Apache beam python dataflow with gcp pub sub counter is over counting

When working with Apache Beam in Python and using Google Cloud Pub/Sub, you may encounter a situation where the counter is over counting. This can be a frustrating issue to deal with, but fortunately, there are several ways to solve it.

Option 1: Adjusting the Windowing Strategy

One possible solution is to adjust the windowing strategy in your Apache Beam pipeline. By default, Apache Beam uses a fixed windowing strategy, which may not be suitable for your specific use case. You can try changing the windowing strategy to a sliding window or a session window, depending on your requirements.

# Adjust the windowing strategy
windowed_data = data | beam.WindowInto(window.FixedWindows(10))

By using a different windowing strategy, you can control the size and frequency of the windows, which may help in reducing the over counting issue.

Option 2: Implementing Deduplication Logic

Another approach is to implement deduplication logic in your pipeline. This involves keeping track of the processed messages and discarding any duplicates. You can achieve this by using a stateful transform or by storing the processed message IDs in a database or cache.

# Implement deduplication logic
def deduplicate_messages(message):
    if message.id not in processed_message_ids:
        return message

deduplicated_data = data | beam.Map(deduplicate_messages)

By removing duplicate messages from the pipeline, you can prevent the counter from over counting.

Option 3: Throttling the Pub/Sub Messages

If the over counting issue is caused by a high volume of incoming messages, you can consider throttling the Pub/Sub messages. This involves setting a limit on the number of messages processed per unit of time. You can achieve this by using a rate limiter or by implementing a custom throttling mechanism.

# Throttle the Pub/Sub messages
throttled_data = data | beam.ParDo(ThrottlingDoFn())

By controlling the rate at which messages are processed, you can prevent the counter from over counting.

After considering these three options, it is difficult to determine which one is the best as it depends on the specific requirements and constraints of your application. However, adjusting the windowing strategy and implementing deduplication logic are generally effective approaches for resolving the over counting issue in Apache Beam Python with GCP Pub/Sub. Throttling the Pub/Sub messages may be more suitable in cases where the issue is caused by a high volume of incoming messages.

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9 Responses

  1. Option 2 seems like a no-brainer. Why complicate things? Just implement the deduplication logic! 🤷‍♀️

    1. I respectfully disagree. Option 2 may be a temporary fix, but it addresses the immediate problem. Fixing the underlying issue could take time and resources. Sometimes a Band-Aid solution is necessary to prevent further harm while working towards a long-term solution.

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