[jira] [Resolved] (SOLR-15149) Learning To Rank model upload fails generically

["Alessandro Benedetti (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SOLR-15149?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Alessandro Benedetti resolved SOLR-15149.
-----------------------------------------
    Fix Version/s: 8.9
       Resolution: Fixed

> Learning To Rank model upload fails generically
> -----------------------------------------------
>
>                 Key: SOLR-15149
>                 URL: https://issues.apache.org/jira/browse/SOLR-15149
>             Project: Solr
>          Issue Type: Bug
>          Components: contrib - LTR
>            Reporter: Alessandro Benedetti
>            Priority: Major
>             Fix For: 8.9
>
>          Time Spent: 50m
>  Remaining Estimate: 0h
>
> When uploading a model, using a not existent store or other incorrect parameters you get:
> "error":{
>     "metadata":[
>       "error-class","org.apache.solr.common.SolrException",
>       "root-error-class","java.lang.ClassCastException"],
>     "msg":"org.apache.solr.ltr.model.ModelException: Model type does not exist org.apache.solr.ltr.model.LinearModel",
>     "code":400}}
> In the response, logs don't help that much out of the box, I had to go for remote debugging and of course we don't want the generic user to do that.
> Reason is in org/apache/solr/ltr/model/LTRScoringModel.java:111
> {code:java}
> try {
>       // create an instance of the model
>       model = solrResourceLoader.newInstance(
>           className,
>           LTRScoringModel.class,
>           new String[0], // no sub packages
>           new Class[] { String.class, List.class, List.class, String.class, List.class, Map.class },
>           new Object[] { name, features, norms, featureStoreName, allFeatures, params });
>       if (params != null) {
>         SolrPluginUtils.invokeSetters(model, params.entrySet());
>       }
>     } catch (final Exception e) {
>       throw new ModelException("Model type does not exist " + className, e);
>     }
> {code}
> This happens when:
> - use a not existent feature store
> - use not existent feature
> - use an integer instead of Double as a weight in a linear model
> unless any objection, we should improve such message with the real one



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