opentelemetry-collector-contrib
prometheus
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opentelemetry-collector-contrib | prometheus | |
---|---|---|
43 | 381 | |
2,546 | 52,748 | |
5.8% | 1.6% | |
10.0 | 9.9 | |
5 days ago | 5 days ago | |
Go | 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.
opentelemetry-collector-contrib
- OpenTelemetry at Scale: what buffer we can use at the behind to buffer the data?
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All you need is Wide Events, not "Metrics, Logs and Traces"
The open telemetry collector does just that. https://github.com/open-telemetry/opentelemetry-collector-co...
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OpenTelemetry Collector Anti-Patterns
There are two official distributions of the OpenTelemetry Collector: Core, and Contrib.
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OpenTelemetry Journey #00 - Introduction to OpenTelemetry
Maybe, you are asking yourself: "But I already had instrumented my applications with vendor-specific libraries and I'm using their agents and monitoring tools, why should I change to OpenTelemetry?". The answer is: maybe you're right and I don't want to encourage you to update the way how you are doing observability in your applications, that's a hard and complex task. But, if you are starting from scratch or you are not happy with your current observability infrastructure, OpenTelemetry is the best choice, independently of the backend telemetry tool that you are using. I would like to invite you to take a look at the number of exporters available in the collector contrib section, if your backend tracing tool is not there, probably it's already using the Open Telemetry Protocol (OTLP) and you will be able to use the core collector. Otherwise, you should consider changing your backend telemetry tool or contributing to the project creating a new exporter.
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Building an Observability Stack with Docker
To receive OTLP data, you set up the standard otlp receiver to receive data in HTTP or gRPC format. To forward traces and metrics, a batch processor was defined to accumulate data and send it every 100 milliseconds. Then set up a connection to Tempo (in otlp/tempo exporter, with a standard top exporter) and to Prometheus (in prometheus exporter, with a control exporter). A debug exporter also was added to log info on container standard I/O and see how the collector is working.
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Spotlight: Sentry for Development
Thanks for the reply. Would the Spotlight sidecar possibly be able to run independently and consume spans emitted by the Sentry exporter[0] or some other similar flow beyond strictly exporting directly from the Sentry SDK provided by Spotlight?
This tooling looks really cool and I'd love to play around with it, but am already pretty entrenched into OTel and funneling data through the collector and don't want to introduce too much additional overhead for devs.
[0] https://github.com/open-telemetry/opentelemetry-collector-co...
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Amazon EKS Monitoring with OpenTelemetry [Step By Step Guide]
A list of all metric definitions can be found here.
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Spring Boot Monitoring with Open-Source Tools
receivers: otlp: protocols: grpc: endpoint: 0.0.0.0:4317 http: endpoint: 0.0.0.0:4318 hostmetrics: collection_interval: 60s scrapers: cpu: {} disk: {} load: {} filesystem: {} memory: {} network: {} paging: {} process: mute_process_name_error: true mute_process_exe_error: true mute_process_io_error: true processes: {} prometheus: config: global: scrape_interval: 60s scrape_configs: - job_name: otel-collector-binary scrape_interval: 60s static_configs: - targets: ["localhost:8889>"] - job_name: "jvm-metrics" scrape_interval: 10s metrics_path: "/actuator/prometheus" static_configs: - targets: ["localhost:8090>"] processors: batch: send_batch_size: 1000 timeout: 10s # Ref: https://github.com/open-telemetry/opentelemetry-collector-contrib/blob/main/processor/resourcedetectionprocessor/README.md resourcedetection: detectors: [env, system] # Before system detector, include ec2 for AWS, gcp for GCP and azure for Azure. # Using OTEL_RESOURCE_ATTRIBUTES envvar, env detector adds custom labels. timeout: 2s system: hostname_sources: [os] # alternatively, use [dns,os] for setting FQDN as host.name and os as fallback extensions: health_check: {} zpages: {} exporters: otlp: endpoint: "ingest.{region}.signoz.cloud:443" tls: insecure: false headers: "signoz-access-token": logging: verbosity: normal service: telemetry: metrics: address: 0.0.0.0:8888 extensions: [health_check, zpages] pipelines: metrics: receivers: [otlp] processors: [batch] exporters: [otlp] metrics/internal: receivers: [prometheus, hostmetrics] processors: [resourcedetection, batch] exporters: [otlp] traces: receivers: [otlp] processors: [batch] exporters: [otlp] logs: receivers: [otlp] processors: [batch] exporters: [otlp]
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Migrating to OpenTelemetry
If you are using the prometheus exporter, you can use the transform processor to get specific resource attributes into metric labels.
