Found it! I had to enable the ExactStatsCache
Found a description over here. Thanks for pointing me in the right direction. https://solr.pl/en/2019/05/20/distributed-idf/ On Sun, Dec 5, 2021 at 11:09 AM Sjoerd Smeets <ssme...@gmail.com> wrote: > Hi Allessandro, > > Thanks for your reply! Yes, the document are in the same result list and > I'm not doing any indexing at the moment and executed a commit just to be > sure. Still the same result. It is an environment with 4 shards. Perhaps > that plays a factor? > > Thanks, > Sjoerd > > On Sun, Dec 5, 2021 at 11:02 AM Alessandro Benedetti <a.benede...@sease.io> > wrote: > >> It's seems like the underline index changed. >> Are those two documents in the same result set? >> Is it just one query? >> It's definitely curious, even if a commit happened search results are >> consistent in one searcher. >> >> >> On Sun, 5 Dec 2021, 16:28 Sjoerd Smeets, <ssme...@gmail.com> wrote: >> >>> Hi all, >>> >>> I'm debugging the relevancy scores of my query and I see the following >>> for >>> two documents hits. My question is, why is the idf score not the same for >>> both documents? This is Solr 6.6. >>> >>> Any guidance would be much appreciated. >>> >>> Thanks! >>> >>> *Doc1* >>> "71d72354eea23b9eae934ab616e8ce38de69d760": " >>> 104.994415 = sum of: >>> 104.994415 = sum of: >>> 82.89969 = weight(stemmed_data.timenote.narratives:remedi in 22470) >>> [SchemaSimilarity], result of: >>> 82.89969 = score(freq=9.0), computed as boost * idf * tf from: >>> 100.0 = boost >>> 0.87546873 = idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) >>> from: >>> *52 = n, number of documents containing term* >>> *125 = N, total number of documents with field* >>> 0.9469177 = tf, computed as freq / (freq + k1 * (1 - b + b * dl / >>> avgdl)) from: >>> 9.0 = freq, occurrences of term within document >>> 1.2 = k1, term saturation parameter >>> 0.75 = b, length normalization parameter >>> 12312.0 = dl, length of field (approximate) >>> 54179.03 = avgdl, average length of field >>> 22.09473 = weight(stemmed_data.timenote.matters:remedi in 22470) >>> [SchemaSimilarity], result of: >>> 22.09473 = score(freq=4.0), computed as boost * idf * tf from: >>> 10.0 = boost >>> 2.4308395 = idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) >>> from: >>> *9 = n, number of documents containing term* >>> *107 = N, total number of documents with field* >>> 0.9089341 = tf, computed as freq / (freq + k1 * (1 - b + b * dl / >>> avgdl)) from: >>> 4.0 = freq, occurrences of term within document >>> 1.2 = k1, term saturation parameter >>> 0.75 = b, length normalization parameter >>> 5656.0 = dl, length of field (approximate) >>> 50520.543 = avgdl, average length of field >>> 0.0 = FunctionQuery(int(s_integer_search.previews)), product of: >>> 0.0 = int(s_integer_search.previews)=0 >>> 1.0 = boost >>> 0.0 = FunctionQuery(int(s_integer_search.downloads)), product of: >>> 0.0 = int(s_integer_search.downloads)=0 >>> 1.0 = boost >>> " >>> >>> *Doc2* >>> "80302a1ecc44d1e556970ab96c25b1fd3328a854": " >>> 84.61461 = sum of: >>> 84.61461 = sum of: >>> 64.68881 = weight(stemmed_data.timenote.narratives:remedi in 0) >>> [SchemaSimilarity], result of: >>> 64.68881 = score(freq=493.0), computed as boost * idf * tf from: >>> 100.0 = boost >>> 0.65094686 = idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) >>> from: >>> *60 = n, number of documents containing term* >>> *115 = N, total number of documents with field* >>> 0.99376476 = tf, computed as freq / (freq + k1 * (1 - b + b * dl >>> / >>> avgdl)) from: >>> 493.0 = freq, occurrences of term within document >>> 1.2 = k1, term saturation parameter >>> 0.75 = b, length normalization parameter >>> 229400.0 = dl, length of field (approximate) >>> 73913.91 = avgdl, average length of field >>> 19.9258 = weight(stemmed_data.timenote.matters:remedi in 0) >>> [SchemaSimilarity], result of: >>> 19.9258 = score(freq=340.0), computed as boost * idf * tf from: >>> 10.0 = boost >>> 2.0024805 = idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) >>> from: >>> *13 = n, number of documents containing term* >>> *99 = N, total number of documents with field* >>> 0.99505585 = tf, computed as freq / (freq + k1 * (1 - b + b * dl >>> / >>> avgdl)) from: >>> 340.0 = freq, occurrences of term within document >>> 1.2 = k1, term saturation parameter >>> 0.75 = b, length normalization parameter >>> 147480.0 = dl, length of field (approximate) >>> 95534.95 = avgdl, average length of field >>> 0.0 = FunctionQuery(int(s_integer_search.previews)), product of: >>> 0.0 = int(s_integer_search.previews)=0 >>> 1.0 = boost >>> 0.0 = FunctionQuery(int(s_integer_search.downloads)), product of: >>> 0.0 = int(s_integer_search.downloads)=0 >>> 1.0 = boost >>> " >>> >>
