Why aren't my string _boost values sorting correctly?

Elastic Stack Elasticsearch
1

Below is my complete curl recreation. I expect the _boost values to sort
the documents nicely from 5 to 1. However I get inconsistent results. If i
run this recreation 10 times, I will get a variety of sort orders! Mostly
out of order, sometimes in order etc, but why the inconsistency?

I understand that many factors contribute to the score, not just _boost,
but all these documents are near identical right? How would term frequency
or document length skew the scoring if the docs are the same?

My goal is to index many terms inside the skills field (hence the array) so
I cannot simply move the boost to the document level. I need the boost to
be on a specific term so when I search for that term the scoring should
reflect that specific term's boost value.

What am I doing wrong?

curl -XDELETE 'http://localhost:9200/p'
curl -XPOST 'http://localhost:9200/p'

curl -XPOST 'localhost:9200/p/profile/_mapping' -d '
{
"profile": {
"properties": {
"skills": {
"type": "string",
"omit_term_freq_and_positions" : true,
"index" : "not_analyzed"
}
}
}
}'

curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "ruby", "_boost": 5 } ]
}'
curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "ruby", "_boost": 4 } ]
}'
curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "ruby", "_boost": 1 } ]
}'
curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "ruby", "_boost": 3 } ]
}'
curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "ruby", "_boost": 2 } ]
}'
curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "python", "_boost": 100 } ]
}'

curl -XPOST "http://localhost:9200/p/_refresh"

curl -XGET 'localhost:9200/p/_search?pretty=true' -d '{
"query": {
"query_string": {
"query": "ruby"
}
},
"sort": [{"_score": "desc"}],
"explain": true
}'

