client_python
jaeger
client_python | jaeger | |
---|---|---|
15 | 94 | |
3,785 | 19,499 | |
1.3% | 1.1% | |
7.2 | 9.7 | |
8 days ago | 4 days ago | |
Python | Go | |
Apache License 2.0 | 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.
client_python
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Show HN: Hatchet – Open-source distributed task queue
Here you go: https://stackoverflow.com/questions/75652326/celery-spawn-si...
Plus some adjacent discussion on GitHub: https://github.com/prometheus/client_python/issues/902
Hope that helps!
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How to monitor Python application performance
Prometheus, which is also a CNCF open source project, collects metrics data by scraping HTTP endpoints and then stores that data in a time series database that uses a multidimensional model. It’s a powerful tool for gathering metrics about your application and it also includes alerting functionality that you can use to notify your teams when issues come up. Prometheus includes a client library for Python.
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Kafka-Python metric reporters
We have a java one but the principle is the same. Install the Prometheus client ( https://github.com/prometheus/client_python) ,create the metrics you want, then push jmx settings to Prometheus.
- Observabilidade com Prometheus
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Setup Grafana with Prometheus for Python projects using Docker
The code above is copied from the official documentation of prometheus_client which simply creates a new metric named request_processing_seconds that measures the time spent on that particular request. We'll cover other types of metrics later in this post.
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Prometheus histogram with python
Just use the client? https://github.com/prometheus/client_python
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Monitoring Latency with Python
I've experimented with the official Prometheus python client, i really really like the way they use decorators to instrument. I've tried to measure latency with multiple types of metrics (histogram, & summary), i see the value in both of them, but the one that between fits my objective is the histogram metric type. Great!
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Best way to handle several python script plugins for a service? Create an image + container for each one? Create one for them all? Running them as microservices?
Now is a good time to expand your event loop by adding metrics collection of the event handler functions and also use that endpoint as a liveness probe. E.g. https://github.com/prometheus/client_python just add the event handled, success/error and the duration as a histogram (look for examples of tracking http requests served)
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Why is Prometheus generating duplicate data (while using python client)?
I've spent along time trying to figure out a bug that I'm facing while using Prometheus from its python client.
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Python node exporter *Help
The official Prometheus Python client library makes this easy, no need to worry about the export file format.
jaeger
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Observability with OpenTelemetry, Jaeger and Rails
Jaeger maps the flow of requests and data as they traverse a distributed system. These requests may make calls to multiple services, which may introduce their own delays or errors. https://www.jaegertracing.io/
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Show HN: An open source performance monitoring tool
As engineers at past startups, we often had to debug slow queries, poor load times, inconsistent errors, etc... While tools like Jaegar [2] helped us inspect server-side performance, we had no way to tie user events to the traces we were inspecting. In other words, although we had an idea of what API route was slow, there wasn’t much visibility into the actual bottleneck.
This is where our performance product comes in: we’re rethinking a tracing/performance tool that focuses on bridging the gap between the client and server.
What’s unique about our approach is that we lean heavily into creating traces from the frontend. For example, if you’re using our Next.js SDK, we automatically connect browser HTTP requests with server-side code execution, all from the perspective of a user. We find this much more powerful because you can understand what part of your frontend codebase causes a given trace to occur. There’s an example here [3].
From an instrumentation perspective, we’ve built our SDKs on-top of OTel, so you can create custom spans to expand highlight-created traces in server routes that will transparently roll up into the flame graph you see in our UI. You can also send us raw OTel traces and manually set up the client-server connection if you want. [4] Here’s an example of what a trace looks like with a database integration using our Golang GORM SDK, triggered by a frontend GraphQL query [5] [6].
In terms of how it's built, we continue to rely heavily on ClickHouse as our time-series storage engine. Given that traces require that we also query based on an ID for specific groups of spans (more akin to an OLTP db), we’ve leveraged the power of CH materialized views to make these operations efficient (described here [7]).
To try it out, you can spin up the project with our self hosted docs [8] or use our cloud offering at app.highlight.io. The entire stack runs in docker via a compose file, including an OpenTelemetry collector for data ingestion. You’ll need to point your SDK to export data to it by setting the relevant OTLP endpoint configuration (ie. environment variable OTEL_EXPORTER_OTLP_LOGS_ENDPOINT [9]).
Overall, we’d really appreciate feedback on what we’re building here. We’re also all ears if anyone has opinions on what they’d like to see in a product like this!
