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@priya_prabu shared a post, 3 months, 3 weeks ago
Senior Product Marketer

Key Oracle performance metrics

Oracle performance issues rarely come from a single metric. This guide breaks down the most important Oracle performance indicators across instance health, memory, storage, waits, SQL, and availability, and shows how to use them together to detect bottlenecks early and prevent downtime.

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@eon01 shared a post, 3 months, 3 weeks ago
Founder, FAUN.dev

Microk8s vs K3s

Kubernetes k3s MicroK8s Rancher k3d

To truly master Kubernetes, you need a safe sandbox, and running a lightweight distribution is the perfect solution for your local development workflow. These smaller K8s flavors provide a full-featured, yet constrained, environment that is easy on system resources. Both MicroK8s (maintained by Canonical) and k3s (from Rancher) are popular, production-ready options that deliver the core K8s experience with minimal operational burden, low storage needs, and simple networking setups.

These two platforms are fantastic for learning, experimentation, rapid testing, and skill development. If you don't know which one to choose, this post will give you the quick overview you need to decide.

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@kaptain added a new tool k3d , 3 months, 3 weeks ago.
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@varbear shared a link, 3 months, 3 weeks ago
FAUN.dev()

Phishing for AWS Credentials via the New 'aws login' Flow

AWS rolled out a newaws loginCLI command using OAuth 2.0 with PKCE. It grabs short-lived credentials, finally pushing out those dusty long-lived access keys. But here’s the hitch:The remote login flow opens up a phishing gap. Since the CLI session and browser session aren’t bound, attackers could sp.. read more  

Phishing for AWS Credentials via the New 'aws login' Flow
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@varbear shared a link, 3 months, 3 weeks ago
FAUN.dev()

SQLite JSON Superpower: Virtual Columns + Indexing - DB Pro Blog

SQLite’sJSON virtual generated columnspunch way above their weight. They let you index JSON fields on the fly, no migrations, no whining. Computed like real columns, queryable like real columns, indexable like real columns. But from JSON. Want flexibility without surrendering speed? This flips the s.. read more  

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@varbear shared a link, 3 months, 3 weeks ago
FAUN.dev()

Guarding My Git Forge Against AI Scrapers

To stop a wave of scraping on their self-hosted Forgejo, the author stacked defenses like a firewall architect on caffeine. First camemanual IP rate-limiting. ThenNGINX caching and traffic shaping. Finally:Iocaine 3. That last one didn’t just block bots, it lured them into a maze of junk pages. The .. read more  

Guarding My Git Forge Against AI Scrapers
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@varbear shared a link, 3 months, 3 weeks ago
FAUN.dev()

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, 3 months, 3 weeks ago
FAUN.dev()

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, 3 months, 3 weeks ago
FAUN.dev()

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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@varbear shared a link, 3 months, 3 weeks ago
FAUN.dev()

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!
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.