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I have problem with rebuilding xDB index in solr. I changed IndexPIISensitiveData to true in job SerachIndexer and after executing the command \XConnectSearchIndexer.exe -rr there is no sensitive data from facet in the indexes. They are added only for "new" data in xDb (postponed after changing the flag)

Additionally, I noticed (I don't know if it is related) an error in the logs:

[Error] Command find failed: errmsg: "Sort operation used more than the maximum 33554432 bytes of RAM. Add an index, or specify a smaller limit.". Sitecore.Xdb.Collection.Data.MongoDb.Exceptions.MongoDbProviderException: Command find failed: errmsg: "Sort operation used more than the maximum 33554432 bytes of RAM. Add an index, or specify a smaller limit.". ---> MongoDB.Driver.MongoCommandException: Command find failed: errmsg: "Sort operation used more than the maximum 33554432 bytes of RAM. Add an index, or specify a smaller limit.".

But the rebild command was theoretically correct (I did it on an empty xdb_rebuild index). In the hints for the problem with mongo there are ideas for creating an index, but I don't know what to put it on. Increasing the RAM memory also does not make sense - this base is very large. Or maybe this has nothing to do with the problem? The goal is to re-index the xdb database in order to display a person with sensitive data.

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I found the answer:

https://doc.sitecore.com/developers/92/sitecore-experience-platform/en/configuring-the-xconnect-search-indexer.html

You need to control the parameters: ParallelizationDegree and BatchSize.

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  • Hi @Jan, what did you set the values to for the parameters mentioned above? – Christopher Huemmer Mar 10 at 20:41
  • The parameters will vary depending on your environment. This article can be helpful: sitecoreart.martinrayenglish.com/2019/09/… – Jan Nowak yesterday
  • Hi @Jan, thanks for the link. I've tried many different indexer configurations on a bug we had recently. Turned out that the root cause was different than what you had encountered, the symptoms however were similar. – Christopher Huemmer yesterday

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