# Grafana Cloud Metrics Fully managed metrics service in Grafana Cloud for visualizing metric data, alerting, and troubleshooting issues. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## v20.2.3 Tentacle released DevFeed: [v20.2.3 Tentacle released]() Original publisher: [Read original article]() Author: Yuri Weinstein Published: 2026-08-05T00:00:00Z Content type: release Language: en Sources: [Ceph Blog]() Topics: [Deployment](), [Server](), [Hardware](), [TLS (Transport Layer Security)](), [Kafka](), [Grafana Cloud Metrics](), [Monitoring](), [Command-line interface](), [IO]() Tags: [2026](), [authentication](), [blog-post](), [cli](), [en-article](), [en-blog-post](), [hardware](), [io](), [kafka](), [metrics](), [monitoring](), [release](), [storage](), [tentacle](), [tls]() ### AI overview Ceph v20.2.3, the third minor release in the Tentacle series, is a recommended update containing fixes and improvements across metadata servers, object storage daemons, RADOS Gateway, client libraries, the dashboard, ceph-volume, and NVMe-oF. Changes include storage reliability fixes, TLS 1.3 cipher customization, encrypted object copying, Kafka notification authentication, hardware monitoring, CSV host uploads, and role-management updates. ### Source excerpt This is the third minor release in the Tentacle series. We recommend that all users update to this release. Release Date ¶ August 5, 2026 Notable Changes ¶ MDS (Metadata Server) ¶ Fixed a crash and shutdown hang that could occur when ephemeral pins were active and max_mds was set to 0. Session reclaim could miss blocklisting an old session; this is now handled correctly. Fixed a case where scrub was unable to identify damage from an irreparable hard link. OSD (Object Storage Daemon) ¶ BlueStore: Fixed a BlueFS WAL envelope-mode bug that caused write buffer misalignment. Erasure Coding: Fixed truncate+write planning for EC shard sizes. Fixed a condition that prevented rolling forward of PG log entries. Prevented OSDMap::check_health() from asserting when new OSDs are found in a subtree. Scrub: "repairing" scrubs -- deep scrubs re-run specifically to fix a previously detected inconsistency -- are no longer blocked by the configured allowed scrub hours/days; they can now run at any time. RGW (RADOS Gateway) ¶ Added the ssl_ciphersuites option for the Beast front-end to support TLS 1.3 cipher customization. Fixed PutObject's canned_acl comparison bug affecting BlockPublicAcls. Fixed crashes occurring on realm reload. Implemented CopyObject support for encrypted objects. Added mTLS client-certificate authentication support for Kafka notifications. Fixed removal of delete markers in lifecycle processing. RADOS / librados / neorados ¶ neorados: Fixed an infinite trim loop on empty data log shards. Fixed cancellation-slot cleanup in librados/asio's associated executor. Dashboard ¶ Added a hardware-monitoring dashboard backed by node-proxy metrics. Fixed a bind-address regression caused by CherryPy isolation. Added support for adding hosts via CSV upload. NVMeoF: multiple CLI and UI backports (EC pool support, IO statistics, role management). Object: The global RGW Roles tab has been removed. Role management is now scoped under RGW Accounts, where roles can be listed, created, ## Building a Custom Metrics Exporter for Kubernetes DevFeed: [Building a Custom Metrics Exporter for Kubernetes]() Original publisher: [Read original article]() Author: Victor David Effiok Published: 2026-07-14T18:00:00Z Content type: tutorial Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [autoscaling](), [Grafana Cloud Metrics](), [Time Series](), [Go Language](), [WebSocket]() Tags: [autoscaling](), [building](), [code](), [go](), [http](), [kubernetes](), [metrics](), [prometheus](), [server](), [time-series]() ### AI overview A tutorial on building a custom Kubernetes metrics exporter that exposes application or external state for Prometheus, enabling queries, alerts, and HorizontalPodAutoscaler scaling. It covers exporter architecture, direct instrumentation, metric formatting, and choosing counters, gauges, or histograms. ### Source excerpt Kubernetes ships with built-in awareness of CPU and memory, but most real-world scaling decisions depend on signals that live entirely outside that narrow window: how many messages are waiting in a queue, how long the last batch job took, how many active WebSocket connections a pod is holding. When the built-in metrics are not enough, a metrics exporter bridges that gap. This post walks through writing one from