A long-lived project that still receives updates
Chewy provides functionality for Elasticsearch index handling, documents import mappings and chainable query DSL


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Chewy is an ODM (Object Document Mapper), built on top of the official Elasticsearch client.

Why Chewy?

In this section we'll cover why you might want to use Chewy instead of the official elasticsearch-ruby client gem.

  • Every index is observable by all the related models.

    Most of the indexed models are related to other and sometimes it is necessary to denormalize this related data and put at the same object. For example, you need to index an array of tags together with an article. Chewy allows you to specify an updateable index for every model separately - so corresponding articles will be reindexed on any tag update.

  • Bulk import everywhere.

    Chewy utilizes the bulk ES API for full reindexing or index updates. It also uses atomic updates. All the changed objects are collected inside the atomic block and the index is updated once at the end with all the collected objects. See Chewy.strategy(:atomic) for more details.

  • Powerful querying DSL.

    Chewy has an ActiveRecord-style query DSL. It is chainable, mergeable and lazy, so you can produce queries in the most efficient way. It also has object-oriented query and filter builders.

  • Support for ActiveRecord.


Add this line to your application's Gemfile:

gem 'chewy'

And then execute:

$ bundle

Or install it yourself as:

$ gem install chewy



Chewy is compatible with MRI 3.0-3.2¹.

¹ Ruby 3 is only supported with Rails 6.1

Elasticsearch compatibility matrix

Chewy version Elasticsearch version
7.2.x 7.x
7.1.x 7.x
7.0.x 6.8, 7.x
6.0.0 5.x, 6.x
5.x 5.x, limited support for 1.x & 2.x

Important: Chewy doesn't follow SemVer, so you should always check the release notes before upgrading. The major version is linked to the newest supported Elasticsearch and the minor version bumps may include breaking changes.

See our migration guide for detailed upgrade instructions between various Chewy versions.

Active Record

5.2, 6.0, 6.1 Active Record versions are supported by all Chewy versions.

Getting Started

Chewy provides functionality for Elasticsearch index handling, documents import mappings, index update strategies and chainable query DSL.

Minimal client setting

Create config/initializers/chewy.rb with this line:

Chewy.settings = {host: 'localhost:9250'}

And run rails g chewy:install to generate chewy.yml:

# config/chewy.yml
# separate environment configs
  host: 'localhost:9250'
  prefix: 'test'
  host: 'localhost:9200'


Make sure you have Elasticsearch up and running. You can install it locally, but the easiest way is to use Docker:

$ docker run --rm --name elasticsearch -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" elasticsearch:7.11.1


Create app/chewy/users_index.rb with User Index:

class UsersIndex < Chewy::Index
  settings analysis: {
    analyzer: {
      email: {
        tokenizer: 'keyword',
        filter: ['lowercase']

  index_scope User
  field :first_name
  field :last_name
  field :email, analyzer: 'email'


Add User model, table and migrate it:

$ bundle exec rails g model User first_name last_name email
$ bundle exec rails db:migrate

Add update_index to app/models/user.rb:

class User < ApplicationRecord
  update_index('users') { self }

Example of data request

  1. Once a record is created (could be done via the Rails console), it creates User index too:
  first_name: "test1",
  last_name: "test1",
  email: 'test1@example.com',
  # other fields
# UsersIndex Import (355.3ms) {:index=>1}
# => #<User id: 1, first_name: "test1", last_name: "test1", email: "test1@example.com", # other fields>
  1. A query could be exposed at a given UsersController:
def search
  @users = UsersIndex.query(query_string: { fields: [:first_name, :last_name, :email, ...], query: search_params[:query], default_operator: 'and' })
  render json: @users.to_json, status: :ok


def search_params
  params.permit(:query, :page, :per)
  1. So a request against http://localhost:3000/users/search?query=test1@example.com issuing a response like:

Usage and configuration

Client settings

To configure the Chewy client you need to add chewy.rb file with Chewy.settings hash:

# config/initializers/chewy.rb
Chewy.settings = {host: 'localhost:9250'} # do not use environments

And add chewy.yml configuration file.

You can create chewy.yml manually or run rails g chewy:install to generate it:

# config/chewy.yml
# separate environment configs
  host: 'localhost:9250'
  prefix: 'test'
  host: 'localhost:9200'

The resulting config merges both hashes. Client options are passed as is to Elasticsearch::Transport::Client except for the :prefix, which is used internally by Chewy to create prefixed index names:

  Chewy.settings = {prefix: 'test'}
  UsersIndex.index_name # => 'test_users'

The logger may be set explicitly:

Chewy.logger = Logger.new(STDOUT)

See config.rb for more details.

