If you add “&debugQuery=true” to the URL, you’ll get a JSON payload back for each row, which gives you the score computation, like so:
For solr, how ranking is computed is often more interesting than performance, but the same concepts for having a computation tree apply”
"ad7b44e1-44d5-4df1-a0fe-729633010c98": "
5.023878 = sum of:
4.100354 = weight(talk_day_i:`\b\u0000\u0000\u0000\u0004 in 5) [ClassicSimilarity], result of:
4.100354 = score(doc=5,freq=1.0), product of:
0.972982 = queryWeight, product of:
4.214214 = idf(docFreq=85, maxDocs=2140)
0.23088102 = queryNorm
4.214214 = fieldWeight in 5, product of:
1.0 = tf(freq=1.0), with freq of:
1.0 = termFreq=1.0
4.214214 = idf(docFreq=85, maxDocs=2140)
1.0 = fieldNorm(doc=5)
0.9235241 = FunctionQuery(int(talk_day_i)), product of:
4.0 = int(talk_day_i)=4
0.23088102 = boost
1.0 = queryNorm
You do also get the equivalent of analyze, and some information about how the query was prepared:
"rawquerystring": "talk_day_i:4",
"querystring": "talk_day_i:4",
"parsedquery": "(+talk_day_i:4 FunctionQuery(int(talk_day_i)))/no_coord",
"parsedquery_toString": "+talk_day_i:`\b\u0000\u0000\u0000\u0004 int(talk_day_i)",
"explain": {
...
},
"QParser": "ExtendedDismaxQParser",
"altquerystring": null,
"boost_queries": null,
"parsed_boost_queries": [],
"boostfuncs": [
"talk_day_i"
],
"timing": {
"time": 39,
"prepare": {
"time": 0,
"query": {
"time": 0
},
"facet": {
"time": 0
},
"facet_module": {
"time": 0
},
"mlt": {
"time": 0
},
"highlight": {
"time": 0
},
"stats": {
"time": 0
},
"expand": {
"time": 0
},
"debug": {
"time": 0
}
},
"process": {
"time": 38,
"query": {
"time": 0
},
"facet": {
"time": 0
},
"facet_module": {
"time": 0
},
"mlt": {
"time": 0
},
"highlight": {
"time": 0
},
"stats": {
"time": 0
},
"expand": {
"time": 0
},
"debug": {
"time": 38
}
}
}