Implementing custom metrics and monitoring in Apache Beam Java applications

25 Sep 2023

In any data processing application, monitoring and tracking various metrics is crucial for ensuring the health and performance of your application. Apache Beam, a powerful data processing framework, provides built-in support for monitoring and reporting metrics. However, sometimes you may need to implement custom metrics specific to your application requirements. In this blog post, we will explore how to implement custom metrics and monitoring in Apache Beam Java applications.

Setting up the Environment

Before diving into the implementation details, let’s ensure we have the necessary setup for our Apache Beam Java application.

Make sure you have Apache Beam installed and set up in your development environment.
Create a new Apache Beam Java project or open an existing one.

Implementing Custom Metrics

Step 1: Define Metrics

The first step is to define the custom metrics you want to track in your application. This can include things like processing time, record counts, or any other relevant metrics. For example, let’s define a metric to track the number of records processed:

import org.apache.beam.sdk.metrics.Counter;
import org.apache.beam.sdk.metrics.Metrics;

public class CustomMetrics {
    private static final Counter recordsProcessed = Metrics.counter(CustomMetrics.class, "recordsProcessed");

    public static void increaseProcessedRecordsCount() {
        recordsProcessed.inc();
    }
}

In the above code, we define a Counter metric called recordsProcessed and expose a method increaseProcessedRecordsCount() to increment the counter.

Step 2: Track Metrics

Next, we need to update our data processing pipeline to track and report the custom metrics. This can be done by calling the appropriate metrics methods at relevant points in the pipeline. For example, let’s add the metric tracking to our pipeline:

import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.io.TextIO;
import org.apache.beam.sdk.transforms.Count;
import org.apache.beam.sdk.transforms.DoFn;
import org.apache.beam.sdk.transforms.MapElements;
import org.apache.beam.sdk.values.KV;
import org.apache.beam.sdk.values.PCollection;

public class MyPipeline {

    public static void main(String[] args) {
        // Create a new pipeline
        Pipeline pipeline = Pipeline.create();

        // Read input data from a file
        PCollection<String> lines = pipeline.apply(TextIO.read().from("input.txt"));

        // Process the data and track metrics
        PCollection<KV<String, Long>> wordCounts = lines
            .apply(MapElements.via(new DoFn<String, KV<String, Long>>() {
                @ProcessElement
                public void processElement(ProcessContext c) {
                    String[] words = c.element().split(" ");
                    for (String word : words) {
                        // Track custom metrics
                        CustomMetrics.increaseProcessedRecordsCount();
                        c.output(KV.of(word, 1L));
                    }
                }
            }))
            .apply(Count.perKey());

        // Write the output to a file
        wordCounts.apply(MapElements.via(new DoFn<KV<String, Long>, String>() {
            @ProcessElement
            public void processElement(ProcessContext c) {
                String word = c.element().getKey();
                Long count = c.element().getValue();
                c.output(word + ": " + count);
            }
        }))
    .apply(TextIO.write().to("output.txt"));

        // Run the pipeline
        pipeline.run();
    }
}

In the above code, we added a call to the CustomMetrics.increaseProcessedRecordsCount() method in the processing logic to track the number of records processed.

Monitoring and Reporting Metrics

Apache Beam provides various options for monitoring and reporting metrics. One common option is to use the Metrics API to extract and display the custom metrics. Here’s an example of how to monitor and report the custom metric we defined:

import org.apache.beam.sdk.metrics.Metrics;
import org.apache.beam.sdk.metrics.MetricQueryResults;

public class MetricsMonitor {
    public static void main(String[] args) {
        // Retrieve the custom metric
        MetricQueryResults metricResults = Metrics
            .metricResults()
            .queryMetricsFiltering(MetricFilter.builder()
                    .namespace(CustomMetrics.class.getName())
                    .name("recordsProcessed")
                    .build());

        // Extract and display the metric value
        for (MetricResult<Long> metric : metricResults.counters()) {
            System.out.println("Records Processed: " + metric.attempted().get());
        }
    }
}

In the above code, we use the Metrics API to retrieve the custom metric by specifying the namespace and name of the metric. We can then iterate over the metric results and display the metric value.

Conclusion

Implementing custom metrics and monitoring in Apache Beam Java applications allows you to track and report specific metrics relevant to your application’s requirements. By following the steps outlined in this blog post, you can easily customize and monitor your data processing pipelines in Apache Beam.

#dataengineering #apachbeam