Composing a Nested Elasticsearch Query in Golang

July 23, 2016

TL;DR: A well-composed elasticsearch query is a beautiful thing, but it's easy to get lost on the way there. This post is one path. Stay clear-headed from the top down and you should be fine.

Elasticsearch is a fast database with a powerful query language.

Golang is a fine language to build an app with, and with a little work, you can use it to present a clean search abstraction to the rest of your app.


  • I'm running Elasticsearch (v2.3) via Docker Native on my mac
  • I'm running go1.7beta1
  • The source code for this whole post is available here

The dataset: Pokemon trainers with nested Poke-docs

Pokemon is topical enough to give us a nested data structure. We're going to be working with a trainer type that has a nested property pokemon, which is a type: nested array on the doc.

The mapping:

type: string
type: string
type: nested
type: string
type: long

type: nested?

Nested documents in elasticsearch maintain the associations you'd expect.

Take this trainer:

"name": "Ash",
"pokemon": [
{ "name": "Pikachu", "level": 7 },
{ "name": "Charizard", "level": 45 }

If our mapping did not mark the type of field pokemon as nested, elasticsearch would have flattend these pokemon onto the trainer object.

// Pseudo code: Elasticsearch internals (via Lucene)
"name": "ash",
"pokemon.name": ["pikachu", "charizard"]
"pokemon.level": [7, 45]

Searches for a level 45 pikachu would return this trainer, when in fact he has no such thing!

Using type: nested tells elasticsearch to store each of these as separate documents. This lets us to search for individual pokemon on trainers, but also requires us to build a nested query - elasticsearch does not do that work for us.

Elasticsearch doesn't store nested documents on the root doc at all, which can be a performance improvement for your queries that don't touch the nested object - especially if those nested pokemon add significant size to the document.

Anyone have a Level 19 Magikarp?

Our goal with this query builder will be an abstraction that we can use to search for trainers. Specifically, we want to find a trainer with a pokemon matching our search criteria.

Something like:

// Golang!
trainers, err := Storage.FetchTrainers(&TrainerSearchOpts{
Pokemon: "Magikarp",
Level: 19,

Basics first

A basic FetchTrainers via olivere/elastic might look like:

// FetchTrainers queries trainers with the passed options
func (s *Store) FetchTrainers(opts TrainerSearchOpts) ([]*trainers.Trainer, error) {
query := elastic.NewMatchAllQuery()
res, err := s.es.Search(s.trainerIndex).Type(s.trainerType).Query(query).Do()
if err != nil {
return nil, err
var trns []*trainers.Trainer
for _, iT := range res.Each(reflect.TypeOf(&trainers.Trainer{})) {
trns = append(trns, iT.(*trainers.Trainer))
return trns, nil

We're going to refactor this into a nested query that uses a filter to limit the returned trainers to those with a pokemon matching the search.

Learn that Bool Query

The Bool Query is the key to composability in elasticsearch queries. You can read more in the docs, but the gist is that you can attach arrays of subqueries to a single Bool Query, and it is treated as a single query wherever you drop it in.

At our top-level query, we're going to create a Bool query and then attach a Filter. If no other queries are attached, Bool Queries default to using a "match_all", so attaching a Filter will limit the results to everything in the index that gets through our filter.

// switch to a Bool query
query := elastic.NewBoolQuery()
// set some filters
query = query.Filter(buildFilters(opts)...)

Where that nested magic happens

We need to implement the buildFilters function used above. It should return a slice of filters relevant to our query - in this case, we want to limit the results to our search criteria.

func buildFilters(opts TrainerSearchOpts) []elastic.Query {
return []elastic.Query{
elastic.NewMatchQuery("pokemon.name", opts.Pokemon),

We use NewNestedQuery(path string, query elastic.Query) to get a nested query, then set a match query for a pokemon's name.

This works fine for filtering on a single field, and leaves it easy for us to add more filters of our choosing (perhaps on a trainer's badges or items). However, the moment we want to search for a more specific nested pokemon, this will start to get a little messy.

Notable here: if you were to add a second nested filter to buildFilters()'s slice that matched on a pokemon's level, the queries would not be applied to the same pokemon. Queries that you want to apply to the same nested object must live on the same nested query.

Let's go ahead and refactor it:

func buildFilters(opts TrainerSearchOpts) []elastic.Query {
return []elastic.Query{
func buildPokemonFilter(opts TrainerSearchOpts) elastic.Query {
return elastic.NewBoolQuery().Filter(
elastic.NewMatchQuery("pokemon.name", opts.Pokemon),
elastic.NewMatchQuery("pokemon.level", opts.Level),

Here we're using the composability of the Bool Query again - we want both our Match queries to apply as a single boolean signal to the nested Query above.

Off to the Pokemon League!

This could be taken much farther, and the design should be shaped by the API you want to expose. I don't have a strong search story yet here, but if you want to see this taken a few steps farther, leave a comment with your use-case, I'm happy to keep extending the metaphor.

I hope this helps unlock your use of Elasticsearch in Golang! Good luck catching them all!

Full source code.

Russell Matney

Russell Matney

Writing, Stories, Software, Indie Games


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