opentelemetry-collector-contrib
Grafana
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opentelemetry-collector-contrib | Grafana | |
---|---|---|
43 | 379 | |
2,546 | 60,395 | |
5.8% | 1.7% | |
10.0 | 10.0 | |
5 days ago | 3 days ago | |
Go | TypeScript | |
Apache License 2.0 | GNU Affero General Public License v3.0 |
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
Grafana
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Docker Log Observability: Analyzing Container Logs in HashiCorp Nomad with Vector, Loki, and Grafana
Monitoring application logs is a crucial aspect of the software development and deployment lifecycle. In this post, we'll delve into the process of observing logs generated by Docker container applications operating within HashiCorp Nomad. With the aid of Grafana, Vector, and Loki, we'll explore effective strategies for log analysis and visualization, enhancing visibility and troubleshooting capabilities within your Nomad environment.
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Golang: out-of-box backpressure handling with gRPC, proven by a Grafana dashboard
To help us visualize these scenarios, we'll build a Grafana Dashboard so we can follow along.
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Monitoring, Observability, and Telemetry Explained
Visualization and Analysis: Choose a tool with intuitive and customizable dashboards, charts, and visualizations. A question to ask is, "Are the visualization features of this tool user-friendly and adaptable to our team's specific needs?" Tools like Grafana and Kibana provide powerful visualization capabilities.
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4 facets of API monitoring you should implement
Prometheus: Open-source monitoring system. Often used together with Grafana.
- Grafana: Open and composable observability and data visualization platform
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The Mechanics of Silicon Valley Pump and Dump Schemes
Grafana
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Reverse engineering the Grafana API to get the data from a dashboard
Yes I'm aware that Grafana is open source but the method I used to find the API endpoints is far quicker than digging through hundreds of files in a codebase I'm not familiar with.
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Building an Observability Stack with Docker
So, you will add one last container to allow us to visualize this data: Grafana, an open-source analytics and visualization platform that allows us to see traces and metrics simply. You can set Grafana to read data from both Tempo and Prometheus by setting them as datastores with the following grafana.datasource.yaml config file:
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How to collect metrics from node.js applications in PM2 with exporting to Prometheus
In example above, we use 2 additional parameters: code (HTTP response code) and page (page identifier), which provide detailed statistics. For example, you can build such graphs in Grafana:
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Root Cause Chronicles: Quivering Queue
Robin switched to the Grafana dashboard tab, and sure enough, the 5xx volume on web service was rising. It had not hit the critical alert thresholds yet, but customers had already started noticing.
What are some alternatives?
uptrace - Open source APM: OpenTelemetry traces, metrics, and logs
Thingsboard - Open-source IoT Platform - Device management, data collection, processing and visualization.
cockpit-podman - Cockpit UI for podman containers
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
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
Heimdall - An Application dashboard and launcher
podman-compose - a script to run docker-compose.yml using podman
Wazuh - Wazuh - The Open Source Security Platform. Unified XDR and SIEM protection for endpoints and cloud workloads.
traefik - The Cloud Native Application Proxy
Thingspeak - ThingSpeak is an open source “Internet of Things” application and API to store and retrieve data from things using HTTP over the Internet or via a Local Area Network. With ThingSpeak, you can create sensor logging applications, location tracking applications, and a social network of things with status updates.
serilog-sinks-seq - A Serilog sink that writes events to the Seq structured log server
uptime-kuma - A fancy self-hosted monitoring tool