----------------------- OUTPUT -----------------------

{
"took" : 22,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"hits" : {
"total" : 5,
"max_score" : 2.828427,
"hits" : [ {
"_shard" : 3,
"_node" : "eunz7yXoQvKYGfCbAsltYw",
"_index" : "p",
"_type" : "profile",
"_id" : "2JV5NJYOQoSgUr59XulocA",
"_score" : 2.828427, "_source" : {
"skills": [ { "_value": "ruby", "_boost": 4 } ]
},
"_explanation" : {
"value" : 2.828427,
"description" : "fieldWeight(_all:ruby in 0), product of:",
"details" : [ {
"value" : 2.828427,
"description" : "btq, product of:",
"details" : [ {
"value" : 0.70710677,
"description" : "tf(phraseFreq=0.5)"
}, {
"value" : 4.0,
"description" : "allPayload(...)"
} ]
}, {
"value" : 1.0,
"description" : "idf(_all: ruby=2)"
}, {
"value" : 1.0,
"description" : "fieldNorm(field=_all, doc=0)"
} ]
}
}, {
"_shard" : 4,
"_node" : "eunz7yXoQvKYGfCbAsltYw",
"_index" : "p",
"_type" : "profile",
"_id" : "fuMfbcDcRTqEp3GGSmtUlA",
"_score" : 2.101998, "_source" : {
"skills": [ { "_value": "ruby", "_boost": 5 } ]
},
"_explanation" : {
"value" : 2.1019979,
"description" : "weight(_all:ruby in 0), product of:",
"details" : [ {
"value" : 0.99999994,
"description" : "queryWeight(_all:ruby), product of:",
"details" : [ {
"value" : 0.5945349,
"description" : "idf(_all: ruby=2)"
}, {
"value" : 1.681987,
"description" : "queryNorm"
} ]
}, {
"value" : 2.101998,
"description" : "fieldWeight(_all:ruby in 0), product of:",
"details" : [ {
"value" : 3.535534,
"description" : "btq, product of:",
"details" : [ {
"value" : 0.70710677,
"description" : "tf(phraseFreq=0.5)"
}, {
"value" : 5.0,
"description" : "allPayload(...)"
} ]
}, {
"value" : 0.5945349,
"description" : "idf(_all: ruby=2)"
}, {
"value" : 1.0,
"description" : "fieldNorm(field=_all, doc=0)"
} ]
} ]
}
}, {
"_shard" : 4,
"_node" : "eunz7yXoQvKYGfCbAsltYw",
"_index" : "p",
"_type" : "profile",
"_id" : "GSrNarYwSSm99RPx_XCfSA",
"_score" : 1.2611988, "_source" : {
"skills": [ { "_value": "ruby", "_boost": 3 } ]
},
"_explanation" : {
"value" : 1.2611988,
"description" : "weight(_all:ruby in 1), product of:",
"details" : [ {
"value" : 0.99999994,
"description" : "queryWeight(_all:ruby), product of:",
"details" : [ {
"value" : 0.5945349,
"description" : "idf(_all: ruby=2)"
}, {
"value" : 1.681987,
"description" : "queryNorm"
} ]
}, {
"value" : 1.2611989,
"description" : "fieldWeight(_all:ruby in 1), product of:",
"details" : [ {
"value" : 2.1213202,
"description" : "btq, product of:",
"details" : [ {
"value" : 0.70710677,
"description" : "tf(phraseFreq=0.5)"
}, {
"value" : 3.0,
"description" : "allPayload(...)"
} ]
}, {
"value" : 0.5945349,
"description" : "idf(_all: ruby=2)"
}, {
"value" : 1.0,
"description" : "fieldNorm(field=_all, doc=1)"
} ]
} ]
}
}, {
"_shard" : 3,
"_node" : "eunz7yXoQvKYGfCbAsltYw",
"_index" : "p",
"_type" : "profile",
"_id" : "L5LiRwf7Tr67KAwiz6Rrig",
"_score" : 0.70710677, "_source" : {
"skills": [ { "_value": "ruby", "_boost": 1 } ]
},
"_explanation" : {
"value" : 0.70710677,
"description" : "fieldWeight(_all:ruby in 1), product of:",
"details" : [ {
"value" : 0.70710677,
"description" : "btq, product of:",
"details" : [ {
"value" : 0.70710677,
"description" : "tf(phraseFreq=0.5)"
}, {
"value" : 1.0,
"description" : "allPayload(...)"
} ]
}, {
"value" : 1.0,
"description" : "idf(_all: ruby=2)"
}, {
"value" : 1.0,
"description" : "fieldNorm(field=_all, doc=1)"
} ]
}
}, {
"_shard" : 0,
"_node" : "eunz7yXoQvKYGfCbAsltYw",
"_index" : "p",
"_type" : "profile",
"_id" : "PNx2hHtVShq4ZJbQAEKqaA",
"_score" : 0.4339554, "_source" : {
"skills": [ { "_value": "ruby", "_boost": 2 } ]
},
"_explanation" : {
"value" : 0.4339554,
"description" : "fieldWeight(_all:ruby in 0), product of:",
"details" : [ {
"value" : 1.4142135,
"description" : "btq, product of:",
"details" : [ {
"value" : 0.70710677,
"description" : "tf(phraseFreq=0.5)"
}, {
"value" : 2.0,
"description" : "allPayload(...)"
} ]
}, {
"value" : 0.30685282,
"description" : "idf(_all: ruby=1)"
}, {
"value" : 1.0,
"description" : "fieldNorm(field=_all, doc=0)"
} ]
}
} ]
}
}

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2

Hi Jade,

In the case you just index a few documents into an index, the documents
tend to be unbalanced between shards,
which I think happened here as well. What you can do to make your test pass
is to set the query string parameter: search_type=dfs_query_then_fetch
in your search request. This enables a distributed document frequency phase
before the actual query is executed and makes sure that for the idf factor
cross shard frequencies are taken into account.

Normally the default search_type is fine, because usually enough data has
been indexed and therefore the documents are more or less balanced between
shards.

Martijn

On 9 February 2013 08:21, Jade Dominguez superjadex12@gmail.com wrote:

Below is my complete curl recreation. I expect the _boost values to sort
the documents nicely from 5 to 1. However I get inconsistent results. If i
run this recreation 10 times, I will get a variety of sort orders! Mostly
out of order, sometimes in order etc, but why the inconsistency?

I understand that many factors contribute to the score, not just _boost,
but all these documents are near identical right? How would term frequency
or document length skew the scoring if the docs are the same?

My goal is to index many terms inside the skills field (hence the array)
so I cannot simply move the boost to the document level. I need the boost
to be on a specific term so when I search for that term the scoring should
reflect that specific term's boost value.

