opentelemetry-examples
opentelemetry-python-contrib
opentelemetry-examples | opentelemetry-python-contrib | |
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8 | 3 | |
56 | 613 | |
- | 4.1% | |
6.6 | 9.4 | |
about 2 months ago | 6 days ago | |
JavaScript | Python | |
- | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
opentelemetry-examples
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KubeCon + CloudNativeCon Europe 2023: Highlights from Amsterdam
We focused on the observability ecosystem and took the time to interact with our friends from Lightstep, New Relic, Honeycomb, Dynatrace, Instana, and many more. With that in mind, keep an eye out for more integrations coming to Tracetest!
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Top 9 Commercial Distributed Tracing Tools
Lightstep bills itself as a platform for the reliability of cloud-native applications. The people behind Lightstep co-founded OpenTelemetry and OpenTracing, which gives them a unique perspective on the use cases of distributed tracing and the value of having a vendor-neutral tracing data format.
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Observability - Types Of Vendor Pricing Models
In the last 5 to 10 years, new Observability vendors have entered the market, including Honeycomb, Instana, Lightstep and Datadog. Similarly, traditional APM vendors such as Dynatrace, AppDynamics, and New Relic, as well as SIEM (and log management) vendors such as Splunk and Sumo Logic, have joined them in the Observability space too. Finally you also have major cloud providers such as AWS with their own observability solution. Each of them is attempting to address the observability issues that modern architecture presents by using Logs, Metrics, Traces, and Events.
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KubeCon North America 2022: A Retrospective
I spent Day 2 at the Colony Club to attend OTel Unplugged. This event was sponsored by Lightstep, Honeycomb, New Relic, Splunk, Dynatrace, Crowdstrike, and NGINX. I came into the event not knowing what to expect. I can sometimes clamp up when I’m around folks that I don’t know, but because I was helping with the event check-in, I got to say hello to a number of the attendees, which helped break the ice. And it turns out that there were a lot of names that I recognized from my work in the OTel community, and it was nice to connect in person with folks whom I’d only previously met through Slack or Zoom.
- Grafana Phlare, open source database for continuous profiling at scale
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OpenTelemetry for Python: The Hard Way
The example in this tutorial can be found in the lightstep/opentelemetry-examples repo. We will be working with three main files:
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Auto-Instrumentation Is Magic: Using OpenTelemetry Python with Lightstep
Note how we don’t have to set a LS_ACCESS_TOKEN, since that’s already configured in the Collector’s config.yml file. Just make sure that you have a running OTel Collector instance!
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Observability Mythbusters: OpenTelemetry to Lightstep 3 Ways in Go IS Possible!
Note: If you’re looking for full code listings, don’t panic! You see them in the Lightstep OTel examples repository.
opentelemetry-python-contrib
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OpenTelemetry for Python: The Hard Way
In my last blog post, I showed y’all how to instrument Python code with OpenTelemetry (OTel), à la auto-instrumentation. You may also recall from that post that I recommended using the Python auto-instrumentation binary even for non-auto-instrumented libraries, because it abstracts all that pesky OTel config stuff so nicely. When you use it, along with any applicable Python auto-instrumentation libraries (installed courtesy of opentelemetry-bootstrap), it takes care of context propagation across related services for you.
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Auto-Instrumentation Is Magic: Using OpenTelemetry Python with Lightstep
More specifically, auto-instrumentation uses shims or bytecode instrumentation agents to intercept your code at runtime or at compile-time to add tracing and metrics instrumentation to the libraries and frameworks you depend on. The beauty of auto-instrumentation is that it requires a minimum amount of effort. Sit back, relax, and enjoy the show. A number of popular Python libraries are auto-instrumented, including Flask and Django. You can find the full list here.
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Do i really want to mess with OpenTelemetry, or just hook straight into Datadog
And sure, there's gaps and those are awful when you get to them. But writing minimal tracing integration is pretty easy. This is the full source of the psycopg2 instrumentation. https://github.com/open-telemetry/opentelemetry-python-contrib/blob/main/instrumentation/opentelemetry-instrumentation-psycopg2/src/opentelemetry/instrumentation/psycopg2/__init__.py
What are some alternatives?
magic-trace - magic-trace collects and displays high-resolution traces of what a process is doing
vector - A high-performance observability data pipeline.
examples - Example apps and instrumentation for Honeycomb
debug-toolkit - A modern code-injection framework for Python. Like Pyrasite but Kubernetes-aware.
opentelemetry-python - OpenTelemetry Python API and SDK
sig-release - Repo for SIG release
opentelemetry.io - The OpenTelemetry website and documentation
aws-otel-js - AWS Distro for OpenTelemetry JavaScript SDK
thanos - Highly available Prometheus setup with long term storage capabilities. A CNCF Incubating project.
tracetest - 🔭 Tracetest - Build integration and end-to-end tests in minutes, instead of days, using OpenTelemetry and trace-based testing.
opentelemetry-specification - Specifications for OpenTelemetry