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Scaling Prometheus: Managing 80M Metrics Smoothly

Flipkart ditched its creakyStatsD + InfluxDBstack for afederated Prometheussetup—built to handle 80M+ time-series metrics without choking. The move leaned intopull-based collection,PromQL's firepower, andhierarchical federationfor smarter aggregation and long-haul queries. Why it matters:Prometheus.. read more  

Scaling Prometheus: Managing 80M Metrics Smoothly
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Writing an operating system kernel from scratch

A barebonestime-sharing OS kernel, written inZig, running onRISC-V. It leans onOpenSBIfor console I/O and timer interrupts. Threads? Statically allocated, each running inuser mode (U-mode). The kernel stays insupervisor mode (S-mode), where it catchessystem callsandcontext switchesvia timer ticks. .. read more  

Writing an operating system kernel from scratch
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Magical systems thinking

AI now writes over **25% of Google’s** and as much as **90% of Anthropic’s** code. That’s not a trend—it’s a regime change. Still, the mess in large public systems reminds us: clever analysis isn’t enough. Complex systems don’t behave; they misbehave. When the machines are churning out code, the .. read more  

Magical systems thinking
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Best 20 Linux Commands for Daily Use in Production Servers

A fresh roundup drops20 go-to Linux commandsfor production sysadmins, dialing in on modern defaults likehtop > top,ss > netstat, andip > ifconfig. The shift? Faster tools that actually get updates. Built with systemd in mind, too. Expect the usual suspects—journalctl,rsync,crontab—all still pulling.. read more  

Best 20 Linux Commands for Daily Use in Production Servers
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SLI Evolution Stages

A new SLI evolution model lays out a maturity roadmap—from rebranded latency/error metrics to ones that actually track business impact. It replaces shallow signals and pulls in the stuff that matters: how service failures hit user goals, tasks, and bottom lines... read more  

SLI Evolution Stages
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%CPU Utilization Is A Lie

Stress tests on the Ryzen 9 5900X uncovered a big gap between **reported CPU utilization** and what the chip actually pushes. Around 50% on paper? Could mean close to full throttle in reality—thanks to sneaky behaviors from **SMT resource sharing** and **Turbo frequency scaling**. **Takeaway:** Raw.. read more  

%CPU Utilization Is A Lie
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Introducing Budget Controls for AWS: Automatically Manage Your Cloud Costs

**Budget Controls for AWS** just got better. The open-source tool now reins in more than just EC2. It wrangles **RDS Aurora**, **SageMaker**, and **OpenSearch** too. Under the hood, it taps **AWS Budgets**, **AWS Config**, and **custom tags** to watch spend like a hawk. Hit a budget threshold? It c.. read more  

Introducing Budget Controls for AWS: Automatically Manage Your Cloud Costs
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Fast, Secure Kubernetes with AKS Automatic

Azure dropped **AKS Automatic**, a new managed Kubernetes tier that tries to do it all—so you don’t have to. It comes with baked-in best practices: autoscaling via HPA, VPA, KEDA, and Karpenter. Automated patching. Node repair. Monitoring. All wired up by default. You still get full access to the .. read more  

Fast, Secure Kubernetes with AKS Automatic
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v1.34: Decoupled Taint Manager Is Now Stable

Kubernetes 1.34 graduates the taint eviction controller to GA. Now, the node lifecycle controller only applies taints, while a dedicated taint eviction controller manages pod eviction. First split in 1.29, now stable in 1.34... read more  

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v1.34: Pods Report DRA Resource Health

Kubernetes v1.34 lands with an alpha upgrade to **[KEP-4680](https://github.com/kubernetes/enhancements/tree/master/keps/sig-node/4680-add-resource-health-to-pod-status)**, pushing **Dynamic Resource Allocation (DRA)** into smarter territory: health-aware Pods. DRA drivers can now stream device heal.. read more  

BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).