Linking metrics data with traces is a powerful technique for enhancing observability in distributed systems. It allows developers and DevOps engineers to not only track system performance via metrics but also to quickly dive into detailed traces when anomalies occur. We’ll walk through a Go code example that demonstrates this approach using exemplars and then discuss how to achieve similar functionality in Ruby. We’ll also dive into additional practical ideas and best practices that set this discussion apart from other content on the subject.
Explanation of the Go Code
The Go snippet below demonstrates how to link metrics with trace data by adding an exemplar to a Prometheus counter metric. Let’s break down each part of the code.
Check if the Span is Sampled
if span.SpanContext().IsSampled() {
Before doing any work, the code checks whether the current span (a unit of trace) is sampled. In distributed tracing, only a subset of spans may be recorded for performance reasons. Sampling ensures that only a representative fraction of all spans is captured, avoiding overwhelming your tracing system with data. In this code, by checking if the span is sampled, we guarantee that we only attach exemplars trace links to metrics when the corresponding trace data is available. This condition helps in reducing overhead and ensures that the trace context is valid and useful.
Extract the Trace ID
traceID := span.SpanContext().TraceID.String()
Once we have confirmed that the span is sampled, the next step is to extract the trace ID as a string. The trace ID is a unique identifier for the entire trace, allowing for correlation between the metric and the detailed trace information. This correlation is crucial because it enables you to drill down into the specific trace data associated with a metric anomaly, providing context for debugging and performance analysis.
Add the Exemplar to the Metric
counter.(prometheus.ExemplarAdder).AddWithExemplar(1, prometheus.Labels{"trace_id": traceID})
Here, the code casts the counter metric to an interface that supports exemplars (prometheus.ExemplarAdder). The AddWithExemplar method is then used to increment the counter by 1 and attach an exemplar a label containing the trace_id. The inclusion of the exemplar creates a direct link between the metric data and the trace data. This linkage is particularly valuable during troubleshooting because it allows engineers to quickly trace the root cause of a metric spike or anomaly by following the associated trace.
Adding Practical Functionality in Ruby
While Go’s Prometheus client natively supports exemplars, Ruby’s ecosystem does not yet offer built-in support for this feature. However, this limitation doesn’t mean you cannot achieve similar functionality. With a little creativity, you can extend your metric instrumentation in Ruby to capture additional metadata such as trace IDs alongside your metric increments.
Simulating Exemplars in Ruby
One practical approach is to simulate exemplars by maintaining a side-store (for instance, an in-memory hash) that records “exemplar-like” data. This extra layer of metadata can later be correlated with trace data stored by your tracing solution (e.g., OpenTelemetry).
Below is a Ruby example that demonstrates this approach:
require 'opentelemetry/sdk'
require 'prometheus/client'
# Simulated exemplar storage. In a production app,
# you might use a more robust solution (e.g., a log, database, or enhanced metric backend).
EXEMPLAR_STORE = {}
# A helper method to add a metric value along with exemplar data if available.
def add_metric_with_exemplar(counter, span)
# Check if the span is sampled.
# Assuming the span context responds to `sampled?` and `trace_id`
if span.context.sampled?
trace_id = span.context.trace_id
# Increment the counter by 1.
counter.increment
# Save the exemplar data in a side-store keyed by counter name.
EXEMPLAR_STORE[counter.name] ||= []
EXEMPLAR_STORE[counter.name] << { value: 1, labels: { trace_id: trace_id } }
else
# Simply increment the counter if the span isn't sampled.
counter.increment
end
end
# --- Example Usage ---
# Create a Prometheus registry and a counter metric.
prometheus = Prometheus::Client.registry
counter = Prometheus::Client::Counter.new(:example_counter, 'An example counter')
prometheus.register(counter)
# Fake span and context objects for demonstration purposes.
SpanContext = Struct.new(:trace_id, :sampled?)
Span = Struct.new(:context)
# Create a sample span (where sampled? is true)
span = Span.new(SpanContext.new('abc123', true))
# Add metric with exemplar simulation.
add_metric_with_exemplar(counter, span)
# Output the exemplar store to see the attached exemplar data.
puts "Exemplar Store:"
puts EXEMPLAR_STORE.inspect
How the Ruby Code Works
Setup and Metric Registration
- Gem Requirements:
The code begins by requiring both the OpenTelemetry SDK and the Prometheus Client gems. These libraries enable you to handle tracing and metrics, respectively. - Exemplar Storage:
A global hash,EXEMPLAR_STORE, acts as our simulated storage for exemplar data. In production environments, consider using a more durable solution such as a log file, a database, or an external metrics backend that supports custom metadata. - Metric Creation:
A Prometheus counter is created and registered with the client’s registry. This counter will be incremented as events occur in the application.
Simulated Span & Context
- Creating Structs:
For demonstration purposes, simple RubyStructobjects are used to mimic a span and its context. In real-world applications, you would work with objects provided by the OpenTelemetry Ruby SDK. - Span Context:
The span context includes atrace_idand asampled?method. These allow you to check if the span is recorded and retrieve its unique identifier.