scratch, packaging it as a container, and wiring it into a cluster so that Prometheus -- and ultimately the HorizontalPodAutoscaler -- can consume it. What a metrics exporter actually does An exporter is a small HTTP server with a single responsibility: expose application state as text on a /metrics endpoint. Prometheus scrapes that endpoint on a regular interval, stores the time-series data, and makes it available for queries, alerts, and autoscaling rules. In some cases you can instrument your application directly -- embedding the Prometheus client library and exposing /metrics from within the same process -- rather than running a separate exporter. A standalone exporter makes more sense when the data source is external to your application or when you do not control the application code. The format Prometheus expects is plain text -- one metric per line, with a name, optional labels, and a numeric value. Client libraries handle the serialization for you, so in practice you only need to decide what to measure and call the right function when that value changes. Choosing what to measure Before writing any code, it helps to decide what kind of signal you are dealing with. The Prometheus data model has three main types: Counters only ever increase. They are the right tool for totals: requests served, jobs processed, errors encountered. Never use a counter for a value that can go down. Gauges represent a current snapshot of a value that can rise and fall freely. Queue depth, active connections, and cache size are all gauges. Histograms record the distribution of obse ## Introducing AI observability notebooks for Managed ClickStack in Private Preview DevFeed: [Introducing AI observability notebooks for Managed ClickStack in Private Preview]() Original publisher: [Read original article]() Author: Mike Shi Published: 2026-03-18T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [ai observability](), [observability](), [Claude](), [clickhouse](), [log management](), [Grafana Cloud Metrics](), [Traces](), [anthropic]() Tags: [ai](), [ai-observability](), [anthropic](), [clickhouse](), [logs](), [metrics](), [observability](), [production](), [sre](), [traces]() ### AI overview ClickStack AI Notebooks bring AI-assisted investigation to Managed ClickStack in ClickHouse Cloud. Engineers can investigate logs, metrics, and traces in a persistent, structured workspace, using natural-language questions, editable ClickHouse queries, intermediate reasoning, and branching investigations while retaining control of the workflow. ### Source excerpt AI-native observability notebooks for Managed ClickStack let engineers investigate logs, metrics, and traces with AI as a collaborator -- keeping humans in control at every step. ## Monitoring Temporal Cloud with ClickStack DevFeed: [Monitoring Temporal Cloud with ClickStack]() Original publisher: [Read original article]() Author: Dale McDiarmid Published: 2026-01-28T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [clickhouse](), [observability](), [Monitoring](), [Grafana Cloud Metrics](), [dashboards](), [telemetry](), [OpenTelemetry](), [site-reliability-engineering]() Tags: [announcements](), [clickhouse](), [dashboards](), [logs](), [metrics](), [monitoring](), [observability](), [opentelemetry](), [traces]() ### AI overview Temporal announces an integration between ClickStack and Temporal Cloud's OpenMetrics endpoint. The article explains how ClickStack, built on ClickHouse, can monitor workflow success rates, latency, Actions consumption, and Task Queue backlogs through dashboards, alerts, and correlation across logs, metrics, and traces. ### Source excerpt Monitor Temporal Cloud in ClickStack via the OpenMetrics endpoint. See Workflow success rates, latency, Actions consumption, and Task Queue backlogs in ClickHouse dashboards, with alerts and correlation across logs, metrics, and traces. ## Monitoriza Linux con Prometheus y Grafana DevFeed: [Monitoriza Linux con Prometheus y Grafana]() Original publisher: [Read original article]() Author: Antonio Pérez Published: 2026-01-25T00:00:00Z Content type: tutorial Language: es Sources: [tengoping.com]() Topics: [Grafana](), [Grafana Cloud Metrics](), [Linux](), [systemd]() Tags: [dashboards](), [grafana](), [linux](), [monitor](), [prometheus](), [systemd]() ### AI overview A step-by-step guide to deploying a Prometheus and Grafana monitoring stack on Linux servers with Node Exporter. It covers collecting system metrics, configuring Prometheus targets, connecting Grafana, importing a dashboard, and using PromQL