With the advantage that you get only the specific attributes you want, thus avoiding a cardinality explosion.
https://github.com/open-telemetry/opentelemetry-collector-co...
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Exploring the OpenTelemetry Collector
OpenTelemetry Operators
prometheus
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Fivefold Slower Compared to Go? Optimizing Rust's Protobuf Decoding Performance
WriteRequest::timeseries is a vector (https://github.com/prometheus/prometheus/blob/main/prompb/re...) and
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Tools for frontend monitoring with Prometheus
Developers widely use Prometheus as a system for operational monitoring and alerting for their projects. Here is a list of tools for monitoring frontend services with Prometheus.
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The power of the CLI with Golang and Cobra CLI
Just to give an example of the power of Go for CLI builds, you may have already used or at least heard of Docker, Kubernetes, Prometheus, Terraform, but what do they all have in common? They all have a large part of their usability via CLI and are developed in Go 🐿.
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On Implementation of Distributed Protocols
Distributed system administrators need mechanisms and tools for monitoring individual nodes in order to analyze the system and promptly detect anomalies. Developers also need effective mechanisms for analyzing, diagnosing issues, and identifying bugs in protocol implementations. Logging, tracing, and collecting metrics are common observability techniques to allow monitoring and obtaining diagnostic information from the system; most of the explored code bases use these techniques. OpenTelemetry and Prometheus are popular open-source monitoring solutions, which are used in many of the explored code bases.
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Golang: out-of-box backpressure handling with gRPC, proven by a Grafana dashboard
Setting up monitoring for a system, especially one involving GRPC communication, provides crucial visibility into its operations. In this guide, we walked through the steps to instrument both a GRPC server and client with Prometheus metrics, exposed those metrics via an HTTP endpoint, and visualized them using Grafana. The Docker-Compose setup simplified the deployment of both Prometheus and Grafana, ensuring a streamlined process.
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Monitoring, Observability, and Telemetry Explained
Alerting and Notification: Select a tool with flexible alerting mechanisms to proactively detect anomalies or deviations from defined thresholds. Consider asking questions like "Does this tool offer customizable alerting options and support notification channels that suit our team's communication preferences?" A tool like Prometheus provides robust alerting capabilities.
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Observability at KubeCon + CloudNativeCon Europe 2024 in Paris
Prometheus
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Top 5 Docker Container Monitoring Tools in 2024
Prometheus is an open-source monitoring and alerting toolkit. It is designed to monitor highly dynamic containerized systems, making it an excellent choice for monitoring Docker containers and Kubernetes clusters.
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Install and Setup Grafana & Prometheus on Ubuntu 20.04 | 22.04/EC2
wget https://github.com/prometheus/prometheus/releases/download/v2.46.0/prometheus-2.46.0.linux-amd64.tar.gz
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4 facets of API monitoring you should implement
Prometheus: Open-source monitoring system. Often used together with Grafana.
What are some alternatives?
uptrace - Open source APM: OpenTelemetry traces, metrics, and logs
metrics-server - Scalable and efficient source of container resource metrics for Kubernetes built-in autoscaling pipelines.
cockpit-podman - Cockpit UI for podman containers
skywalking - APM, Application Performance Monitoring System
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
Jolokia - JMX on Capsaicin
podman-compose - a script to run docker-compose.yml using podman
Telegraf - The plugin-driven server agent for collecting & reporting metrics.
traefik - The Cloud Native Application Proxy
JavaMelody - JavaMelody : monitoring of JavaEE applications
serilog-sinks-seq - A Serilog sink that writes events to the Seq structured log server
Glowroot - Easy to use, very low overhead, Java APM