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Mr("keydown",(e=>{const{code:t,ctrlKey:n,altKey:r}=e;"Space"===t&&(n||r)&&(e.preventDefault(),a({type:"SET_AUTOCOMPLETE_QUICK",payload:!0}))})),Nt("div",{className:Er()({"vm-additional-settings":!0,"vm-additional-settings_mobile":t}),children:[!(null!==n&&void 0!==n&&n.autocomplete)&&Nt(ba,{title:Nt(Ct.FK,{children:["Quick tip: ",Na]}),children:Nt(Ti,{label:"Autocomplete",value:r,onChange:()=>{a({type:"TOGGLE_AUTOCOMPLETE"})},fullWidth:t})}),Nt(Ti,{label:"Disable cache",value:i,onChange:()=>{l({type:"TOGGLE_NO_CACHE"})},fullWidth:t}),!(null!==n&&void 0!==n&&n.traceQuery)&&Nt(Ti,{label:"Trace query",value:o,onChange:()=>{l({type:"TOGGLE_QUERY_TRACING"})},fullWidth:t})]})},ml=e=>{const{isMobile:t}=ta(),n=(0,r.useRef)(null),{value:a,toggle:i,setFalse:o}=ma(!1);return t?Nt(Ct.FK,{children:[Nt("div",{ref:n,children:Nt(da,{variant:"outlined",startIcon:Nt(dr,{}),onClick:i,ariaLabel:"additional the query settings"})}),Nt(ha,{open:a,buttonRef:n,placement:"bottom-left",onClose:o,title:"Query 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    \n

    \n When looking to reduce the number of active series in your data source,\n you can start by inspecting individual metrics with high cardinality\n (i.e. that have lots of active time series associated with them),\n since that single metric contributes a large fraction of the series that make up your total series count.\n

    ",seriesCountByLabelName:"\n

    \n This table returns a list of the labels with the highest number of series.\n

    \n

    \n Use this table to identify labels that are storing dimensions with high cardinality\n (many different label values).\n

    \n

    \n It is recommended to choose labels such that they have a finite set of values,\n since every unique combination of key-value label pairs creates a new time series\n and therefore can dramatically increase the number of time series in your system.\n

    ",seriesCountByFocusLabelValue:"\n

    \n This table returns a list of unique label values per selected label.\n

    \n

    \n Use this table to identify label values that are storing per each selected series.\n

    ",labelValueCountByLabelName:"\n

    \n This table returns a list of labels with the highest number of the unique values.\n

    \n ",seriesCountByLabelValuePair:"\n

    \n This table returns a list of the label values pairs with the highest number of series.\n

    \n

    \n Use this table to identify unique label values pairs. This helps to identify same labels \n is applied to count timeseries in your system, since every unique combination of key-value label pairs \n creates a new time series and therefore can dramatically increase the number of time series in your system\n

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WITH expressions may improve this:"]}),Nt(pp,{code:'WITH (\n my_resource_utilization(free, limit, filters) = (limit{filters} - free{filters}) / limit{filters} * 100\n)\nmy_resource_utilization(\n node_memory_MemFree_bytes,\n node_memory_MemTotal_bytes,\n {instance=~"$node:$port",job=~"$job"},\n)'}),Nt("p",{className:"vm-with-template-tutorial-section__text",children:"Now the template function my_resource_utilization() may be used for monitoring arbitrary resources - memory, CPU, network, storage, you name it."})]}),Nt("div",{className:"vm-with-template-tutorial-section",children:[Nt("p",{className:"vm-with-template-tutorial-section__text",children:["Let's take another nice query from ",Nt(vp,{})," dashboard:"]}),Nt(pp,{code:'(\n (\n (\n count(\n count(node_cpu_seconds_total{instance=~"$node:$port",job=~"$job"}) by (cpu)\n )\n )\n -\n avg(\n sum by (mode) (rate(node_cpu_seconds_total{mode=\'idle\',instance=~"$node:$port",job=~"$job"}[5m]))\n )\n )\n *\n 100\n)\n /\ncount(\n count(node_cpu_seconds_total{instance=~"$node:$port",job=~"$job"}) by (cpu)\n)'}),Nt("p",{className:"vm-with-template-tutorial-section__text",children:"Do you understand what does this mess do? 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