What am I doing wrong?

curl -XDELETE 'http://localhost:9200/p'
curl -XPOST 'http://localhost:9200/p'

curl -XPOST 'localhost:9200/p/profile/_mapping' -d '
{
"profile": {
"properties": {
"skills": {
"type": "string",
"omit_term_freq_and_positions" : true,
"index" : "not_analyzed"
}
}
}
}'

curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "ruby", "_boost": 5 } ]
}'
curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "ruby", "_boost": 4 } ]
}'
curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "ruby", "_boost": 1 } ]
}'
curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "ruby", "_boost": 3 } ]
}'
curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "ruby", "_boost": 2 } ]
}'
curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "python", "_boost": 100 } ]
}'

curl -XPOST "http://localhost:9200/p/_refresh"

curl -XGET 'localhost:9200/p/_search?pretty=true' -d '{
"query": {
"query_string": {
"query": "ruby"
}
},
"sort": [{"_score": "desc"}],
"explain": true
}'

----------------------- OUTPUT -----------------------

{
"took" : 22,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"hits" : {
"total" : 5,
"max_score" : 2.828427,
"hits" : [ {
"_shard" : 3,
"_node" : "eunz7yXoQvKYGfCbAsltYw",
"_index" : "p",
"_type" : "profile",
"_id" : "2JV5NJYOQoSgUr59XulocA",
"_score" : 2.828427, "_source" : {
"skills": [ { "_value": "ruby", "_boost": 4 } ]
},
"_explanation" : {
"value" : 2.828427,
"description" : "fieldWeight(_all:ruby in 0), product of:",
"details" : [ {
"value" : 2.828427,
"description" : "btq, product of:",
"details" : [ {
"value" : 0.70710677,
"description" : "tf(phraseFreq=0.5)"
}, {
"value" : 4.0,
"description" : "allPayload(...)"
} ]
}, {
"value" : 1.0,
"description" : "idf(_all: ruby=2)"
}, {
"value" : 1.0,
"description" : "fieldNorm(field=_all, doc=0)"
} ]
}
}, {
"_shard" : 4,
"_node" : "eunz7yXoQvKYGfCbAsltYw",
"_index" : "p",
"_type" : "profile",
"_id" : "fuMfbcDcRTqEp3GGSmtUlA",
"_score" : 2.101998, "_source" : {
"skills": [ { "_value": "ruby", "_boost": 5 } ]
},
"_explanation" : {
"value" : 2.1019979,
"description" : "weight(_all:ruby in 0), product of:",
"details" : [ {
"value" : 0.99999994,
"description" : "queryWeight(_all:ruby), product of:",
"details" : [ {
"value" : 0.5945349,
"description" : "idf(_all: ruby=2)"
}, {
"value" : 1.681987,
"description" : "queryNorm"
} ]
}, {
"value" : 2.101998,
"description" : "fieldWeight(_all:ruby in 0), product of:",
"details" : [ {
"value" : 3.535534,
"description" : "btq, product of:",
"details" : [ {
"value" : 0.70710677,
"description" : "tf(phraseFreq=0.5)"
}, {
"value" : 5.0,
"description" : "allPayload(...)"
} ]
}, {
"value" : 0.5945349,
"description" : "idf(_all: ruby=2)"
}, {
"value" : 1.0,
"description" : "fieldNorm(field=_all, doc=0)"
} ]
} ]
}
}, {
"_shard" : 4,
"_node" : "eunz7yXoQvKYGfCbAsltYw",
"_index" : "p",
"_type" : "profile",
"_id" : "GSrNarYwSSm99RPx_XCfSA",
"_score" : 1.2611988, "_source" : {
"skills": [ { "_value": "ruby", "_boost": 3 } ]
},
"_explanation" : {
"value" : 1.2611988,
"description" : "weight(_all:ruby in 1), product of:",
"details" : [ {
"value" : 0.99999994,
"description" : "queryWeight(_all:ruby), product of:",
"details" : [ {
"value" : 0.5945349,
"description" : "idf(_all: ruby=2)"
}, {
"value" : 1.681987,
"description" : "queryNorm"
} ]
}, {
"value" : 1.2611989,
"description" : "fieldWeight(_all:ruby in 1), product of:",
"details" : [ {
"value" : 2.1213202,
"description" : "btq, product of:",
"details" : [ {
"value" : 0.70710677,
"description" : "tf(phraseFreq=0.5)"
}, {
"value" : 3.0,
"description" : "allPayload(...)"
} ]
}, {
"value" : 0.5945349,
"description" : "idf(_all: ruby=2)"
}, {
"value" : 1.0,
"description" : "fieldNorm(field=_all, doc=1)"
} ]
} ]
}
}, {
"_shard" : 3,
"_node" : "eunz7yXoQvKYGfCbAsltYw",
"_index" : "p",
"_type" : "profile",
"_id" : "L5LiRwf7Tr67KAwiz6Rrig",
"_score" : 0.70710677, "_source" : {
"skills": [ { "_value": "ruby", "_boost": 1 } ]
},
"_explanation" : {
"value" : 0.70710677,
"description" : "fieldWeight(_all:ruby in 1), product of:",
"details" : [ {
"value" : 0.70710677,
"description" : "btq, product of:",
"details" : [ {
"value" : 0.70710677,
"description" : "tf(phraseFreq=0.5)"
}, {
"value" : 1.0,
"description" : "allPayload(...)"
} ]
}, {
"value" : 1.0,
"description" : "idf(_all: ruby=2)"
}, {
"value" : 1.0,
"description" : "fieldNorm(field=_all, doc=1)"
} ]
}
}, {
"_shard" : 0,
"_node" : "eunz7yXoQvKYGfCbAsltYw",
"_index" : "p",
"_type" : "profile",
"_id" : "PNx2hHtVShq4ZJbQAEKqaA",
"_score" : 0.4339554, "_source" : {
"skills": [ { "_value": "ruby", "_boost": 2 } ]
},
"_explanation" : {
"value" : 0.4339554,
"description" : "fieldWeight(_all:ruby in 0), product of:",
"details" : [ {
"value" : 1.4142135,
"description" : "btq, product of:",
"details" : [ {
"value" : 0.70710677,
"description" : "tf(phraseFreq=0.5)"
}, {
"value" : 2.0,
"description" : "allPayload(...)"
} ]
}, {
"value" : 0.30685282,
"description" : "idf(_all: ruby=1)"
}, {
"value" : 1.0,
"description" : "fieldNorm(field=_all, doc=0)"
} ]
}
} ]
}
}

