Error Handling Patterns

1. Introduction

Error Handling Patterns are strategies to handle parsing exceptions, validation failures, or external API timeouts inside streaming pipelines without stalling the execution flow.

2. Why This Concept Exists

In distributed streaming pipelines, a single malformed event (e.g. invalid JSON) that throws an unhandled exception will cause the task runner to retry the event indefinitely. This halts the entire stream. Robust error-handling patterns route these problematic events aside.

3. Code Example

Using basic Try-Catch patterns inside a custom mapping function:

import apache_beam as beam
import json

def safe_parse_json(line):
    try:
        data = json.loads(line)
        return [data] # Emit successfully
    except Exception as e:
        # Log and handle error, or return empty list to filter out
        print(f"Error parsing line: {e}")
        return []

with beam.Pipeline() as p:
    (p 
     | beam.Create(['{"id": 1}', 'invalid-json', '{"id": 2}'])
     | "SafeParse" >> beam.FlatMap(safe_parse_json)
     | beam.Map(print))

4. Key Takeaways

• Wrap parsing and API requests inside standard Python try-except blocks.
• Return empty lists [] in FlatMap transforms to drop malformed elements silently if routing is not needed.