AWS Elasticsearch

If you would like to use AWS's Elasticsearch using an IAM user policy, you will need to sign your requests for the es:* action by injecting the appropriate headers passing a proc to transport_options. You'll need an additional gem for Faraday middleware: add gem 'faraday_middleware-aws-sigv4' to your Gemfile.

require 'faraday_middleware/aws_sigv4'

Chewy.settings = {
  host: 'http://my-es-instance-on-aws.us-east-1.es.amazonaws.com:80',
  port: 80, # 443 for https host
  transport_options: {
    headers: { content_type: 'application/json' },
    proc: -> (f) do
        f.request :aws_sigv4,
                  service: 'es',
                  region: 'us-east-1',
                  access_key_id: ENV['AWS_ACCESS_KEY'],
                  secret_access_key: ENV['AWS_SECRET_ACCESS_KEY']

Index definition

  1. Create /app/chewy/users_index.rb
class UsersIndex < Chewy::Index

  1. Define index scope (you can omit this part if you don't need to specify a scope (i.e. use PORO objects for import) or options)
class UsersIndex < Chewy::Index
  index_scope User.active # or just model instead_of scope: index_scope User
  1. Add some mappings
class UsersIndex < Chewy::Index
  index_scope User.active.includes(:country, :badges, :projects)
  field :first_name, :last_name # multiple fields without additional options
  field :email, analyzer: 'email' # Elasticsearch-related options
  field :country, value: ->(user) { user.country.name } # custom value proc
  field :badges, value: ->(user) { user.badges.map(&:name) } # passing array values to index
  field :projects do # the same block syntax for multi_field, if `:type` is specified
    field :title
    field :description # default data type is `text`
    # additional top-level objects passed to value proc:
    field :categories, value: ->(project, user) { project.categories.map(&:name) if user.active? }
  field :rating, type: 'integer' # custom data type
  field :created, type: 'date', include_in_all: false,
    value: ->{ created_at } # value proc for source object context

See here for mapping definitions.

  1. Add some index-related settings. Analyzer repositories might be used as well. See Chewy::Index.settings docs for details:
class UsersIndex < Chewy::Index
  settings analysis: {
    analyzer: {
      email: {
        tokenizer: 'keyword',
        filter: ['lowercase']

  index_scope User.active.includes(:country, :badges, :projects)
  root date_detection: false do
    template 'about_translations.*', type: 'text', analyzer: 'standard'

    field :first_name, :last_name
    field :email, analyzer: 'email'
    field :country, value: ->(user) { user.country.name }
    field :badges, value: ->(user) { user.badges.map(&:name) }
    field :projects do
      field :title
      field :description
    field :about_translations, type: 'object' # pass object type explicitly if necessary
    field :rating, type: 'integer'
    field :created, type: 'date', include_in_all: false,
      value: ->{ created_at }

See index settings here. See root object settings here.

See mapping.rb for more details.

  1. Add model-observing code
class User < ActiveRecord::Base
  update_index('users') { self } # specifying index and back-reference
                                      # for updating after user save or destroy

class Country < ActiveRecord::Base
  has_many :users

  update_index('users') { users } # return single object or collection

class Project < ActiveRecord::Base
  update_index('users') { user if user.active? } # you can return even `nil` from the back-reference

class Book < ActiveRecord::Base
  update_index(->(book) {"books_#{book.language}"}) { self } # dynamic index name with proc.
                                                             # For book with language == "en"
                                                             # this code will generate `books_en`

Also, you can use the second argument for method name passing:

update_index('users', :self)
update_index('users', :users)

In the case of a belongs_to association you may need to update both associated objects, previous and current:

class City < ActiveRecord::Base
  belongs_to :country

  update_index('cities') { self }
  update_index 'countries' do
    previous_changes['country_id'] || country

Default import options

Every index has default_import_options configuration to specify, suddenly, default import options:

class ProductsIndex < Chewy::Index
  index_scope Post.includes(:tags)
  default_import_options batch_size: 100, bulk_size: 10.megabytes, refresh: false

  field :name
  field :tags, value: -> { tags.map(&:name) }

See import.rb for available options.

Multi (nested) and object field types

To define an objects field you can simply nest fields in the DSL:

field :projects do
  field :title
  field :description

This will automatically set the type or root field to object. You may also specify type: 'objects' explicitly.