for gauges and counters. ### Source excerpt Guía paso a paso para desplegar un stack de monitorización con Prometheus y Grafana en servidores Linux usando Node Exporter. ## Inside Laravel Nightwatch's Observability Pipeline: Real-Time Event Processing with Amazon MSK and ClickHouse Cloud DevFeed: [Inside Laravel Nightwatch's Observability Pipeline: Real-Time Event Processing with Amazon MSK and ClickHouse Cloud]() Original publisher: [Read original article]() Author: AWS; Laravel; ClickHouse Published: 2025-10-08T15:36:20Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [Laravel](), [observability](), [clickhouse](), [Kafka](), [Streaming](), [telemetry](), [AWS Lambda](), [Amazon RDS](), [PostgreSQL](), [Amazon API Gateway](), [Cloud](), [Grafana Cloud Metrics]() Tags: [amazon-api-gateway](), [amazon-rds](), [apache-kafka](), [api](), [aws-lambda](), [clickhouse](), [dashboards](), [json](), [laravel](), [low-latency](), [metrics](), [monitoring](), [observability](), [rds-for-postgresql](), [streaming](), [traces]() ### AI overview This article explains how Laravel Nightwatch processes more than one billion observability events per day using AWS managed services and ClickHouse Cloud. Its streaming-first architecture combines Amazon MSK, AWS Lambda, Amazon API Gateway, Amazon RDS for PostgreSQL, and ClickHouse Cloud to provide scalable ingestion, real-time dashboards, and sub-second analytical queries. ### Source excerpt Learn how Laravel Nightwatch platform process billions of observability events in real time while keeping query latency under a second. ## What's New in Apache Pulsar 4.1 DevFeed: [What's New in Apache Pulsar 4.1]() Original publisher: [Read original article]() Author: David Kjerrumgaard Published: 2025-09-09T00:00:00Z Content type: release Language: en Sources: [Apache Pulsar Blog]() Topics: [Apache Pulsar](), [SDKs](), [Security](), [Grafana Cloud Metrics](), [Resilience](), [WebSocket]() Tags: [announce](), [apache](), [api](), [authentication](), [bug-fixes](), [cli](), [configuration](), [encryption](), [java](), [launch](), [monitoring](), [new-features](), [observability](), [performance](), [release](), [resilience](), [schema](), [sdk](), [security](), [websocket]() ### AI overview Apache Pulsar 4.1 is a feature release with 19 approved Pulsar Improvement Proposals, bug fixes, security updates, and performance improvements. Highlights include stronger WebSocket token security, enhanced encryption handling, client configuration and resilience improvements, third-party schema registry support, Java 17 as the minimum for the Java client SDK, improved storage management, monitoring, and topic policies. ### Source excerpt The Apache Pulsar community is thrilled to announce the launch of Apache Pulsar 4.1, a significant feature release! ## Prometheus metrics with Rails and Kamal DevFeed: [Prometheus metrics with Rails and Kamal]() Original publisher: [Read original article]() Author: Lewis Buckley Published: 2024-05-23T17:00:00Z Content type: tutorial Language: en Sources: [37signals Dev]() Topics: [Prometheus](), [Rails](), [telemetry](), [Grafana Cloud Metrics](), [like](), [.NET 9]() Tags: [docker](), [metrics](), [performance](), [processes](), [prometheus](), [rails](), [redis](), [ruby]() ### AI overview This article explains how 37signals exports and aggregates Prometheus metrics from Rails applications running in Docker containers managed by Kamal. It describes using Yabeda and addresses performance issues and process-ID cardinality in multiprocess metric collection. ### Source excerpt How we use Prometheus metrics with Rails and Kamal. ## Using Cloud Monitoring to monitor App Check and Security Rules DevFeed: [Using Cloud Monitoring to monitor App Check and Security Rules]() Original publisher: [Read original article]() Author: Shruti Padamata Published: 2022-12-05T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Monitoring](), [Grafana Cloud Metrics](), [Security](), [Authorization](), [dashboards]() Tags: [app-check](), [cloud](), [cloud-monitoring](), [dashboards](), [firebase](), [firestore](), [incident](), [metrics](), [notify](), [security](), [security-rules]() ### AI overview A tutorial on using Cloud Monitoring with Firebase to monitor App Check and Security Rules. It explains where to find rules evaluation metrics, how to analyze them, and how to create an alerting policy for sustained spikes in denied evaluations. ### Source excerpt Learn how to monitor security metrics and set up alerting policies with Cloud Monitoring and Firebase.