard, transforming raw data into an accessible, visual experience.



Here you go, run this in the docker container terminal command line:



```bash

uv run streamlit run app.py -- --aws-s3-bucket  --aws-region 

```

> _Notice the extra `--` between `streamlit run app.py` and the actual script arguments.  This is necessary to pass arguments to the Streamlit script without causing conflicts with Streamlit's own CLI options._



When you run the script, for instance, it produces the following output:



![streamlit-run-from-terminal-screenshot](.blog/images/streamlit-run-from-terminal-screenshot.png)



Open your host web browser, enter the local URL, `localhost:8501`, and in a few moments this web page will be displayed:



![streamlit-screenshot](.blog/images/streamlit-screenshot.png)



> _**"After many years in this industry, I’m still amazed by what we can achieve today!  The possibilities are endless—enjoy the ride!"**_

> 

> _**---J3**_



### 2.1 Did you notice we prepended `uv run` to `streamlit run`?

You maybe asking yourself why.  Well, `uv` is an incredibly fast Python package installer and dependency resolver, written in [**Rust**](https://github.blog/developer-skills/programming-languages-and-frameworks/why-rust-is-the-most-admired-language-among-developers/), and designed to seamlessly replace `pip`, `pipx`, `poetry`, `pyenv`, `twine`, `virtualenv`, and more in your workflows. By prefixing `uv run` to a command, you're ensuring that the command runs in an optimal Python environment.



Now, let's go a little deeper into the magic behind `uv run`:

- When you use it with a file ending in `.py` or an HTTP(S) URL, `uv` treats it as a script and runs it with a Python interpreter. In other words, `uv run file.py` is equivalent to `uv run python file.py`. If you're working with a URL, `uv` even downloads it temporarily to execute it. Any inline dependency metadata is installed into an isolated, temporary environment—meaning zero leftover mess! When used with `-`, the input will be read from `stdin`, and treated as a Python script.

- If used in a project directory, `uv` will automatically create or update the project environment before running the command.

- Outside of a project, if there's a virtual environment present in your current directory (or any parent directory), `uv` runs the command in that environment. If no environment is found, it uses the interpreter's environment.



So what does this mean when we put `uv run` before `streamlit run`? It means `uv` takes care of all the setup—fast and seamless—right in your local Docker container. If you think AI/ML is magic, the work the folks at [Astral](https://astral.sh/) have done with `uv` is pure wizardry!



Curious to learn more about [Astral](https://astral.sh/)'s `uv`? Check these out:

- Documentation: Learn about [`uv`](https://docs.astral.sh/uv/).

- Video: [`uv` IS THE FUTURE OF PYTHON PACKING!](https://www.youtube.com/watch?v=8UuW8o4bHbw).



## 3.0 Local Integration: How This App Harnesses Apache Flink

This [`app.py`](app.py) Python script streamlines Apache Flink integration by leveraging PyFlink directly in the [`app.py`](app.py) script. Running the Flink job using the same Python process as the [`Streamlit`](https://streamlit.io/) app offers an intuitive setup for local testing and debugging. It's an ideal solution for quickly iterating on data processing logic without additional infrastructure.



_However, a more advanced setup is recommended for production environments where scalability is critical. Running Flink jobs in a separate process and enabling communication through Kafka or a REST API provides greater scalability, albeit at the cost of added complexity._



The simplicity of this app’s current approach makes it perfect for prototyping and development while offering a foundation to explore more sophisticated integration methods as your requirements grow.



## 4.0 Resources



[Flink Python Docs](https://nightlies.apache.org/flink/flink-docs-master/api/python/)



[PyFlink API Reference](https://nightlies.apache.org/flink/flink-docs-release-1.20/api/python/reference/index.html)



[Apache Iceberg in Action with Apache Flink using Python](https://github.com/j3-signalroom/apache_flink-kickstarter/blob/main/.blog/apache-iceberg-in-action-with-apache-flink-using-python.md)



[Streamlit Documentation](https://docs.streamlit.io/)