Understanding SLI Metrics, Distributed Tracing, and OpenTelemetry Logging

Throwing some more light on Know-how of today’s rapidly growing senarios in software development and operations area like sli metrics, distributed tracing, OpenTelemetry logging, etc can definitely improve a top-notch observability strategy. Specifically, this article goes deeper into these topics with rich information and real-life examples that readers can apply in their contexts to ensure systems are running as they should.

What are SLI Metrics?

Service Level Indicators (SLIs) – this is quite important as far as measuring a service’s performance and reliability according to the viewpoint of the end user is concerned. SLOs and SLAs are derived from it You may also see Key Performance Indicators or KPIs as an equivalent term The below are the categories of

SLI Metrics are aimed primarily at selected attributes of a service which include availability, latency measurements, throughput, and error rate. Through these metrics, organizations would be able to guarantee that their services provided are meeting the required standards of performance of its service delivery in a way that is user friendly.

Key SLI Metrics

  1. Availability: Cheaper form, counts the seconds that a service/feature is operational and compares it to total time making it expressed in percentage. For instance, an availability SLI might measure the percentage operating in the case of a web service.
  2. Latency: This enables tracking of the amount of time that is taken to process a request. Lower latency act as an advantage since clients want systems that are faster in rendering services as per their need.
  3. Throughput: Exposes the challenge of determining the capacity of a service based on the count of requests it deals with in a particular time frame. High throughput is an important aspect of a portable system as it effectively handles many users.
  4. Error Rate: Records the probability of the failed request. A lower error rate means the service will be more reliable and stable, which is beneficial to everyone.

Monitoring these SLI metrics allows organizations to identify performance bottlenecks and make informed decisions to improve their services.

What is Distributed Tracing?

What is Distributed tracing system? It is a method used to track request as it passes through different layers of the services present in a distributed environment. In the context of microservices architecture, it is important to note that one request of the user may require a number of microservices to be invoked, and as a result, analyzing the flow and performance of these requests is important.

Distributed tracing gives a detailed overview of the lifecycle of a request starting from its creation to the moment when the result is produced and the response is generated by one or several components of the complex. They can be used to determine poor performance, congestion or failure to deliver quality services in complex environment which involves numerous services.

How Distributed Tracing Works

Trace: A trace is representation of request from the time it has been received until the time it has been completed with support of several services. You see, it has spans of various types and it contains them in its framework.

Span: A span is a single unit of work in a trace, a full trace is a sequence of, or a set of, sub-traces which are spans. Every span is assigned a scientifically identifiable integer number and characterizes the specific operation performed, its duration, status, and any additional parameters thereof.

Context Propagation: LL011 Context propagation is used to ensure that as a request is handled by different services it records the trace and span data so that end-to-end reporting is possible.

Through the use of distributed tracing, various entities can comprehensively know the manner in which their system operates, readily locate problems, and modify their design for enhanced performance.

OpenTelemetry Logging Example

OpenTelemetry is an open-source project that provides the modern-day APIs and SDKs necessary to instrument, collect, and export volumetric telemetry data—metrics, traces, logs—in cloud-native systems. Instrumentation is scaled to the same level of difficulty when attempting to instrument code for observability, which lets the developers revert to improving their applications.

Setting Up OpenTelemetry Logging in Python

Let’s walk through an example of setting up OpenTelemetry logging in a Python application.

Install OpenTelemetry Libraries: Start by installing the necessary OpenTelemetry libraries.
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pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp

  1. Initialize OpenTelemetry :

Set up OpenTelemetry in your Python application.
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from opentelemetry import trace, metrics

from opentelemetry.sdk.trace import TracerProvider

from opentelemetry.sdk.metrics import MeterProvider

from opentelemetry.sdk.trace.export import BatchSpanProcessor, ConsoleSpanExporter

from opentelemetry.exporter.otlp.trace_exporter import OTLPSpanExporter

from opentelemetry.exporter.otlp.metrics_exporter import OTLPMetricExporter

# Set up TracerProvider


tracer = trace.get_tracer(__name__)

# Set up Span Processor and Exporter

span_processor = BatchSpanProcessor(ConsoleSpanExporter())


# Optionally, export spans to OTLP collector

otlp_exporter = OTLPSpanExporter(endpoint=”http://localhost:4317″, insecure=True)


# Set up MeterProvider


meter = metrics.get_meter(__name__)

  1. Instrument Your Code:

Use the OpenTelemetry APIs to instrument your code with tracing and logging.
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from opentelemetry.trace import Status, StatusCode

def some_function():

    with tracer.start_as_current_span(“some_function”) as span:

        span.set_attribute(“key”, “value”)


            # Simulate some work

            result = do_work()

            span.add_event(“Work done successfully”)

            return result

        except Exception as e:


            span.set_status(Status(StatusCode.ERROR, str(e)))


def do_work():

    # Simulate work

    return “work_result”

# Main execution

if __name__ == “__main__”:

    result = some_function()

    print(f”Result: {result}”)

  1. Run Your Application: Execute your application and observe the generated traces and logs in the console or an external observability platform.

Benefits of OpenTelemetry Logging

  • Unified Observability: OpenTelemetry provides a unified framework for collecting metrics, traces, and logs, simplifying the observability setup.
  • Vendor-Agnostic: It supports multiple backends and exporters, allowing you to choose the best observability platform for your needs.
  • Improved Debugging: With detailed traces and logs, you can quickly identify and resolve issues, reducing downtime and improving user experience.


Understanding and leveraging SLI metrics, distributed tracing, and opentelemetry logging example can significantly enhance your observability strategy, leading to more reliable and efficient systems. By focusing on these key areas, organizations can ensure they meet performance standards, quickly identify and resolve issues, and continuously improve their services.

Embrace these observability practices to gain deeper insights into your applications, optimize performance, and deliver a seamless user experience.

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