Analyzed string fields are also multi-value fields, but sorting on them seldom
gives you the results you want. If you analyze a string like "fine old art"
,
it results in three terms. We probably want to sort alphabetically on the
first term, then the second term, etc, but Elasticsearch doesn’t have this
information at its disposal at sort time.
You could use the min
and max
sort modes (it uses min
by default) but
that will result in sorting on either art
or old
, neither of which was the
intent.
In order to sort on a string field, that field should contain one term only:
the whole not_analyzed
string. But of course we still need the field to be
analyzed
in order to be able to query it as full text.
The naive approach to indexing the same string in two ways would be to include
two separate fields in the document: one which is analyzed
for searching,
and one which is not_analyzed
for sorting.
But storing the same string twice in the _source
field is waste of space.
What we really want to do is to pass in a single field but to index it in
two different ways. All of the `core'' field types — strings, numbers,
booleans, dates — accept a `fields
parameter which allows you to transform a
simple mapping like:
"tweet": {
"type": "string",
"analyzer": "english"
}
into a multi-field mapping like this:
"tweet": { (1)
"type": "string",
"analyzer": "english",
"fields": {
"raw": { (2)
"type": "string",
"index": "not_analyzed"
}
}
}
-
The main
tweet
field is just the same as before: ananalyzed
full text field. -
The new
tweet.raw
sub-field isnot_analyzed
.
Now, or at least as soon as we have reindexed our data, we can use the tweet
field for search and the tweet.raw
field for sorting:
GET /_search
{
"query": {
"match": {
"tweet": "elasticsearch"
}
},
"sort": "tweet.raw"
}
Warning
|
Sorting on a full text analyzed field can use a lot of memory. See
[fielddata-intro] for more.
|