As we can see, the time is similar with both methods. If most of the time was due to reranking, we should have seen a great reduction in qTime, which is not the case.
The usage of this dummy model allows us to estimate the features extraction time spent.
What happens when you extract more or fewer features for your model reranking?
We can notice two behaviors:
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- The more features Apache Solr extracts for the model, the more time is required to execute the query
- The greater the number of documents we want to rerank, the more time is required to execute the query. The greater the number of documents we want to rerank, the greater is the number of times we execute features extraction and scoring(once for each document).
This may sound obvious but let’s validate that with the dummy model approach:
The first behavior is reflected by the qTime necessary to execute reranking on 10 documents (rerank docs = 10) with the dummy model with few features (qTime = 10) and the qTime necessary to execute reranking on 10 documents with the dummy model with many features (qTime = 210).
The second behavior is reflected by the qTime necessary to execute reranking on 10 documents (rerank docs = 10) with the dummy model with many features (qTime = 210) and the qTime necessary to execute reranking on 10000 documents with the same dummy model (qTime = 6726).