Varun Thacker created SOLR-10377:
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Summary: Improve readability of the explain output for JSON format
Key: SOLR-10377
URL: https://issues.apache.org/jira/browse/SOLR-10377
Project: Solr
Issue Type: Improvement
Security Level: Public (Default Security Level. Issues are Public)
Reporter: Varun Thacker
Priority: Minor
Today when I ask solr for the debug query output In json with indent I get this:
{code}
1: " 3.545981 = sum of: 3.545981 = weight(name:dns in 0) [SchemaSimilarity],
result of: 3.545981 = score(doc=0,freq=1.0 = termFreq=1.0 ), product of:
2.3025851 = idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq +
0.5)) from: 2.0 = docFreq 24.0 = docCount 1.54 = tfNorm, computed as (freq *
(k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from: 1.0
= termFreq=1.0 1.2 = parameter k1 0.75 = parameter b 7.0 = avgFieldLength 1.0 =
fieldLength ",
2: " 7.4202514 = sum of: 7.4202514 = sum of: 2.7921112 = weight(name:domain in
1) [SchemaSimilarity], result of: 2.7921112 = score(doc=1,freq=1.0 =
termFreq=1.0 ), product of: 2.3025851 = idf, computed as log(1 + (docCount -
docFreq + 0.5) / (docFreq + 0.5)) from: 2.0 = docFreq 24.0 = docCount 1.2125984
= tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength
/ avgFieldLength)) from: 1.0 = termFreq=1.0 1.2 = parameter k1 0.75 = parameter
b 7.0 = avgFieldLength 4.0 = fieldLength 2.7921112 = weight(name:name in 1)
[SchemaSimilarity], result of: 2.7921112 = score(doc=1,freq=1.0 = termFreq=1.0
), product of: 2.3025851 = idf, computed as log(1 + (docCount - docFreq + 0.5)
/ (docFreq + 0.5)) from: 2.0 = docFreq 24.0 = docCount 1.2125984 = tfNorm,
computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength /
avgFieldLength)) from: 1.0 = termFreq=1.0 1.2 = parameter k1 0.75 = parameter b
7.0 = avgFieldLength 4.0 = fieldLength 1.8360289 = weight(name:system in 1)
[SchemaSimilarity], result of: 1.8360289 = score(doc=1,freq=1.0 = termFreq=1.0
), product of: 1.5141277 = idf, computed as log(1 + (docCount - docFreq + 0.5)
/ (docFreq + 0.5)) from: 5.0 = docFreq 24.0 = docCount 1.2125984 = tfNorm,
computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength /
avgFieldLength)) from: 1.0 = termFreq=1.0 1.2 = parameter k1 0.75 = parameter b
7.0 = avgFieldLength 4.0 = fieldLength "
{code}
When I run the same query with "wt=ruby" I get a much nicer output
{code}
'2'=>'
7.4202514 = sum of:
7.4202514 = sum of:
2.7921112 = weight(name:domain in 1) [SchemaSimilarity], result of:
2.7921112 = score(doc=1,freq=1.0 = termFreq=1.0
), product of:
2.3025851 = idf, computed as log(1 + (docCount - docFreq + 0.5) /
(docFreq + 0.5)) from:
2.0 = docFreq
24.0 = docCount
1.2125984 = tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b
+ b * fieldLength / avgFieldLength)) from:
1.0 = termFreq=1.0
1.2 = parameter k1
0.75 = parameter b
7.0 = avgFieldLength
4.0 = fieldLength
2.7921112 = weight(name:name in 1) [SchemaSimilarity], result of:
2.7921112 = score(doc=1,freq=1.0 = termFreq=1.0
), product of:
2.3025851 = idf, computed as log(1 + (docCount - docFreq + 0.5) /
(docFreq + 0.5)) from:
2.0 = docFreq
24.0 = docCount
1.2125984 = tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b
+ b * fieldLength / avgFieldLength)) from:
1.0 = termFreq=1.0
1.2 = parameter k1
0.75 = parameter b
7.0 = avgFieldLength
4.0 = fieldLength
1.8360289 = weight(name:system in 1) [SchemaSimilarity], result of:
1.8360289 = score(doc=1,freq=1.0 = termFreq=1.0
), product of:
1.5141277 = idf, computed as log(1 + (docCount - docFreq + 0.5) /
(docFreq + 0.5)) from:
5.0 = docFreq
24.0 = docCount
1.2125984 = tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b
+ b * fieldLength / avgFieldLength)) from:
1.0 = termFreq=1.0
1.2 = parameter k1
0.75 = parameter b
7.0 = avgFieldLength
4.0 = fieldLength
',
'1'=>'
3.545981 = sum of:
3.545981 = weight(name:dns in 0) [SchemaSimilarity], result of:
3.545981 = score(doc=0,freq=1.0 = termFreq=1.0
), product of:
2.3025851 = idf, computed as log(1 + (docCount - docFreq + 0.5) /
(docFreq + 0.5)) from:
2.0 = docFreq
24.0 = docCount
1.54 = tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b *
fieldLength / avgFieldLength)) from:
1.0 = termFreq=1.0
1.2 = parameter k1
0.75 = parameter b
7.0 = avgFieldLength
1.0 = fieldLength
'}
{code}
Also the explain for the JSON output is not sorted by score
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