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Met vriendelijke groet,

Martijn van Groningen

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3

Martijn, this is great, thank you! It works =)

Your answer makes very logical sense.

Here is the documentation on search types and my working search query to
officially answer this question:

Parameter value: dfs_query_then_fetch.

Same as “Query Then Fetch”, except for an initial scatter phase which goes
and computes the distributed term frequencies for more accurate scoring.

curl -XGET
'localhost:9200/p/_search?&search_type=dfs_query_then_fetch&pretty=true' -d
'{
"query": {
"query_string": {
"query": "ruby"
}
},
"sort": [{"_score": "desc"}],
"explain": true
}'

Thank you for your help!

  • Jade

On Monday, February 11, 2013 1:34:41 AM UTC-8, Martijn v Groningen wrote:

Hi Jade,

In the case you just index a few documents into an index, the documents
tend to be unbalanced between shards,
which I think happened here as well. What you can do to make your test
pass is to set the query string parameter:
search_type=dfs_query_then_fetch
in your search request. This enables a distributed document frequency
phase before the actual query is executed and makes sure that for the idf
factor cross shard frequencies are taken into account.

Normally the default search_type is fine, because usually enough data has
been indexed and therefore the documents are more or less balanced between
shards.

Martijn

On 9 February 2013 08:21, Jade Dominguez <superj...@gmail.com<javascript:>

wrote:

Below is my complete curl recreation. I expect the _boost values to sort
the documents nicely from 5 to 1. However I get inconsistent results. If i
run this recreation 10 times, I will get a variety of sort orders! Mostly
out of order, sometimes in order etc, but why the inconsistency?

I understand that many factors contribute to the score, not just _boost,
but all these documents are near identical right? How would term frequency
or document length skew the scoring if the docs are the same?

My goal is to index many terms inside the skills field (hence the array)
so I cannot simply move the boost to the document level. I need the boost
to be on a specific term so when I search for that term the scoring should
reflect that specific term's boost value.

