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@priya_prabu shared a post, 5 days, 2 hours 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, 5 days, 2 hours 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 , 5 days, 5 hours ago.
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@varbear shared a link, 6 days, 15 hours 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, 6 days, 15 hours 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, 6 days, 15 hours 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, 6 days, 15 hours ago
FAUN.dev()

How We Saved 70% of CPU and 60% of Memory in Refinery’s Go Code, No Rust Required.

Refinery 3.0 cuts CPU by 70% and slashes RAM by 60%. The trick: selective field extraction from serialized spans. No full deserialization. Fewer heap allocations. Way less waste. It also recycles buffers, handles metrics smarter, and is gearing up to parallelize its core decision loop... read more  

How We Saved 70% of CPU and 60% of Memory in Refinery’s Go Code, No Rust Required.
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@varbear shared a link, 6 days, 15 hours 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, 6 days, 15 hours 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, 6 days, 15 hours 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!
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).