To define a multi field you have to specify any type except for object or nested in the root field:

field :full_name, type: 'text', value: ->{ full_name.strip } do
  field :ordered, analyzer: 'ordered'
  field :untouched, type: 'keyword'

The value: option for internal fields will no longer be effective.

Geo Point fields

You can use Elasticsearch's geo mapping with the geo_point field type, allowing you to query, filter and order by latitude and longitude. You can use the following hash format:

field :coordinates, type: 'geo_point', value: ->{ {lat: latitude, lon: longitude} }

or by using nested fields:

field :coordinates, type: 'geo_point' do
  field :lat, value: ->{ latitude }
  field :long, value: ->{ longitude }

See the section on Script fields for details on calculating distance in a search.

Join fields

You can use a join field to implement parent-child relationships between documents. It replaces the old parent_id based parent-child mapping

To use it, you need to pass relations and join (with type and id) options:

field :hierarchy_link, type: :join, relations: {question: %i[answer comment], answer: :vote, vote: :subvote}, join: {type: :comment_type, id: :commented_id}

assuming you have comment_type and commented_id fields in your model.

Note that when you reindex a parent, its children and grandchildren will be reindexed as well. This may require additional queries to the primary database and to elastisearch.

Also note that the join field doesn't support crutches (it should be a field directly defined on the model).

Crutches™ technology

Assume you are defining your index like this (product has_many categories through product_categories):

class ProductsIndex < Chewy::Index
  index_scope Product.includes(:categories)
  field :name
  field :category_names, value: ->(product) { product.categories.map(&:name) } # or shorter just -> { categories.map(&:name) }

Then the Chewy reindexing flow will look like the following pseudo-code:

Product.includes(:categories).find_in_batches(1000) do |batch|
  bulk_body = batch.map do |object|
    {name: object.name, category_names: object.categories.map(&:name)}.to_json
  # here we are sending every batch of data to ES
  Chewy.client.bulk bulk_body

If you meet complicated cases when associations are not applicable you can replace Rails associations with Chewy Crutches™ technology:

class ProductsIndex < Chewy::Index
  index_scope Product
  crutch :categories do |collection| # collection here is a current batch of products
    # data is fetched with a lightweight query without objects initialization
    data = ProductCategory.joins(:category).where(product_id: collection.map(&:id)).pluck(:product_id, 'categories.name')
    # then we have to convert fetched data to appropriate format
    # this will return our data in structure like:
    # {123 => ['sweets', 'juices'], 456 => ['meat']}
    data.each.with_object({}) { |(id, name), result| (result[id] ||= []).push(name) }

  field :name
  # simply use crutch-fetched data as a value:
  field :category_names, value: ->(product, crutches) { crutches[:categories][product.id] }

An example flow will look like this:

Product.includes(:categories).find_in_batches(1000) do |batch|
  crutches[:categories] = ProductCategory.joins(:category).where(product_id: batch.map(&:id)).pluck(:product_id, 'categories.name')
    .each.with_object({}) { |(id, name), result| (result[id] ||= []).push(name) }

  bulk_body = batch.map do |object|
    {name: object.name, category_names: crutches[:categories][object.id]}.to_json
  Chewy.client.bulk bulk_body

So Chewy Crutches™ technology is able to increase your indexing performance in some cases up to a hundredfold or even more depending on your associations complexity.

Witchcraft™ technology

One more experimental technology to increase import performance. As far as you know, chewy defines value proc for every imported field in mapping, so at the import time each of these procs is executed on imported object to extract result document to import. It would be great for performance to use one huge whole-document-returning proc instead. So basically the idea or Witchcraft™ technology is to compile a single document-returning proc from the index definition.

index_scope Product

field :title
field :tags, value: -> { tags.map(&:name) }
field :categories do
  field :name, value: -> (product, category) { category.name }
  field :type, value: -> (product, category, crutch) { crutch.types[category.name] }

The index definition above will be compiled to something close to:

-> (object, crutches) do
    title: object.title,
    tags: object.tags.map(&:name),
    categories: object.categories.map do |object2|
        name: object2.name
        type: crutches.types[object2.name]

And don't even ask how is it possible, it is a witchcraft. Obviously not every type of definition might be compiled. There are some restrictions:

  1. Use reasonable formatting to make method_source be able to extract field value proc sources.
  2. Value procs with splat arguments are not supported right now.
  3. If you are generating fields dynamically use value proc with arguments, argumentless value procs are not supported yet:
[:first_name, :last_name].each do |name|
  field name, value: -> (o) { o.send(name) }

However, it is quite possible that your index definition will be supported by Witchcraft™ technology out of the box in most of the cases.