[1] https://github.com/highlight/highlight/blob/main/LICENSE
[2] https://www.jaegertracing.io
[3] https://app.highlight.io/1383/sessions/COu90Th4Qc3PVYTXbx9Xe...
[4] https://www.highlight.io/docs/getting-started/native-opentel...
[5] https://static.highlight.io/assets/docs/gorm.png
[6] https://github.com/highlight/highlight/blob/1fc9487a676409f1...
[7] https://highlight.io/blog/clickhouse-materialized-views
[8] https://www.highlight.io/docs/getting-started/self-host/self...
[9] https://opentelemetry.io/docs/concepts/sdk-configuration/otl...
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Kubernetes Ingress Visibility
For the request following, something like jeager https://www.jaegertracing.io/, because you are talking more about tracing than necessarily logging. For just monitoring, https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack would be the starting point, then it depends. Nginx gives metrics out of the box, then you can pull in the dashboard like https://grafana.com/grafana/dashboards/14314-kubernetes-nginx-ingress-controller-nextgen-devops-nirvana/ , or full metal with something like service mesh monitoring which would provably fulfil most of the requirements
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Migrating to OpenTelemetry
Have you checked out Jaeger [1]? It is lightweight enough for a personal project, but featureful enough to really help "turn on the lightbulb" with other engineers to show them the difference between logging/monitoring and tracing.
[1] https://www.jaegertracing.io/
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The Road to GraphQL At Enterprise Scale
From the perspective of the realization of GraphQL infrastructure, the interesting direction is "Finding". How to find the problem? How to find the bottleneck of the system? Distributed Tracing System (DTS) will help answer this question. Distributed tracing is a method of observing requests as they propagate through distributed environments. In our scenario, we have dozens of subgraphs, gateway, and transport layer through which the request goes. We have several tools that can be used to detect the whole lifecycle of the request through the system, e.g. Jaeger, Zipkin or solutions that provided DTS as a part of the solution NewRelic.
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OpenTelemetry Exporters - Types and Configuration Steps
Jaeger is an open-source, distributed tracing system that monitors and troubleshoots the flow of requests through complex, microservices-based applications, providing a comprehensive view of system interactions.
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Fault Tolerance in Distributed Systems: Strategies and Case Studies
However, ensuring fault tolerance in distributed systems is not at all easy. These systems are complex, with multiple nodes or components working together. A failure in one node can cascade across the system if not addressed timely. Moreover, the inherently distributed nature of these systems can make it challenging to pinpoint the exact location and cause of fault - that is why modern systems rely heavily on distributed tracing solutions pioneered by Google Dapper and widely available now in Jaeger and OpenTracing. But still, understanding and implementing fault tolerance becomes not just about addressing the failure but predicting and mitigating potential risks before they escalate.
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Observability in Action Part 3: Enhancing Your Codebase with OpenTelemetry
In this article, we'll use HoneyComb.io as our tracing backend. While there are other tools in the market, some of which can be run on your local machine (e.g., Jaeger), I chose HoneyComb because of their complementary tools that offer improved monitoring of the service and insights into its behavior.
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Building for Failure
The best way to do this, is with the help of tracing tools such as paid tools such as Honeycomb, or your own instance of the open source Jaeger offering, or perhaps Encore's built in tracing system.
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Distributed Tracing and OpenTelemetry Guide
In this example, I will create 3 Node.js services (shipping, notification, and courier) using Amplication, add traces to all services, and show how to analyze trace data using Jaeger.
What are some alternatives?
prometheus-fastapi-instrumentator - Instrument your FastAPI with Prometheus metrics.
Sentry - Developer-first error tracking and performance monitoring
django-prometheus - Export Django monitoring metrics for Prometheus.io
skywalking - APM, Application Performance Monitoring System
netbox-plugin-prometheus-sd - Provide Prometheus url_sd compatible API Endpoint with data from Netbox
prometheus - The Prometheus monitoring system and time series database.
pushgateway - Push acceptor for ephemeral and batch jobs.
signoz - SigNoz is an open-source observability platform native to OpenTelemetry with logs, traces and metrics in a single application. An open-source alternative to DataDog, NewRelic, etc. 🔥 🖥. 👉 Open source Application Performance Monitoring (APM) & Observability tool
node_exporter - Exporter for machine metrics
Pinpoint - APM, (Application Performance Management) tool for large-scale distributed systems.
statsd_exporter - StatsD to Prometheus metrics exporter
fluent-bit - Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX and Windows