This project demonstrates how to use PySpark to perform streaming data analysis on a set of CSV files. It includes reading streaming data, processing it, and writing the results to files. We will go through the steps of creating a streaming DataFrame, performing data transformations, and writing the results to output files.
Before you get started, make sure you have the following:
Python: Ensure that you have Python installed on your system, preferably Python 3.x.
Apache Spark and PySpark: Install Apache Spark and PySpark to work with streaming data efficiently. Follow the installation instructions on the Apache Spark website.
CSV Files: Prepare a directory containing the CSV files you want to stream and analyze. In this project, we will use financial data (e.g., stock prices) in CSV format.
Project Structure Here's a brief overview of the project structure:
README.md: The documentation you are currently reading. pyspark-logo.png: An optional image for visual appeal (replace it with your project-related image). streaming_data_analysis.py: The Python script that performs the PySpark streaming data analysis. Getting Started Define the Schema: To create a schema for the streaming data, specify the column names and data types. The schema is crucial for parsing and interpreting the data.
Create the Streaming DataFrame: Use PySpark to create a streaming DataFrame by reading data from the specified directory. Ensure that the data source format is set to "csv" and provide the defined schema.
Check Streaming Status: Verify that the streaming DataFrame is correctly configured for streaming data. You should see a True output for df.isStreaming.
Create Stream Writer: Set up a stream writer to handle the data. In this project, we use an in-memory writer for intermediate results.
Start the Write Stream: Begin the stream writing process and ensure it's working correctly. You can check by running queries to display the data.
Data Preprocessing: Perform data preprocessing tasks, such as removing rows with all null values and creating a new column to calculate the difference between "High" and "Low" prices.
Create a New Stream Writer: Set up a new stream writer for the modified data, and start the write stream.
Write to Files: Instead of writing to memory, write the generated data into output files, specifying the output path and checkpoint location.
Stop the Query: Stop the streaming query once the data is written to files.
Read Generated Files: Read the data from the generated output files using a predefined schema. Sort the DataFrame based on the "ID" column.
Results The project results include:
Streaming data analysis on CSV files. Data preprocessing and transformation. Writing the modified data to output files. You can access the results in the generated output files located in the "outputstream" directory.
Contributing If you find any issues, have suggestions for improvements, or would like to contribute to this project, please feel free to open an issue or create a pull request. We welcome collaboration and contributions from the community.