<doc>
    <int name="Id">7</int>
    <str name="Name">PersonName</str>
    <str name="Address">Address Line 1,Address Line 2,City</str>
    <str name="Country">India</str>
    <str name="ImageURL">0000028415.jpeg</str>
    <arr name="Category">
      <str>Student</str>
      <str>Group A</str>
    </arr>
</doc>

我们希望对其进行规范化,并为Person,Country和Category分别提供doc类型.

<doc>
    <int name="PId">7</int>
    <str name="Name">PersonName</str>
    <str name="Address">Address Line 1,City</str>
    <str name="CountryId">91</str>
    <str name="ImageURL">0000028415.jpeg</str>
    <arr name="CategoryId">
      <str>2</str>
      <str>5</str>
    </arr>
</doc>



    <doc>
        <int name="CId">91</int>
        <str name="CountryName">India</str>
    </doc>



<doc>
        <int name="CatId">2</int>
        <str name="CategoryName">Student</str>
    </doc>

请注意,我只是简化了示例,我使用的实际文档比这复杂得多,并且索引中有数百万个文档.

我想了解,如何使用这种文档结构加入并进行过滤查询.与之前的情况相比,它如何影响性能,其中所有细节都存储在单个doc结构中.

更新

具有当前结构的示例查询,希望这有助于了解当前如何完成它：

以下是应用了某些方面的搜索示例查询 –

/select?indent=on&wt=json&facet.field={!ex%3DCategory}Category&facet.field=Manufacturer&facet.field=Vendor&facet.field=f_Hardrive&facet.field=f_Operating%2BSystem&facet.field=f_Memory&facet.field=f_cpu%2BType&facet.field=f_Screensize&facet.field=pa_OS&bf=&start=0&fq={!tag%3DCategory}Category:Notebooks&fq=Price:[0+TO+9999999999999]&rows=6&version=2.2&bq=&facet.query=AverageRating:[4+TO+5]&facet.query=AverageRating:[3+TO+5]&facet.query=AverageRating:[2+TO+5]&facet.query=AverageRating:[1+TO+5]&q=(laptop)&defType=edismax&spellcheck.q=(laptop)&qf=Name^7++ShortDescription^6++FullDescription^4+CategoryCopy^2+ManufacturerCopy^2+Sku^3+ChildSku^3+nGramContent+Attributes+ProductAttributes+Tag+ManufacturerPartNumber+CustomProperties&spellcheck=true&stats=true&facet.mincount=1&facet=true&spellcheck.collate=true&stats.field=Price

这个带有facets的过滤器查询：

select?indent=on&wt=json&facet.field=f_Hardrive&facet.field=f_Operating%2BSystem&facet.field=f_Memory&facet.field=f_cpu%2BType&facet.field={!ex%3Df_Screensize}f_Screensize&facet.field=pa_HDD&facet.field=pa_OS&facet.field={!ex%3Dpa_OS}pa_OS&facet.field=pa_OS&facet.field=pa_Processor&facet.field=pa_RAM&facet.field=pa_Software&facet.field=Vendor&facet.field={!ex%3DManufacturer}Manufacturer&facet.field=Category&start=0&fq=StockAvailability:(true)&fq={!tag%3Df_Screensize}f_Screensize:15.0%2527%2527\!!4!!&fq={!tag%3Dpa_OS}pa_OS:Apple\!!0!!&fq={!tag%3DPrice}Price:[594+TO+1800]&sort=CDO_1+asc&rows=6&version=2.2&facet.query=AverageRating:[4+TO+5]&facet.query=AverageRating:[3+TO+5]&facet.query=AverageRating:[2+TO+5]&facet.query=AverageRating:[1+TO+5]&q=CategoryID:(1+OR+2+OR+3+OR+4)&defType=edismax&spellcheck=true&stats=true&facet.mincount=1&facet=true&spellcheck.collate=true&stats.field=Price