Difference between revisions of "Cardinality"

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Calculating the exact cardinality of a [[multiset]] requires an amount of memory proportional to the [[cardinality]], which is impractical for very large data sets. The HyperLogLog algorithm is able to estimate cardinalities of > 109 with a typical accuracy (standard error) of 2%, using 1.5 kB of memory.
 
Calculating the exact cardinality of a [[multiset]] requires an amount of memory proportional to the [[cardinality]], which is impractical for very large data sets. The HyperLogLog algorithm is able to estimate cardinalities of > 109 with a typical accuracy (standard error) of 2%, using 1.5 kB of memory.
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You can have lower cardinality (1:5 label-value ratio), standard cardinality (1:80 label-value ratio), or high cardinality (1:10,000 label-value ratio). <ref>https://grafana.com/blog/2022/02/15/what-are-cardinality-spikes-and-why-do-they-matter/</ref>
  
  
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* [[Infinite set]], [[Multiset]]
 
* [[Infinite set]], [[Multiset]]
  
You can have lower cardinality (1:5 label-value ratio), standard cardinality (1:80 label-value ratio), or high cardinality (1:10,000 label-value ratio). <ref>https://grafana.com/blog/2022/02/15/what-are-cardinality-spikes-and-why-do-they-matter/</ref>
 
  
  

Revision as of 14:03, 8 July 2022

wikipedia:Cardinality is generally defined as the number of elements in a set.

Calculating the exact cardinality of a multiset requires an amount of memory proportional to the cardinality, which is impractical for very large data sets. The HyperLogLog algorithm is able to estimate cardinalities of > 109 with a typical accuracy (standard error) of 2%, using 1.5 kB of memory.

You can have lower cardinality (1:5 label-value ratio), standard cardinality (1:80 label-value ratio), or high cardinality (1:10,000 label-value ratio). [1]


NRQL

FROM Metric SELECT cardinality(metric.name) SINCE today RAW



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See also

  • https://grafana.com/blog/2022/02/15/what-are-cardinality-spikes-and-why-do-they-matter/
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