Fielddataedit

Aggregations work via a data structure known as fielddata (briefly introduced in Fielddata). Fielddata is often the largest consumer of memory in an Elasticsearch cluster, so it is important to understand how it works.

Tip

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.

Tip

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.

Note

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 Relationship), and certain types of queries or filters, such as the geo_distance filter.