Raw Import

Another way to speed up import time is Raw Imports. This technology is only available in ActiveRecord adapter. Very often, ActiveRecord model instantiation is what consumes most of the CPU and RAM resources. Precious time is wasted on converting, say, timestamps from strings and then serializing them back to strings. Chewy can operate on raw hashes of data directly obtained from the database. All you need is to provide a way to convert that hash to a lightweight object that mimics the behaviour of the normal ActiveRecord object.

class LightweightProduct
  def initialize(attributes)
    @attributes = attributes

  # Depending on the database, `created_at` might
  # be in different formats. In PostgreSQL, for example,
  # you might see the following format:
  #   "2016-03-22 16:23:22"
  # Taking into account that Elastic expects something different,
  # one might do something like the following, just to avoid
  # unnecessary String -> DateTime -> String conversion.
  #   "2016-03-22 16:23:22" -> "2016-03-22T16:23:22Z"
  def created_at
    @attributes['created_at'].tr(' ', 'T') << 'Z'

index_scope Product
default_import_options raw_import: ->(hash) {

field :created_at, 'datetime'

Also, you can pass :raw_import option to the import method explicitly.

Index creation during import

By default, when you perform import Chewy checks whether an index exists and creates it if it's absent. You can turn off this feature to decrease Elasticsearch hits count. To do so you need to set skip_index_creation_on_import parameter to false in your config/chewy.yml

Skip record fields during import

You can use ignore_blank: true to skip fields that return true for the .blank? method:

index_scope Country
field :id
field :cities, ignore_blank: true do
  field :id
  field :name
  field :surname, ignore_blank: true
  field :description

Default values for different types

By default ignore_blank is false on every type except geo_point.


You can record all actions that were made to the separate journal index in ElasticSearch. When you create/update/destroy your documents, it will be saved in this special index. If you make something with a batch of documents (e.g. during index reset) it will be saved as a one record, including primary keys of each document that was affected. Common journal record looks like this:

  "action": "index",
  "object_id": [1, 2, 3],
  "index_name": "...",
  "created_at": "<timestamp>"

This feature is turned off by default. But you can turn it on by setting journal setting to true in config/chewy.yml. Also, you can specify journal index name. For example:

# config/chewy.yml
  journal: true
  journal_name: my_super_journal

Also, you can provide this option while you're importing some index:

CityIndex.import journal: true

Or as a default import option for an index:

class CityIndex
  index_scope City
  default_import_options journal: true

You may be wondering why do you need it? The answer is simple: not to lose the data.

Imagine that you reset your index in a zero-downtime manner (to separate index), and in the meantime somebody keeps updating the data frequently (to old index). So all these actions will be written to the journal index and you'll be able to apply them after index reset using the Chewy::Journal interface.

When enabled, journal can grow to enormous size, consider setting up cron job that would clean it occasionally using chewy:journal:clean rake task.

Index manipulation

UsersIndex.delete # destroy index if it exists

UsersIndex.create! # use bang or non-bang methods

UsersIndex.purge! # deletes then creates index

UsersIndex.import # import with 0 arguments process all the data specified in index_scope definition
UsersIndex.import User.where('rating > 100') # or import specified users scope
UsersIndex.import User.where('rating > 100').to_a # or import specified users array
UsersIndex.import [1, 2, 42] # pass even ids for import, it will be handled in the most effective way
UsersIndex.import User.where('rating > 100'), update_fields: [:email] # if update fields are specified - it will update their values only with the `update` bulk action
UsersIndex.import! # raises an exception in case of any import errors

UsersIndex.reset! # purges index and imports default data for all types

If the passed user is #destroyed?, or satisfies a delete_if index_scope option, or the specified id does not exist in the database, import will perform delete from index action for this object.

index_scope User, delete_if: :deleted_at
index_scope User, delete_if: -> { deleted_at }
index_scope User, delete_if: ->(user) { user.deleted_at }

See actions.rb for more details.

Index update strategies

Assume you've got the following code:

class City < ActiveRecord::Base
  update_index 'cities', :self

class CitiesIndex < Chewy::Index
  index_scope City
  field :name

If you do something like City.first.save! you'll get an UndefinedUpdateStrategy exception instead of the object saving and index updating. This exception forces you to choose an appropriate update strategy for the current context.

