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Website docs

Github

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Website docs

Github

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https://grafana.com/docs/grafana/latest/variables/variable-types/global-variables/

$__range

Currently only supported for Prometheus and Loki data sources. This variable represents the range for the current dashboard. It is calculated by to - from. It has a millisecond and a second representation called $__range_ms and $__range_s.

$__interval

You can use the $__interval variable as a parameter to group by time (for InfluxDB, MySQL, Postgres, MSSQL), Date histogram interval (for Elasticsearch), or as a summarize function parameter (for Graphite).

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https://iximiuz.com/en/posts/prometheus-metrics-labels-time-series/

What is a time series in Prometheus?

*Side note 1: Despite being born in the age of distributed systems, every Prometheus server node is autonomous. I.e., there is no distributed metric storage in the default Prometheus setup, and every node acts as a self-sufficient monitoring server with local metric storage. It simplifies a lot of things, including the following explanation, because we don’t need to think of how to merge overlapping series from different Prometheus nodes *😉

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https://iximiuz.com/en/posts/prometheus-functions-agg-over-time/

Almost all the functions in the aggregation family accept just a single parameter - a range vector. It means that the over time part, i.e., the duration of the aggregation period, comes from the range vector definition itself.

The only way to construct a range vector in PromQL is by appending a bracketed duration to a vector selector. E.g. http_requests_total[5m]. Therefore, an <agg>_over_time() function can be applied only to a vector selector, meaning the aggregation will always be done using raw scrapes.

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https://en.wikipedia.org/wiki/Cache_coherence

Theoretically, coherence can be performed at the load/store granularity. However, in practice it is generally performed at the granularity of cache blocks.[3]

https://www.geeksforgeeks.org/cache-coherence/

Cache coherence is the discipline that ensures that changes in the values of shared operands are propagated throughout the system in a timely fashion.

http://tutorials.jenkov.com/java-concurrency/cache-coherence-in-java-concurrency.html

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https://events.static.linuxfound.org/sites/events/files/slides/Optimizing%20Application%20Performance%20in%20Large%20Multi-core%20Systems_0.pdf

  • Ticket spinlock is the spinlock implementation used in the Linux kernel prior to 4.2. A lock waiter gets a ticket number and spin on the lock cacheline until it sees its ticket number. By then, it becomes the lock owner and enters the critical section.

  • Queued spinlock is the new spinlock implementation used in 4.2 Linux kernel and beyond. A lock waiter goes into a queue and spins in its own cacheline until it becomes the queue head. By then, it can spin on the lock cacheline and attempt to get the lock.