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    <title>topic Calculated Dimension Performance Issue in Water Cooler</title>
    <link>https://community.qlik.com/t5/Water-Cooler/Calculated-Dimension-Performance-Issue/m-p/366879#M1141</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Hi there,&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I have a straight table with a calculated dimension using the aggr() function. The dimension for the aggr function is a field of several million values. I then make selections in some other filters, and should reduce the possible values of the aggr dimension to several thousands. However, charting of the straight table still takes several minutes. &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;So, my questions are as following:&lt;/P&gt;&lt;P&gt;1. Is it appropriate to use aggr() over a dimension of millions of values to create a dimension in a straight table?&lt;/P&gt;&lt;P&gt;2. Can reducing possible values of the dimension of the aggr() reduce time of charting the table?&lt;/P&gt;&lt;P&gt;3. If it is no to 1. and 2 above, what alternatives can I have for a better performance?&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;The report is historical data analysis. I also wonder what data model technique is appropriate for trending analysis with QlikView. I would appreciate for any suggestions and/or examples on this.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Thank you very much in advance!&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Wed, 25 Apr 2012 02:15:28 GMT</pubDate>
    <dc:creator />
    <dc:date>2012-04-25T02:15:28Z</dc:date>
    <item>
      <title>Calculated Dimension Performance Issue</title>
      <link>https://community.qlik.com/t5/Water-Cooler/Calculated-Dimension-Performance-Issue/m-p/366879#M1141</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Hi there,&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I have a straight table with a calculated dimension using the aggr() function. The dimension for the aggr function is a field of several million values. I then make selections in some other filters, and should reduce the possible values of the aggr dimension to several thousands. However, charting of the straight table still takes several minutes. &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;So, my questions are as following:&lt;/P&gt;&lt;P&gt;1. Is it appropriate to use aggr() over a dimension of millions of values to create a dimension in a straight table?&lt;/P&gt;&lt;P&gt;2. Can reducing possible values of the dimension of the aggr() reduce time of charting the table?&lt;/P&gt;&lt;P&gt;3. If it is no to 1. and 2 above, what alternatives can I have for a better performance?&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;The report is historical data analysis. I also wonder what data model technique is appropriate for trending analysis with QlikView. I would appreciate for any suggestions and/or examples on this.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Thank you very much in advance!&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 25 Apr 2012 02:15:28 GMT</pubDate>
      <guid>https://community.qlik.com/t5/Water-Cooler/Calculated-Dimension-Performance-Issue/m-p/366879#M1141</guid>
      <dc:creator />
      <dc:date>2012-04-25T02:15:28Z</dc:date>
    </item>
    <item>
      <title>Calculated Dimension Performance Issue</title>
      <link>https://community.qlik.com/t5/Water-Cooler/Calculated-Dimension-Performance-Issue/m-p/366880#M1142</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Can you pls explain it with an example?&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Fri, 27 Apr 2012 12:01:22 GMT</pubDate>
      <guid>https://community.qlik.com/t5/Water-Cooler/Calculated-Dimension-Performance-Issue/m-p/366880#M1142</guid>
      <dc:creator>jansen28</dc:creator>
      <dc:date>2012-04-27T12:01:22Z</dc:date>
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