Elasticsearch gives different scores for same documents - elasticsearch

Question

I have some documents which have the same content but when I try to query for these documents, I am getting different scores although the queried field contains the same text. I have explained the scores but I am not able to analyse and find the reason for different scores.
My query is
curl 'localhost:9200/acqindex/_search?pretty=1' -d '{
"explain" : true,
"query" : {
"query_string" : {
"query" : "text:shimla"
}
}
}'
Search response :
{
"took" : 8,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"hits" : {
"total" : 31208,
"max_score" : 268.85962,
"hits" : [ {
"_shard" : 0,
"_node" : "KOebAnGhSJKUHLPNxndcpQ",
"_index" : "acqindex",
"_type" : "autocomplete_questions",
"_id" : "50efec6c38cc6fdabd8653a3",
"_score" : 268.85962, "_source" : {"_class":"com.ixigo.next.cms.model.AutoCompleteObject","_id":"50efec6c38cc6fdabd8653a3","ad":"rajasthan,IN","category":["Destination"],"ctype":"destination","eid":"503b2a65e4b032e338f0d24b","po":8.772307692307692,"text":"shimla","url":"/travel-guide/shimla"},
"_explanation" : {
"value" : 268.85962,
"description" : "sum of:",
"details" : [ {
"value" : 38.438133,
"description" : "weight(text:shi in 5860), product of:",
"details" : [ {
"value" : 0.37811017,
"description" : "queryWeight(text:shi), product of:",
"details" : [ {
"value" : 5.0829277,
"description" : "idf(docFreq=7503, maxDocs=445129)"
}, {
"value" : 0.074388266,
"description" : "queryNorm"
} ]
}, {
"value" : 101.658554,
"description" : "fieldWeight(text:shi in 5860), product of:",
"details" : [ {
"value" : 1.0,
"description" : "tf(termFreq(text:shi)=1)"
}, {
"value" : 5.0829277,
"description" : "idf(docFreq=7503, maxDocs=445129)"
}, {
"value" : 20.0,
"description" : "fieldNorm(field=text, doc=5860)"
} ]
} ]
}, {
"value" : 66.8446,
"description" : "weight(text:shim in 5860), product of:",
"details" : [ {
"value" : 0.49862078,
"description" : "queryWeight(text:shim), product of:",
"details" : [ {
"value" : 6.7029495,
"description" : "idf(docFreq=1484, maxDocs=445129)"
}, {
"value" : 0.074388266,
"description" : "queryNorm"
} ]
}, {
"value" : 134.05899,
"description" : "fieldWeight(text:shim in 5860), product of:",
"details" : [ {
"value" : 1.0,
"description" : "tf(termFreq(text:shim)=1)"
}, {
"value" : 6.7029495,
"description" : "idf(docFreq=1484, maxDocs=445129)"
}, {
"value" : 20.0,
"description" : "fieldNorm(field=text, doc=5860)"
} ]
} ]
}, {
"value" : 81.75818,
"description" : "weight(text:shiml in 5860), product of:",
"details" : [ {
"value" : 0.5514458,
"description" : "queryWeight(text:shiml), product of:",
"details" : [ {
"value" : 7.413075,
"description" : "idf(docFreq=729, maxDocs=445129)"
}, {
"value" : 0.074388266,
"description" : "queryNorm"
} ]
}, {
"value" : 148.2615,
"description" : "fieldWeight(text:shiml in 5860), product of:",
"details" : [ {
"value" : 1.0,
"description" : "tf(termFreq(text:shiml)=1)"
}, {
"value" : 7.413075,
"description" : "idf(docFreq=729, maxDocs=445129)"
}, {
"value" : 20.0,
"description" : "fieldNorm(field=text, doc=5860)"
} ]
} ]
}, {
"value" : 81.8187,
"description" : "weight(text:shimla in 5860), product of:",
"details" : [ {
"value" : 0.55164987,
"description" : "queryWeight(text:shimla), product of:",
"details" : [ {
"value" : 7.415818,
"description" : "idf(docFreq=727, maxDocs=445129)"
}, {
"value" : 0.074388266,
"description" : "queryNorm"
} ]
}, {
"value" : 148.31636,
"description" : "fieldWeight(text:shimla in 5860), product of:",
"details" : [ {
"value" : 1.0,
"description" : "tf(termFreq(text:shimla)=1)"
}, {
"value" : 7.415818,
"description" : "idf(docFreq=727, maxDocs=445129)"
}, {
"value" : 20.0,
"description" : "fieldNorm(field=text, doc=5860)"
} ]
} ]
} ]
}
}, {
"_shard" : 1,
"_node" : "KOebAnGhSJKUHLPNxndcpQ",
"_index" : "acqindex",
"_type" : "autocomplete_questions",
"_id" : "50efed1c38cc6fdabd8b8d2f",
"_score" : 268.29953, "_source" : {"_id":"50efed1c38cc6fdabd8b8d2f","ad":"himachal pradesh,IN","category":["Hill","See and Do","Destination","Mountain","Nature and Wildlife"],"ctype":"destination","eid":"503b2a64e4b032e338f0d0af","po":8.781970310391364,"text":"shimla","url":"/travel-guide/shimla"},
"_explanation" : {
"value" : 268.29953,
"description" : "sum of:",
"details" : [ {
"value" : 38.52957,
"description" : "weight(text:shi in 14769), product of:",
"details" : [ {
"value" : 0.37895453,
"description" : "queryWeight(text:shi), product of:",
"details" : [ {
"value" : 5.083667,
