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#metrics

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LabPlot

How to visualize #server #metrics in #RealTime via #TCP in @LabPlot ?

@labplot@lemmy.kde.social @sysadmin@lemmy.world @sysadmin@lemmy.ml @cybersecurity

The purpose of this simple tutorial is not to position #LabPlot against dedicated applications, but rather to show how its "Live Data" functionality can be used to read and visualize data in real time.

👉 docs.labplot.org/en/tutorials/

#DevOps#SysAdmin#LiveData
Public
BaselOne

🎥 Das siebte Video der #BaselOne 2024-Serie ist da! 🎉

🔍 "Using Metrics and Traces to Diagnose Cloud-Native Applications" mit Grace Jansen

Wie können wir Metriken und Traces nutzen, um cloud-native Anwendungen zu überwachen und Fehler frühzeitig zu erkennen? Erfahre mehr über Open-Source-Tools wie OpenTelemetry und MicroProfile!

📺 youtube.com/watch?v=YifivDuiXeM
🔖 Save the Date – BaselOne 2025! 15. & 16. Oktober 2025

🙏 Danke an das Team & die Community!

#CloudNative#Java#Metrics
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Data Bene

Have you heard about StatsMgr? A recent release from our team here at Data Bene, it's an #OpenSource #PostgreSQL extension that enables support for advanced statistics querying & management.

Pull, watch, fork, or contribute to the extension on #Codeberg: codeberg.org/Data-Bene/StatsMg

Or find more details on our blog: data-bene.io/en/blog/postgres-

StatsMgr: PostgreSQL Statistics Management Extension StatsMgr is a PostgreSQL extension designed for efficient and organized management of statistics. It leverages background workers and shared memory to snapshot, manage, and query various statistics kinds, including WAL, SLRU, checkpointing, and others. The extension supports dynamic initialization, shared memory utilization, and SQL interfaces for querying and managing statistics data. Main Features: - snapshot PostgreSQL cumulative statistics at fixed intervals - keep history of the N last snapshots - C and SQL API
#Postgres#FOSS#OSS
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Miguel Afonso Caetano

"Rather than using metrics as the sole truth when it comes to assessing academic performance, we should put them in perspective. We could do this by complementing quantitative metrics with qualitative information. Narratives, discussions of assumptions, and explanations can give back much-needed context to interpret metrics. Read a job candidate’s working paper instead of counting her publications in journals. Metrics can be great conversation starters, but should not replace our understanding of what (a) good research(er) is.

If we don’t change our use of metrics, research quality itself may suffer. Peter Higgs, the Nobel laureate who passed away last year, warned in an interview: “Today I wouldn’t get an academic job. It’s as simple as that. I don’t think I would be regarded as productive enough.” The pressure to produce and perform in the short term can come at the expense of scientific progress in the long term. A more critical stance towards metrics and rankings is essential if we want to enhance the quality and credibility of research."

socialsciencespace.com/2025/01

#Science#SocialSciences#Quantification
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