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@varbear shared a link, 2 months, 1 week ago
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Use Python for Scripting!

Shell scripts love to break across macOS and Linux. Blame all the GNU vs BSD quirks;sed,date,readlink, take your pick. The mess adds up fast, especially in build pipelines and CI systems. This post makes the case for a cleaner way:Python 3. Standard library. Predictable behavior. Same results whethe.. read more  

Use Python for Scripting!
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@varbear shared a link, 2 months, 1 week ago
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14x Faster Faceted Search in PostgreSQL with ParadeDB

ParadeDB brings Elasticsearch-stylefacetingtoPostgreSQL, ranked search results and filter counts, all in one shot. No extra passes. It pulls this off with a customwindow function, planner hooks, andTantivy's columnar index under the hood. That's how they’re squeezing out10×+ speedupson hefty dataset.. read more  

14x Faster Faceted Search in PostgreSQL with ParadeDB
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@varbear shared a link, 2 months, 1 week ago
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How We Migrated DB 1 to DB 2 , 1 Billion Records Without Downtime

A team movedover 1 billion production records- no downtime, no drama. The stack: dual writes, Kafka retries, and idempotent inserts to keep it clean. They ranshadow readsto sniff for errors, chunked the transfers with checksums, and held off indexing to keep inserts fast. Caches got warmed early to .. read more  

How We Migrated DB 1 to DB 2 , 1 Billion Records Without Downtime
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@varbear shared a link, 2 months, 1 week ago
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How Reddit Migrated Comments Functionality from Python to Go

Reddit successfully migrated its monolithic, high-traffic Comments service from legacy Python to modern Go microservices with zero user disruption. This was achieved by using a "tap compare" for reads and isolated "sister datastores" for writes, ensuring safe verification of the new code against pro.. read more  

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@kaptain shared a link, 2 months, 1 week ago
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Why Kubernetes Won: Perfect Timing & Developer Culture

Kubernetes won big because the stars aligned, DevOps took off, Docker exploded, and enterprises finally stopped side-eyeing open source. Then came the institutional tailwind: CNCF pushed hard, GCP bet big, and the rest followed. Kubernetes isn't just tech. It's a new operating model, built in the op.. read more  

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@kaptain shared a link, 2 months, 1 week ago
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An In-Depth Look at Istio Ambient Mode with Calico

Tigera just wiredIstio Ambient Modeinto Calico. That means you getsidecarless service mesh, think mTLS, L4/L7 policy, and observability, without stuffing every pod with a sidecar. It’s all handled by lean zTunnel and Waypoint proxies. Ports stay visible, soCalico and Istio policiesplay nice. No rewr.. read more  

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@kaptain shared a link, 2 months, 1 week ago
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Kubernetes Made Simple: A Guide for JVM Developers

A sharp walkthrough for JVM devs shipping aKotlin Spring Boot app on Kubernetes. It covers the full deployment arc, packaging with Docker, wiring upDeploymentandServicemanifests, and managing config withConfigMapsandSecrets. There's a cleanPostgreSQLintegration baked in. It even gets intoheader-base.. read more  

Kubernetes Made Simple: A Guide for JVM Developers
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@kaptain shared a link, 2 months, 1 week ago
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The “Inception” of Kubernetes: A Deep Dive into vCluster Architecture and Benefits

vCluster, a CNCF sandbox project, spins up real-deal Kubernetes control planes inside pods. Each lives in its own namespace but behaves like a full cluster, admin access, CRDs, Helm, the works. It reuses the host’s worker nodes using a syncer that routes vCluster workloads onto the real thing... read more  

The “Inception” of Kubernetes: A Deep Dive into vCluster Architecture and Benefits
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@kaptain shared a link, 2 months, 1 week ago
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A Deep Dive into Kubernetes Headless Service

Headless Serviceis a powerfulKubernetesfeature enabling direct pod-to-pod communication forstateful applicationsand preciseservice discoverywithout traditional load balancing.No automatic load balancing, pod IP changes, andspecial use casesmake it ideal for specific scenarios, not general workloads... read more  

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@kaptain shared a link, 2 months, 1 week ago
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How to Troubleshoot Common Kubernetes Errors

A fresh Kubernetes troubleshooting guide lays out real-world tactics for tracking down 12 common cluster headaches. Think:kubectlsleuthing, poking through system logs, scraping observability metrics, and jumping intodebug containers. The guide breaks down howAIOpsis stepping in, digesting event data.. read more  

How to Troubleshoot Common Kubernetes Errors
AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.