Aggregations work via a data structure known as fielddata (briefly introduced in [fielddata-intro]). Fielddata is often the largest consumer of memory in an Elasticsearch cluster, so it is important to understand how it works.
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Tip
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Fielddata can be loaded on the fly into memory, or built at index time and stored on disk. Later, we will talk about on-disk fielddata in [doc-values]. For now we will focus on in-memory fielddata, as it is currently the default mode of operation in Elasticsearch. This may well change in a future version. |
Fielddata exists because inverted indices are efficient only for certain operations. The inverted index excels at finding documents that contain a term. It does not perform well in the opposite direction: determining which terms exist in a single document. Aggregations need this secondary access pattern.
Consider the following inverted index:
Term Doc_1 Doc_2 Doc_3 ------------------------------------ brown | X | X | dog | X | | X dogs | | X | X fox | X | | X foxes | | X | in | | X | jumped | X | | X lazy | X | X | leap | | X | over | X | X | X quick | X | X | X summer | | X | the | X | | X ------------------------------------
If we want to compile a complete list of terms in any document that mentions brown, we might build a query like so:
GET /my_index/_search
{
"query" : {
"match" : {
"body" : "brown"
}
},
"aggs" : {
"popular_terms": {
"terms" : {
"field" : "body"
}
}
}
}The query portion is easy and efficient. The inverted index is sorted by
terms, so first we find brown in the terms list, and then scan across all the
columns to see which documents contain brown. We can very quickly see that
Doc_1 and Doc_2 contain the token brown.
Then, for the aggregation portion, we need to find all the unique terms in
Doc_1 and Doc_2. Trying to do this with the inverted index would be a
very expensive process: we would have to iterate over every term in the index
and collect tokens from Doc_1 and Doc_2 columns. This would be slow
and scale poorly: as the number of terms and documents grows, so would the
execution time.
Fielddata addresses this problem by inverting the relationship. While the inverted index maps terms to the documents containing the term, fielddata maps documents to the terms contained by the document:
Doc Terms ----------------------------------------------------------------- Doc_1 | brown, dog, fox, jumped, lazy, over, quick, the Doc_2 | brown, dogs, foxes, in, lazy, leap, over, quick, summer Doc_3 | dog, dogs, fox, jumped, over, quick, the -----------------------------------------------------------------
Once the data has been uninverted, it is trivial to collect the unique tokens from
Doc_1 and Doc_2. Go to the rows for each document, collect all the terms, and
take the union of the two sets.
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Tip
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The fielddata cache is per segment. In other words, when a new segment becomes visible to search, the fielddata cached from old segments remains valid. Only the data for the new segment needs to be loaded into memory. |
Thus, search and aggregations are closely intertwined. Search finds documents by using the inverted index. Aggregations collect and aggregate values from fielddata, which is itself generated from the inverted index.
The rest of this chapter covers various functionality that either decreases fielddata’s memory footprint or increases execution speed.
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Note
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Fielddata is not just used for aggregations. It is required for any
operation that needs to look up the value contained in a specific document.
Besides aggregations, this includes sorting, scripts that access field
values, parent-child relationships (see [parent-child]), and certain types
of queries or filters, such as the |