If you want to return to the pre-0.7.0 behavior - just set Chewy.root_strategy = :bypass.


The main strategy here is :atomic. Assume you have to update a lot of records in the db.

Chewy.strategy(:atomic) do

Using this strategy delays the index update request until the end of the block. Updated records are aggregated and the index update happens with the bulk API. So this strategy is highly optimized.


This does the same thing as :atomic, but asynchronously using sidekiq. Patch Chewy::Strategy::Sidekiq::Worker for index updates improving.

Chewy.strategy(:sidekiq) do

The default queue name is chewy, you can customize it in settings: sidekiq.queue_name

Chewy.settings[:sidekiq] = {queue: :low}


This does the same thing as :sidekiq, but with lazy evaluation. Beware it does not allow you to use any non-persistent record state for indices and conditions because record will be re-fetched from database asynchronously using sidekiq. However for destroying records strategy will fallback to :sidekiq because it's not possible to re-fetch deleted records from database.

The purpose of this strategy is to improve the response time of the code that should update indexes, as it does not only defer actual ES calls to a background job but update_index callbacks evaluation (for created and updated objects) too. Similar to :sidekiq, index update is asynchronous so this strategy cannot be used when data and index synchronization is required.

Chewy.strategy(:lazy_sidekiq) do

The default queue name is chewy, you can customize it in settings: sidekiq.queue_name

Chewy.settings[:sidekiq] = {queue: :low}


It accumulates IDs of records to be reindexed during the latency window in Redis and then performs the reindexing of all accumulated records at once. This strategy is very useful in the case of frequently mutated records. It supports the update_fields option, so it will attempt to select just enough data from the database.

Keep in mind, this strategy does not guarantee reindexing in the event of Sidekiq worker termination or an error during the reindexing phase. This behavior is intentional to prevent continuous growth of Redis db.

There are three options that can be defined in the index:

class CitiesIndex...
  strategy_config delayed_sidekiq: {
    latency: 3,
    margin: 2,
    ttl: 60 * 60 * 24,
    reindex_wrapper: ->(&reindex) {
      ActiveRecord::Base.connected_to(role: :reading) { reindex.call }
    # latency - will prevent scheduling identical jobs
    # margin - main purpose is to cover db replication lag by the margin
    # ttl - a chunk expiration time (in seconds)
    # reindex_wrapper - lambda that accepts block to wrap that reindex process AR connection block.


Also you can define defaults in the initializers/chewy.rb

Chewy.settings = {
  strategy_config: {
    delayed_sidekiq: {
      latency: 3,
      margin: 2,
      ttl: 60 * 60 * 24,
      reindex_wrapper: ->(&reindex) {
        ActiveRecord::Base.connected_to(role: :reading) { reindex.call }

or in config/chewy.yml

      latency: 3
      margin: 2
      ttl: <%= 60 * 60 * 24 %>
      # reindex_wrapper setting is not possible here!!! use the initializer instead

You can use the strategy identically to other strategies

Chewy.strategy(:delayed_sidekiq) do

The default queue name is chewy, you can customize it in settings: sidekiq.queue_name

Chewy.settings[:sidekiq] = {queue: :low}

Explicit call of the reindex using :delayed_sidekiq strategy

CitiesIndex.import([1, 2, 3], strategy: :delayed_sidekiq)

Explicit call of the reindex using :delayed_sidekiq strategy with :update_fields support

CitiesIndex.import([1, 2, 3], update_fields: [:name], strategy: :delayed_sidekiq)

While running tests with delayed_sidekiq strategy and Sidekiq is using a real redis instance that is NOT cleaned up in between tests (via e.g. Sidekiq.redis(&:flushdb)), you'll want to cleanup some redis keys in between tests to avoid state leaking and flaky tests. Chewy provides a convenience method for that:

# it might be a good idea to also add to your testing setup, e.g.: a rspec `before` hook


This does the same thing as :atomic, but using ActiveJob. This will inherit the ActiveJob configuration settings including the active_job.queue_adapter setting for the environment. Patch Chewy::Strategy::ActiveJob::Worker for index updates improving.

Chewy.strategy(:active_job) do

The default queue name is chewy, you can customize it in settings: active_job.queue_name

Chewy.settings[:active_job] = {queue: :low}


The following strategy is convenient if you are going to update documents in your index one by one.

Chewy.strategy(:urgent) do

This code will perform City.popular.count requests for ES documents update.