"description" : "idf(docFreq=7263, maxDocs=431211)"
}, {
"value" : 0.07454354,
"description" : "queryNorm"
} ]
}, {
"value" : 101.67334,
"description" : "fieldWeight(text:shi in 14769), product of:",
"details" : [ {
"value" : 1.0,
"description" : "tf(termFreq(text:shi)=1)"
}, {
"value" : 5.083667,
"description" : "idf(docFreq=7263, maxDocs=431211)"
}, {
"value" : 20.0,
"description" : "fieldNorm(field=text, doc=14769)"
} ]
} ]
}, {
"value" : 66.67524,
"description" : "weight(text:shim in 14769), product of:",
"details" : [ {
"value" : 0.49850821,
"description" : "queryWeight(text:shim), product of:",
"details" : [ {
"value" : 6.6874766,
"description" : "idf(docFreq=1460, maxDocs=431211)"
}, {
"value" : 0.07454354,
"description" : "queryNorm"
} ]
}, {
"value" : 133.74953,
"description" : "fieldWeight(text:shim in 14769), product of:",
"details" : [ {
"value" : 1.0,
"description" : "tf(termFreq(text:shim)=1)"
}, {
"value" : 6.6874766,
"description" : "idf(docFreq=1460, maxDocs=431211)"
}, {
"value" : 20.0,
"description" : "fieldNorm(field=text, doc=14769)"
} ]
} ]
}, {
"value" : 81.53204,
"description" : "weight(text:shiml in 14769), product of:",
"details" : [ {
"value" : 0.5512571,
"description" : "queryWeight(text:shiml), product of:",
"details" : [ {
"value" : 7.3951015,
"description" : "idf(docFreq=719, maxDocs=431211)"
}, {
"value" : 0.07454354,
"description" : "queryNorm"
} ]
}, {
"value" : 147.90204,
"description" : "fieldWeight(text:shiml in 14769), product of:",
"details" : [ {
"value" : 1.0,
"description" : "tf(termFreq(text:shiml)=1)"
}, {
"value" : 7.3951015,
"description" : "idf(docFreq=719, maxDocs=431211)"
}, {
"value" : 20.0,
"description" : "fieldNorm(field=text, doc=14769)"
} ]
} ]
}, {
"value" : 81.56268,
"description" : "weight(text:shimla in 14769), product of:",
"details" : [ {
"value" : 0.55136067,
"description" : "queryWeight(text:shimla), product of:",
"details" : [ {
"value" : 7.3964915,
"description" : "idf(docFreq=718, maxDocs=431211)"
}, {
"value" : 0.07454354,
"description" : "queryNorm"
} ]
}, {
"value" : 147.92982,
"description" : "fieldWeight(text:shimla in 14769), product of:",
"details" : [ {
"value" : 1.0,
"description" : "tf(termFreq(text:shimla)=1)"
}, {
"value" : 7.3964915,
"description" : "idf(docFreq=718, maxDocs=431211)"
}, {
"value" : 20.0,
"description" : "fieldNorm(field=text, doc=14769)"
} ]
} ]
} ]
}
}
}
}
The documents are :
{"_class":"com.ixigo.next.cms.model.AutoCompleteObject","_id":"50efec6c38cc6fdabd8653a3","ad":"rajasthan,IN","category":["Destination"],"ctype":"destination","eid":"503b2a65e4b032e338f0d24b","po":8.772307692307692,"text":"shimla","url":"/travel-guide/shimla"}
{"_id":"50efed1c38cc6fdabd8b8d2f","ad":"himachal
pradesh,IN","category":["Hill","See and
Do","Destination","Mountain","Nature and Wildlife"],"ctype":"destination","eid":"503b2a64e4b032e338f0d0af","po":8.781970310391364,"text":"shimla","url":"/travel-guide/shimla"}
Please guide me in understanding the reason for the difference in scores.

Answer 1

The lucene score depends on different factors. Using the tf idf similarity (default one) it mainly depends on:
Term frequency: how much the terms found are frequent within the document
Inverted document frequency: how much the terms found appear among the documents (while index)
Field norms (including index time boosting). Shorter fields get higher score than longer ones.
In your case you have to take into account that your two documents come from different shards, thus the score is computed separately on each of those, since every shard is in fact a separate lucene index.
You might want to have a look at the more expensive DFS, Query then Fetch search type that elasticsearch provides for more accurate scoring. The default one is the simple Query then Fetch.

Answer 2

javanna clearly pointed out the problem indicating that a difference of scores comes from the fact that scoring happens in multiple shards. Those shards may have different number of documents. This affects scoring algorithm.
However, authors of Elasticsearch: The Definitive Guide inform:
The differences between local and global IDF [inverse document frequency] diminish the more documents that you add to the index. With real-world volumes of data, the local IDFs soon even out. The problem is not that relevance is broken but that there is too little data.
You should not use dfs_query_then_fetch on production. For testing, put your index on one primary shard or specify ?search_type=dfs_query_then_fetch.