Google Dataflow : Handling Late Data in Google Dataflow

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Handling late-arriving data is a common challenge when working with streaming data processing systems like Google Dataflow. Late data refers to data that arrives after its expected processing time has already passed. This can occur due to network delays, data skews, or other factors.

In this article, we’ll explore how to handle late-arriving data in Google Dataflow and provide examples of how to implement late data handling in your data processing pipelines.

What is Late Data in Google Dataflow?

Late data refers to data that arrives after its expected processing time has already passed. In a streaming data processing system like Google Dataflow, data is processed in batches according to a fixed window size. Each batch includes data that arrived during a specific time window.

When data arrives late, it can cause processing delays and inaccuracies in your data processing results. For example, if you’re calculating average sales revenue over a 5-minute window, and some of the data arrives late, the calculation will be inaccurate.

Handling Late Data in Google Dataflow

There are several ways to handle late-arriving data in Google Dataflow. The most common approach is to use windowing and triggering, which allows you to define how to handle late data based on your specific use case.

Windowing and triggering in Google Dataflow refer to the way data is grouped and processed based on time windows. A window is a fixed interval of time during which data is collected and processed. A trigger is an event that determines when the processing of a window is complete.

There are two types of triggers in Google Dataflow:

Early triggers: These triggers allow you to process data before the end of a window, based on a predefined condition.

Late triggers: These triggers allow you to process late-arriving data that arrives after the end of a window.

Here are some examples of how to handle late data in Google Dataflow using windowing and triggering.

Example 1: Discarding late data

One way to handle late data is to simply discard it. This approach is suitable for use cases where late data is not critical and does not affect the accuracy of your data processing results.

Here’s an example of how to discard late data in Google Dataflow using the Apache Beam SDK for Java:

PCollection<MyData> data = pipeline
    .apply("Read Data", TextIO.read().from("gs://input-bucket/data.txt"))
    .apply("Parse Data", ParDo.of(new MyDataParser()));

PCollection<MyData> onTimeData = data
    .apply("Window Data", Window.into(FixedWindows.of(Duration.standardMinutes(5))))
    .apply("Filter Late Data", Filter.by(new FilterLateDataFn()));

public static class FilterLateDataFn extends DoFn<MyData, Boolean> {
  @ProcessElement
  public void processElement(ProcessContext c) {
    if (c.element().getTimestamp().isBefore(c.pane().window().maxTimestamp())) {
      c.output(true); // data is on time
    } else {
      c.output(false); // data is late
    }
  }
}

In this example, the FilterLateDataFn class filters out late-arriving data by comparing the timestamp of each data element with the end of the processing window. Data that arrives before the end of the window is processed, while late data is discarded.

Example 2: Accumulating late data

Another way to handle late data is to accumulate it and process it in a subsequent window. This approach is suitable for use cases where late data is critical and needs to be included in your data processing results.

Here’s an example of how to accumulate late data in Google Dataflow using the Apache Beam SDK for Python:

data = (p
        | 'Read Data' >> beam.io.ReadFromPubSub(subscription=subscription_name)
        | 'Parse Data' >> beam.ParDo(ParseData())
        | 'Window Data' >> beam.WindowInto(
            window.FixedWindows(60),
            trigger=trigger.AfterWatermark(
                early=trigger.AfterCount(50),
                late=trigger.AfterCount(10)),
            accumulation_mode=trigger.AccumulationMode.ACCUMULATING)
        | 'Aggregate Data' >> beam.CombineGlobally(AggregateData())
        | 'Write Output' >> beam.io.WriteToBigQuery(
            table_spec,
            write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND))

class ParseData(beam.DoFn):
    def process(self, element):
        timestamp = element['timestamp']
        data = element['data']
        yield beam.window.TimestampedValue((timestamp, data), timestamp)

class AggregateData(beam.CombineFn):
    def create_accumulator(self):
        return {'count': 0, 'sum': 0}

    def add_input(self, accumulator, input):
        accumulator['count'] += 1
        accumulator['sum'] += input[1]
        return accumulator

    def merge_accumulators(self, accumulators):
        result = self.create_accumulator()
        for accumulator in accumulators:
            result['count'] += accumulator['count']
            result['sum'] += accumulator['sum']
        return result

    def extract_output(self, accumulator):
        return accumulator['sum'] / accumulator['count']

In this example, we’re using windowing and triggering to accumulate late-arriving data and include it in our data processing results. We’re using a 60-second window size, and triggering the processing of the window based on a count-based condition: process the window after 50 elements arrive, or after 10 elements arrive after the watermark has passed.

The AccumulateData() function combines the data in each window and calculates the average value, which is then written to BigQuery.

Handling late-arriving data is an essential part of building robust and accurate data processing pipelines in Google Dataflow. By using windowing and triggering, you can define how to handle late data based on your specific use case, whether that means discarding it, accumulating it, or processing it in a subsequent window.

With the support for multiple programming languages, including Java, Python, and Go, Google Dataflow offers a flexible and powerful platform for stream processing that can scale to handle even the most demanding workloads.

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