# jaeger Published articles for jaeger. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Building a Unified Observability Platform on GreptimeDB: A Reference Architecture DevFeed: [Building a Unified Observability Platform on GreptimeDB: A Reference Architecture]() Original publisher: [Read original article]() Author: Greptime Published: 2026-10-05T20:11:01Z Content type: article Language: en Sources: [DevOps.dev]() Topics: [observability pipelines](), [OpenTelemetry](), [Grafana](), [Prometheus](), [table-partitioning]() Tags: [agent](), [alerts](), [analysis](), [architecture](), [article](), [astronomy](), [cluster](), [collectors](), [components](), [configuration](), [demo](), [deploy](), [devops](), [enterprise](), [incident](), [investigation](), [jaeger](), [large](), [observability](), [opentelemetry](), [opentelemetry-demo](), [platform](), [reference-architecture](), [tutorial]() ### AI overview The architecture routes metrics, logs, and traces into one GreptimeDB while leaving collectors in place: dashboards and alerts use PromQL, and investigation and long-range analysis use SQL. The excerpt describes protocol endpoints, per-signal table and indexing choices, and three deployment tiers. Flow can maintain small aggregate tables for dashboards and alerts; frontend groups isolate query handling, while storage isolation through Read Replicas or Datanode groups is Enterprise-only. Object storage supports longer retention; Local WAL suits standalone and small clusters, while Remote WAL uses Kafka for faster recovery. Migration is described as dual-write, switch reads, then retire the old system. Compatibility has limits: Loki writes are supported but LogQL queries move to SQL; open-source Elasticsearch support covers bulk writes, not Query DSL. The article also reports better agent-diagnosis results on GreptimeDB, while cautioning that its comparison changed the full interface bundle and does not isolate the storage engine's contribution. ### Source excerpt This article assumes you have already decided to put metrics, logs, and traces into one database. Why unify, and why now, is covered in our earlier post on the history of observability unification. This one covers what comes after that decision: which components to deploy, how data flows, which decisions are still yours to make, and what the topology looks like at three scales: standalone, small cluster, and large cluster. The architecture fits in one sentence: leave the collectors alone and point them at one GreptimeDB; dashboards and alerts go through PromQL, incident investigation and long-range analysis through SQL. The validation environment is the official OpenTelemetry Astronomy Shop demo: 14 services, SDKs in several languages, and a default backend of Jaeger, OpenSearch, and Prometheus. We replaced those three with a single GreptimeDB v1.2.0 standalone instance, plus a node_exporter and a Prometheus that only scrapes. The configuration, table schemas, query results, and incident records in this article come from that environment; the complete setup is in the greptimedb-observability-playground repository. Enterprise-only features are marked where they come up. Three workloads the platform has to carry Dashboards and alerts. Grafana panels refresh every few seconds and alert rules evaluate every minute, over the last few minutes to few hours of data. Many queries, each small, each latency-sensitive. Incident investigation. One investigation moves between metrics, logs, and traces: from an alert to the slow service, from the service to the slow call chain, from the call chain to the error logs and node resources of that time window. The queries are exploratory; nobody knows in advance how many there will be, and humans are no longer the only ones issuing them. In our Agent RCA Bench, one agent investigation averaged thirty to forty tool calls, with some approaching fifty. Long-range analysis. Capacity planning, weekly reports, cost attribution: scans over wee ## Uploading a large OpenTelemetry trace to a local OTLP/HTTP traces receiver DevFeed: [Uploading a large OpenTelemetry trace to a local OTLP/HTTP traces receiver]() Original publisher: [Read original article]() Author: Jamie Tanna Published: 2026-09-30T17:33:46Z Content type: tutorial Language: en Sources: [Jamie Tanna]() Topics: [OpenTelemetry](), [cURL](), [HTTP]() Tags: [blogumentation](), [command-line](), [curl](), [jaeger](), [opentelemetry](), [traces]() ### AI overview The author describes investigating Renovate performance on large repositories using OpenTelemetry traces, which reach roughly 30 MB. Because HTTP size limits blocked ingestion into a local Jaeger instance, they adjusted a shell loop to send each JSONL line to the local OTLP/HTTP receiver through stdin with curl. ### Source excerpt 🤖 This post includes some LLM-derived content 🤖 I've recently been doing some work with improving the performance of Renovate on larger repositories. To guide this investigation, I'm leveraging our OpenTelemetry support to more meaningfully understand how Renovate operates in parts of the codebase, and hone in on areas of improvement. One of the repositories I'm running these tests is particularly large, and leads to traces of roughly ~30MB in size. When trying to ingest them into my local Jaeger instance, I hit HTTP size limits, and the documentation I'd written in the past (with help from Claude Sonnet 5) for ingesting these doesn't seem to handle the size limits. With a bit of tweaking with Claude Sonnet 5, this is now better handled with: while IFS= read -r line || [ -n "$line" ]; do curl -s -X POST http://localhost:4318/v1/traces \ -H 'Content-Type: application/json' \ --data-binary @- <<< "$line" done < /tmp/traces.jsonl Notice that we redirect (via a herestring) the line's contents in via stdin, instead of passing it as an argument, which is what led to the shell crashing. With this few-liner this leads to us being able to ingest very large traces! ## OpenTelemetry Collection DevFeed: [OpenTelemetry Collection]() Original publisher: [Read original article]() Author: Ivan Burazin CEO; Co-Founder Published: 2026-05-11T00:00:00Z Content type: article Language: en Sources: [Dotfiles Insider]() Topics: [OpenTelemetry](), [telemetry](), [observability pipelines]() Tags: [browser-based-ide](), [browser-ide](), [cloud-coding](), [cloud-dev-environment](), [cloud-development-environment](), [cloud-editor](), [cloud-ide](), [cloud-programming](), [code-in-cloud](), [code-on-web](), [coding-environment](), [daytona](), [grafana-cloud](), [ide-editor-online](), [ide-in-browser](), [ide-online](), [jaeger](), [logs](), [metrics](), [observability](), [online-code-editor](), [online-ide](), [online-programming](), [online-web-dev-ide](), [online-web-development-ide](), [opentelemetry](), [pipelines](), [sde](), [standardised-development-environment](), [stories-insights](), [traces](), [web-based-coding](), [web-ide]() ### AI overview The article describes Daytona's OpenTelemetry collection for sandbox runtime telemetry and SDK traces. It covers exporting logs, metrics, and traces to OTLP-compatible backends, plus organization-level resource metrics for capacity planning and quota reviews. ### Source excerpt Collect distributed traces, logs, and metrics from Daytona sandboxes with OpenTelemetry support. ## OpenTelemetry, Kubernetes, and Fir: Putting it All Together DevFeed: [OpenTelemetry, Kubernetes, and Fir: Putting it All Together]() Original publisher: [Read original article]() Author: Ken W. Alger Published: 2025-07-09T15:00:13Z Content type: article Language: en Sources: [Heroku]() Topics: [OpenTelemetry](), [observability](), [Heroku](), [Microservices](), [Cloud Architecture](), [Monitoring]() Tags: [application](), [applications](), [at-the-core](), [automated](), [backends](), [buildpacks](), [cloud-infrastructure](), [cloud-native](), [cloud-platform](), [collector](), [datadog](), [distributed](), [distributed-systems](), [engineering](), [fir](), [framework](), [grafana](), [heroku](), [jaeger](), [kubernetes](), [observability](), [opentelemetry](), [performance-optimization](), [telemetry]() ### AI overview Fir uses Kubernetes for scaling, self-healing, and resource management while presenting users with Heroku's interface instead of exposing Kubernetes directly. OpenTelemetry provides a standard approach to collecting and exporting telemetry across distributed systems; the excerpt says it can send data to backends such as Prometheus, Jaeger, Grafana, Datadog, or Heroku without changing application code. With Fir, developers can trace distributed requests, inspect application performance, and route telemetry to external monitoring platforms. The provided text ends partway through its explanation of OpenTelemetry's components, so further implementation details are not available. ### Source excerpt Modern cloud-native architectures are composed of multiple microservices running across dynamic environments. Effectively diagnosing performance issues, bottlenecks, or failures requires comprehensive observability. For this, many organizations look to OpenTelemetry, which provides a standardized approach to capturing and analyzing telemetry data. Fir is Heroku's next generation cloud platform, designed to offer more modern cloud-native capabilities with [...] The post OpenTelemetry, Kubernetes, and Fir: Putting it All Together appeared first on Heroku. ## New Chainguard Images October 2024: Swift, Dart, and more DevFeed: [New Chainguard Images October 2024: Swift, Dart, and more]() Original publisher: [Read original article]() Published: 2024-11-14T00:00:00Z Content type: release Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard images](), [container images](), [Swift](), [Dart]() Tags: [chainguard](), [chainguard-images](), [container-images](), [cve](), [dart](), [fips](), [jaeger](), [linkerd](), [swift](), [zero-cves]() ### AI overview Chainguard's October 2024 update announces 126 new CVE-free container images, including offerings for Swift, Dart, Linkerd, and Jaeger, along with new FIPS-enabled images and a notice about upcoming Developer tier changes. ### Source excerpt Go over all the new Chainguard container images released in October, including new offerings for Swift, Dart, Linkerd, Jaeger, and more. ## Logs and tracing: not just for production, local development too DevFeed: [Logs and tracing: not just for production, local development too]() Original publisher: [Read original article]() Author: Lawrence Jones Published: 2021-11-11T00:00:00Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [tracing](), [observability](), [Google](), [GraphQL]() Tags: [api](), [cloud](), [debugging](), [developer](), [development](), [engineering](), [google](), [graphql](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [integration](), [jaeger](), [local](), [logging](), [logs](), [observability](), [outage](), [post-mortem](), [production](), [slack-incident](), [stackdriver](), [traces](), [tracing]() ### AI overview This article explains how a small engineering team built an observability setup for both production and local development. It focuses on sending distributed traces from a Go application to Google Cloud Trace, then integrating Stackdriver logs with trace and span IDs so developers can inspect contextual log messages alongside traces. ### Source excerpt For a small initial investment, we've found a observability setup that works great for both production and local development. You should try it, too!