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Tempo 3.1 release: new features for Kafka, TraceQL metrics updates, trace redaction, and more

Grafana Labs blog features a new post on Tempo 3.1 release with new features for Kafka, TraceQL metrics updates, trace redaction, and more.

Observability Stack: Prometheus, Node Exporter, Grafana

A technical how-to explaining the three-piece observability stack: Prometheus (time-series database that scrapes metrics), Node Exporter (exposes OS-level metrics at a /metrics HTTP endpoint), and Grafana (visualizes Prometheus data as dashboards). The article describes the pull-based model Prometheus uses, the role of Node Exporter as a translator of OS stats, how Grafana queries Prometheus, default ports (Prometheus 9090, Node Exporter 9100, Grafana 3000), basic install commands, a sample prometheus.yml with scrape_interval and job_name, and next steps such as adding scrape targets, writing PromQL queries, and adding Alertmanager for notifications.

• Prometheus is a time-series database and monitoring system that scrapes metrics from HTTP endpoints and exposes its own metrics on port 9090.
• Node Exporter exposes hardware and OS-level metrics at a /metrics endpoint (default port 9100) and is typically installed one-per-machine.
• Grafana queries Prometheus (e.g., via PromQL) to render dashboards and runs by default on port 3000; it stores no metrics itself.

Building an Internal Developer Platform on Azure AKS

This technical article explains how to create an Internal Developer Platform (IDP) using Azure Kubernetes Service (AKS). It outlines core components including AKS as the managed Kubernetes backbone, a service mesh (e.g., Istio or Linkerd) for microservice communication, CI/CD pipelines (Azure DevOps, GitHub Actions, Jenkins) for automated build and deployment, monitoring and logging tools (Azure Monitor, Prometheus, Grafana, Azure Log Analytics), and security/compliance controls (RBAC, Pod Security Policies, Azure Policy, Azure Security Center). The piece describes an example developer workflow from code push to production and summarizes benefits such as increased efficiency, scalability, security, and consistency.

• An Internal Developer Platform (IDP) is a set of tools, processes, and automations that simplifies development, testing, and deployment for developers.
• Azure Kubernetes Service (AKS) is presented as the central managed Kubernetes environment where containerized applications run.
• Service meshes such as Istio or Linkerd are recommended to manage microservice communication, including load balancing, traffic management, and security policies.

Gap Between TLS Everywhere and Actual Transport Security

This article discusses the common misconception of 'TLS everywhere' in cloud-native architectures, where TLS is often only implemented at the edge, leaving internal traffic unencrypted. It identifies four key layers where TLS is typically absent: ingress-to-pod, pod-to-pod, application-to-database, and cluster infrastructure certificates. The author details implementation strategies to close these gaps without necessarily adopting a service mesh, including re-encryption at the ingress, using cert-manager for automated certificate management, and implementing mTLS at the application level for smaller service estates. The article provides practical configuration examples, such as NGINX ingress annotations, cert-manager Certificate resources, and Prometheus alerting rules for certificate expiry. It emphasizes the importance of automating certificate rotation and monitoring time-to-expiry to prevent outages.

• TLS is often terminated only at the edge, leaving internal traffic unencrypted in many systems.
• Kubernetes does not encrypt pod-to-pod data plane traffic by default.
• Enabling TLS on a database (e.g., RDS) does not enforce its use; connection strings need to have sslmode=require or Encrypt=True.

Resilient Real-Time Systems with WebSockets & Redis Pub/Sub

This technical guide explains how to build resilient, low-latency real-time systems by combining persistent WebSocket client-server connections with Redis as a central pub/sub broadcast layer, distributed state store, and cache. It describes architectural patterns for scaling (single server, multiple WebSocket servers + single Redis, and Redis Cluster), and explains when to integrate durable queues (Kafka/RabbitMQ/AWS SQS) for persistence and guaranteed delivery. The article includes a concrete Node.js example (ws and ioredis: server.js, publisher.js, client.html) and Docker, plus client reconnection best practices (exponential backoff) and session persistence in Redis to allow resuming on another instance. Operational topics covered are Redis high availability (Sentinel/Cluster), sharding and serialization, backpressure handling, load balancing, security, idempotency, and monitoring. It also discusses operational deployment patterns, monitoring metrics, and security practices for production environments.

• Combines persistent WebSocket client connections with Redis Pub/Sub as a broadcast layer and Redis as a distributed state store and cache.
• Covers scaling patterns: single server, multiple WebSocket servers + single Redis, and multiple servers with Redis Cluster for sharding and horizontal scale.
• Provides a practical Node.js example using ws and ioredis (server.js, publisher.js, client.html) with Docker.

Mitigating HTTP Request Smuggling Attacks

The article explains HTTP Request Smuggling, an attack that leverages discrepancies in how front-end proxies (load balancers, WAFs) and back-end servers parse HTTP/1.1 request boundaries when both Content-Length and Transfer-Encoding headers are present or malformed. It describes common variants (CL.TE, TE.CL, TE.TE), concrete examples showing how smuggled requests can be interpreted differently by proxy and backend, and the resulting risks: bypassing security controls, cache poisoning, session hijacking, and credential theft. Recommended mitigations include upgrading to HTTP/2 end-to-end, normalizing/rejecting ambiguous requests at the edge (e.g., return 400 when both headers appear), using consistent server software across layers, disabling connection reuse, strict HTTP parsing (Nginx/Gunicorn settings), WAF rules, and timeouts. The article also provides testing guidance (curl, Python socket example, Burp Suite extension) and log-monitoring suggestions to detect attempted smuggling.

• HTTP Request Smuggling exploits inconsistent parsing of request boundaries between front-end proxies and back-end servers when both Content-Length and Transfer-Encoding headers are present or malformed.
• Common smuggling variants include CL.TE, TE.CL, and TE.TE; these can cause the back-end to treat leftover bytes as a new request, enabling attacks like cache poisoning and session hijacking.
• Primary mitigations: upgrade to HTTP/2 end-to-end, normalize/reject ambiguous requests at the edge (return 400 for requests containing both Content-Length and Transfer-Encoding), and use consistent parsing across layers.