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DevOps & observability · #prometheus
Learn how to hook Prometheus to a long‑term store with remote_write, configure TLS and auth, monitor success, and understand trade‑offs. A step‑by‑step guide with a real config example.
Learn how to implement Prometheus Remote Write for long-term storage, including configuration, monitoring metrics, and handling failure modes like memory pressure.
Discover how to expose Moleculer service metrics to Prometheus with built‑in middleware, extend them with custom gauges, and balance security and performance. A step‑by‑step guide with code snippets and best‑practice trade‑offs.
Decide between local TSDB storage and Remote Write for Prometheus based on retention needs, disk I/O, and CPU overhead. Learn how to configure and verify both strategies.
Goal: configure Ory Kratos self‑service login alerts so that genuine credential‑stuffing attacks trigger notifications while routine low‑volume errors do not flood the alert channel. Current metrics expose kratos_selfservice_flow_error_total labeled by flow type and strategy, but each failed attempt increments the counter individually. Operators must decide
Remote Write Scalability and Memory Pressure Prometheus utilizes a queue-based mechanism to buffer time series data before transmitting it to remote storage endpoints via HTTP POST requests. In high-volume environments, the interaction between max_samples_per_send and the overall capacity within the queue_config determines the memory footprint of the remote
Goal Establish alerting that surfaces genuine performance issues while suppressing false positives caused by sampling granularity. Constraints MariaDB’s Performance Schema samples at a default 10 s interval, potentially missing brief spikes. The Prometheus exporter translates these tables into metrics, but the exporter itself does not alter the sampling rate
Prometheus integrates with Kubernetes service discovery to scrape pod endpoints and logs scrape errors at warn level with target address and error context. The integration relies on metadata labels such as __meta_kubernetes_pod_name to associate failures with pod lifecycle events. Target state moves from UP to DOWN after consecutive scrape failures within sc
Goal is to lower storage and resource consumption for a low-traffic Prometheus deployment by keeping only a short local window and offloading longer-term data. The configuration involves --storage.tsdb.retention.time for local pruning, a coarser scrape_interval to reduce head block series churn, and remote_write to a separate store. Drop relabeling is also u
mariadb
kubernetes