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 } } }