What am I doing wrong?

curl -XDELETE 'http://localhost:9200/p'
curl -XPOST 'http://localhost:9200/p'

curl -XPOST 'localhost:9200/p/profile/_mapping' -d '
{
"profile": {
"properties": {
"skills": {
"type": "string",
"omit_term_freq_and_positions" : true,
"index" : "not_analyzed"
}
}
}
}'

curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "ruby", "_boost": 5 } ]
}'
curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "ruby", "_boost": 4 } ]
}'
curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "ruby", "_boost": 1 } ]
}'
curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "ruby", "_boost": 3 } ]
}'
curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "ruby", "_boost": 2 } ]
}'
curl -XPOST localhost:9200/p/profile -d '{
"skills": [ { "_value": "python", "_boost": 100 } ]
}'

curl -XPOST "http://localhost:9200/p/_refresh"

curl -XGET 'localhost:9200/p/_search?pretty=true' -d '{
"query": {
"query_string": {
"query": "ruby"
}
},
"sort": [{"_score": "desc"}],
"explain": true
}'

----------------------- OUTPUT -----------------------

{
"took" : 22,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"hits" : {
"total" : 5,
"max_score" : 2.828427,
"hits" : [ {
"_shard" : 3,
"_node" : "eunz7yXoQvKYGfCbAsltYw",
"_index" : "p",
"_type" : "profile",
"_id" : "2JV5NJYOQoSgUr59XulocA",
"_score" : 2.828427, "_source" : {
"skills": [ { "_value": "ruby", "_boost": 4 } ]
},
"_explanation" : {
"value" : 2.828427,
"description" : "fieldWeight(_all:ruby in 0), product of:",
"details" : [ {
"value" : 2.828427,
"description" : "btq, product of:",
"details" : [ {
"value" : 0.70710677,
"description" : "tf(phraseFreq=0.5)"
}, {
"value" : 4.0,
"description" : "allPayload(...)"
} ]
}, {
"value" : 1.0,
"description" : "idf(_all: ruby=2)"
}, {
"value" : 1.0,
"description" : "fieldNorm(field=_all, doc=0)"
} ]
}
}, {
"_shard" : 4,
"_node" : "eunz7yXoQvKYGfCbAsltYw",
"_index" : "p",
"_type" : "profile",
"_id" : "fuMfbcDcRTqEp3GGSmtUlA",
"_score" : 2.101998, "_source" : {
"skills": [ { "_value": "ruby", "_boost": 5 } ]
},
"_explanation" : {
"value" : 2.1019979,
"description" : "weight(_all:ruby in 0), product of:",
"details" : [ {
"value" : 0.99999994,
"description" : "queryWeight(_all:ruby), product of:",
"details" : [ {
"value" : 0.5945349,
"description" : "idf(_all: ruby=2)"
}, {
"value" : 1.681987,
"description" : "queryNorm"
} ]
}, {
"value" : 2.101998,
"description" : "fieldWeight(_all:ruby in 0), product of:",
"details" : [ {
"value" : 3.535534,
"description" : "btq, product of:",
"details" : [ {
"value" : 0.70710677,
"description" : "tf(phraseFreq=0.5)"
}, {
"value" : 5.0,
"description" : "allPayload(...)"
} ]
}, {
"value" : 0.5945349,
"description" : "idf(_all: ruby=2)"
}, {
"value" : 1.0,
"description" : "fieldNorm(field=_all, doc=0)"
} ]
} ]
}
}, {
"_shard" : 4,
"_node" : "eunz7yXoQvKYGfCbAsltYw",
"_index" : "p",
"_type" : "profile",
"_id" : "GSrNarYwSSm99RPx_XCfSA",
"_score" : 1.2611988, "_source" : {
"skills": [ { "_value": "ruby", "_boost": 3 } ]
},
"_explanation" : {
"value" : 1.2611988,
"description" : "weight(_all:ruby in 1), product of:",
"details" : [ {
"value" : 0.99999994,
"description" : "queryWeight(_all:ruby), product of:",
"details" : [ {
"value" : 0.5945349,
"description" : "idf(_all: ruby=2)"
}, {
"value" : 1.681987,
"description" : "queryNorm"
} ]
}, {
"value" : 1.2611989,
"description" : "fieldWeight(_all:ruby in 1), product of:",
"details" : [ {
"value" : 2.1213202,
"description" : "btq, product of:",
"details" : [ {
"value" : 0.70710677,
"description" : "tf(phraseFreq=0.5)"
}, {
"value" : 3.0,
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