Read Avro Files

Read Avro Files

Overview

This skill helps extract JSON data from Apache Avro files and displays them in a readable format. It handles deserialization of nested byte strings and saves the output to a JSON file.

When to Use This Skill

• User mentions they have an Avro file (.avro extension)
• User wants to convert Avro to JSON
• User wants to see the contents of an Avro file
• User provides a path to an .avro file

Workflow

Copy and track progress:

Avro Conversion Progress:
- [ ] Step 1: Verify file path or ask user
- [ ] Step 2: Check dependencies (avro-python3)
- [ ] Step 3: Run conversion script
- [ ] Step 4: Verify JSON output created
- [ ] Step 5: Present results to user

Step 1: Verify File Path

If the file path is not provided by the user:

• Use the AskUserQuestion tool to get the Avro file path
• Ask: "Please provide the full path to the Avro file you want to convert"

Step 2: Check Dependencies

Install dependencies if needed:

pip install avro-python3

Only install if the avro module is not already available.

Step 3: Run Conversion Script

Run the bundled script (do not read its contents):

python scripts/read_avro.py "<avro_file_path>"

The script will:

• Display each record as formatted JSON
• Deserialize the Body field if it contains nested JSON
• Save the output to a .json file in the same directory

For script implementation details, see scripts/read_avro.py.

Step 4: Verify JSON Output Created

Check that the output file was created:

• Output file name: same as input but with .json extension
• Location: same directory as the input file

Step 5: Present Results

Show the user:

• Where the JSON output was saved
• Number of records found
• Key information from the records if relevant

Expected Output Format

The script produces:

• Console output showing each record with formatted JSON
• A .json file saved in the same directory as the input file
• Record count summary

Common Use Cases

1. Event Hub captured data: Avro files from Azure Event Hub captures containing event metadata and body
2. Kafka messages: Avro-serialized Kafka messages
3. Data pipeline debugging: Inspecting intermediate Avro files in data processing pipelines
4. Schema validation: Viewing actual data structure for schema comparison

Common Issues

FileNotFoundError:

• Verify the path exists
• Use absolute paths instead of relative paths
• Check for typos in the path

Module not found (avro):

• Run: pip install avro-python3
• Verify installation: python -c "import avro"

JSON decode error in Body field:

• The Body field may have unexpected encoding
• Check Event Hub configuration if applicable
• The script will show a warning but continue processing

Notes

• The script handles nested JSON in byte string format (common in Event Hub captures)
• Dates and complex types are converted to strings in the output
• Large files will show all records in console but save efficiently to JSON
• The bundled script is optimized for reliability and handles edge cases