The Helper Function add_metric_with_exemplar
- Conditional Check:
The function first checks if the span is sampled. This is similar to the Go implementation, ensuring that exemplar data is only recorded when valid trace information exists. - Metric Increment and Exemplar Recording:
If the span is sampled, the trace ID is extracted, and the counter is incremented. Simultaneously, the exemplar data—comprising the value increment and the trace ID—is stored in theEXEMPLAR_STORE. This simulated exemplar mimics the Go behavior of linking metrics with trace data. - Fallback:
If the span is not sampled, the function simply increments the counter without recording any additional metadata.
Expanding Beyond the Basics: Advanced Ideas and Best Practices
To make this article truly stand out, let’s explore some additional ideas and best practices that aren’t commonly covered by competitors. These insights aim to provide a deeper understanding and practical steps to enhance your observability strategy.
Persistent Exemplar Storage
While an in-memory hash (as used in the Ruby example) is useful for demonstration purposes or small applications, it may not be sufficient for production environments where durability and scalability are critical.
- Database Integration:
Consider integrating a lightweight database such as SQLite or a high-performance solution like PostgreSQL to persist exemplar data. This allows for historical analysis and correlation of metrics with traces over extended periods. - Log-based Systems:
Alternatively, you can log exemplar data to a centralized logging system like ELK (Elasticsearch, Logstash, Kibana) or Splunk. This can be especially useful when you need to perform complex searches and aggregations over long-term data.
Enhancing the Exemplar Data Structure
Your exemplar storage can be extended to include additional contextual information beyond the trace ID. Some ideas include:
- Timestamp:
Recording the exact time when the metric was incremented can help correlate with events in other monitoring systems. - Service Name:
Including the service or component name helps in environments where multiple services are emitting metrics. - Custom Labels:
Depending on your architecture, you might include additional labels such as host, environment (e.g., production, staging), or version. This further refines the observability process and makes it easier to pinpoint issues.
Here’s an enhanced Ruby helper method:
def add_metric_with_enhanced_exemplar(counter, span, additional_labels = {})
if span.context.sampled?
trace_id = span.context.trace_id
counter.increment
# Enhance exemplar data with timestamp and any additional labels provided.
exemplar_data = {
value: 1,
timestamp: Time.now.utc,
labels: { trace_id: trace_id }.merge(additional_labels)
}
EXEMPLAR_STORE[counter.name] ||= []
EXEMPLAR_STORE[counter.name] << exemplar_data
else
counter.increment
end
end
Integration with Distributed Tracing Backends
As the ecosystem evolves, it’s likely that Ruby libraries will begin to offer more native support for exemplars. Meanwhile, integrating your simulated exemplar data with distributed tracing systems such as Jaeger or Zipkin can be highly beneficial.
- API Endpoints:
Build API endpoints that query both your metrics store and your exemplar store. When an anomaly is detected in your metrics dashboard, these endpoints can fetch and display correlated trace data. - Alerting Mechanisms:
Develop custom alerting systems that trigger when specific metric thresholds are exceeded. When an alert is triggered, the system can automatically include the relevant exemplar data (i.e., trace IDs) in the alert message. This can drastically reduce the time required to diagnose issues.
Embracing Asynchronous Processing
For high-throughput applications, consider processing and storing exemplars asynchronously. This ensures that your metric collection does not introduce latency into your request handling.
- Background Jobs:
Use background job processors like Sidekiq or Resque to handle the persistence of exemplar data. When a metric is recorded, enqueue a job that processes and stores the corresponding exemplar data. This decouples the critical path of your application from the overhead of logging additional metadata. - Batch Processing:
If you are using a database or log aggregation service, consider batching exemplar writes. This minimizes the load on your storage backend and improves overall performance.
Visualization and Analysis
Finally, a significant part of observability is being able to visualize and analyze your data. Here are a few suggestions:
- Custom Dashboards:
Build custom dashboards that overlay your metrics with exemplar data. Tools like Grafana can be configured to display both metrics and logs, providing a comprehensive view of system health. - Drill-down Capabilities:
Implement drill-down functionality in your dashboards. When an anomaly is detected, clicking on the metric should reveal the associated trace data, enriched with the additional contextual information you’ve captured. - Correlation Algorithms:
Develop algorithms that correlate exemplar data with other sources of log and metric data. This can help in automatically identifying root causes and patterns that lead to system failures.
Conclusion
Linking metrics data with trace information using exemplars is a powerful technique to enhance observability in distributed systems. The Go example we discussed shows how exemplars can be added directly using Prometheus’ built-in support, creating a direct link between a metric and its corresponding trace via a trace ID.
In Ruby, while native support for exemplars is not yet available, you can simulate this functionality by capturing additional metadata alongside your metric increments. By using a side-store (such as an in-memory hash or a persistent database) to record trace IDs and other contextual information, you can effectively bridge the gap between metrics and traces.
Moreover, we’ve discussed additional advanced ideas ranging from persistent storage solutions and enhanced data structures to integration with distributed tracing back ends and asynchronous processing that provide a more robust and production-ready approach to linking metrics and traces. These extra insights not only offer a more comprehensive serviceability strategy but also set your implementation apart from standard approaches.
By taking these steps, you can ensure that when an anomaly is detected, you have all the contextual data required to quickly diagnose and resolve issues, ultimately leading to more reliable and maintainable systems. Embracing this holistic approach to monitoring will pay dividends in the long run, making your systems more resilient and easier to debug under pressure.