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        <p>
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          where each time series is defined as a unique combination of key-value label pairs.
        </p>
        <p>
          When looking to reduce the number of active series in your data source,
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        <p>
          This table returns a list of the labels with the highest number of series.
        </p>
        <p>
          Use this table to identify labels that are storing dimensions with high cardinality
          (many different label values).
        </p>
        <p>
          It is recommended to choose labels such that they have a finite set of values,
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          and therefore can dramatically increase the number of time series in your system.
        </p>`,seriesCountByFocusLabelValue:`
       <p>
          This table returns a list of unique label values per selected label.
       </p>
       <p>
          Use this table to identify label values that are storing per each selected series.
       </p>`,labelValueCountByLabelName:`
       <p>
          This table returns a list of labels with the highest number of the unique values.
       </p>
      `,seriesCountByLabelValuePair:`
        <p>
          This table returns a list of the label values pairs with the highest number of series.
        </p>
        <p>
          Use this table to identify unique label values pairs. This helps to identify same labels 
          is applied to count timeseries in your system, since every unique combination of key-value label pairs 
          creates a new time series and therefore can dramatically increase the number of time series in your system
        </p>`}}get tablesHeaders(){return{seriesCountByMetricName:Wd,seriesCountByLabelName:Kd,seriesCountByFocusLabelValue:Jd,seriesCountByLabelValuePair:Zd,labelValueCountByLabelName:Xd}}totalSeries(r,n=!1){return r==="labelValueCountByLabelName"?-1:n?this.tsdbStatus.totalSeriesPrev:this.tsdbStatus.totalSeries}}const tm=()=>{const t=new em,[r]=me(),n=r.get("match"),a=r.get("focusLabel"),s=+(r.get("topN")||10),o=r.get("date")||Q().tz().format(it),i=Gt(o),l=Z(),{tenantId:c,serverUrl:m}=ne(),[d,u]=b(!1),[h,p]=b(),[v,y]=b(t.defaultTSDBStatus),[f,g]=b(t.defaultMetricNameStats),[_,C]=b(!1),w=async T=>{const E=await fetch(T),M=await E.json();if(E.ok)return M;console.error(`Error fetching ${T}:`,M);const I=M.errorType||ge.unknownType,k=M?.error||M?.message||"see console for more details",L=[I,k].join(`\r
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      -
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    )
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    *
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((cpuCount - cpuIdle) * 100) / cpuCount`})]}),e("div",{className:"vm-with-template-tutorial-section",children:[e("p",{className:"vm-with-template-tutorial-section__text",children:["4. Put node_cpu_seconds_total","{commonFilters}"," into its own variable with the name cpuSeconds:"]}),e(Qe,{code:`WITH (
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