It is convenient for use in e.g. the Rails console with non-block notation:

> Chewy.strategy(:urgent)
> City.popular.map(&:do_some_update_action!)


When the bypass strategy is active the index will not be automatically updated on object save.

For example, on City.first.save! the cities index would not be updated.


Strategies are designed to allow nesting, so it is possible to redefine it for nested contexts.

Chewy.strategy(:atomic) do
  Chewy.strategy(:urgent) do
    # there will be 2 update index requests for city2 and city3
  # city1 and city4 will be grouped in one index update request

Non-block notation

It is possible to nest strategies without blocks:

city1.do_update! # index updated
city2.do_update! # update bypassed
city3.do_update! # index updated again

Designing your own strategies

See strategy/base.rb for more details. See strategy/atomic.rb for an example.

Rails application strategies integration

There are a couple of predefined strategies for your Rails application. Initially, the Rails console uses the :urgent strategy by default, except in the sandbox case. When you are running sandbox it switches to the :bypass strategy to avoid polluting the index.

Migrations are wrapped with the :bypass strategy. Because the main behavior implies that indices are reset after migration, there is no need for extra index updates. Also indexing might be broken during migrations because of the outdated schema.

Controller actions are wrapped with the configurable value of Chewy.request_strategy and defaults to :atomic. This is done at the middleware level to reduce the number of index update requests inside actions.

It is also a good idea to set up the :bypass strategy inside your test suite and import objects manually only when needed, and use Chewy.massacre when needed to flush test ES indices before every example. This will allow you to minimize unnecessary ES requests and reduce overhead.

RSpec.configure do |config|
  config.before(:suite) do

Elasticsearch client options

All connection options, except the :prefix, are passed to the Elasticseach::Client.new (chewy/lib/chewy.rb):

Here's the relevant Elasticsearch documentation on the subject: https://rubydoc.info/gems/elasticsearch-transport#setting-hosts

ActiveSupport::Notifications support

Chewy has notifying the following events:

search_query.chewy payload

  • payload[:index]: requested index class
  • payload[:request]: request hash

import_objects.chewy payload

  • payload[:index]: currently imported index name

  • payload[:import]: imports stats, total imported and deleted objects count:

    {index: 30, delete: 5}
  • payload[:errors]: might not exist. Contains grouped errors with objects ids list:

    {index: {
      'error 1 text' => ['1', '2', '3'],
      'error 2 text' => ['4']
    }, delete: {
      'delete error text' => ['10', '12']

NewRelic integration

To integrate with NewRelic you may use the following example source (config/initializers/chewy.rb):

require 'new_relic/agent/instrumentation/evented_subscriber'

class ChewySubscriber < NewRelic::Agent::Instrumentation::EventedSubscriber
  def start(name, id, payload)
    event = ChewyEvent.new(name, Time.current, nil, id, payload)

  def finish(_name, id, _payload)

  class ChewyEvent < NewRelic::Agent::Instrumentation::Event
      'import_objects.chewy' => 'import',
      'search_query.chewy' => 'search',
      'delete_query.chewy' => 'delete'

    def initialize(*args)
      @segment = start_segment

    def start_segment
      segment = NewRelic::Agent::Transaction::DatastoreSegment.new product, operation, collection, host, port
      if (txn = state.current_transaction)
        segment.transaction = txn
      segment.notice_sql @payload[:request].to_s

    def finish
      if (txn = state.current_transaction)
        txn.add_segment @segment


    def state
      @state ||= NewRelic::Agent::TransactionState.tl_get

    def product

    def operation

    def collection
      payload.values_at(:type, :index)
             .reject { |value| value.try(:empty?) }

    def host

    def port

ActiveSupport::Notifications.subscribe(/.chewy$/, ChewySubscriber.new)

Search requests

Quick introduction.

Composing requests

The request DSL have the same chainable nature as AR. The main class is Chewy::Search::Request.

CitiesIndex.query(match: {name: 'London'})

Main methods of the request DSL are: query, filter and post_filter, it is possible to pass pure query hashes or use elasticsearch-dsl.

  .filter(term: {name: 'Bangkok'})
  .query(match: {name: 'London'})
  .query.not(range: {population: {gt: 1_000_000}})

You can query a set of indexes at once:

CitiesIndex.indices(CountriesIndex).query(match: {name: 'Some'})

See https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html and https://github.com/elastic/elasticsearch-dsl-ruby for more details.

An important part of requests manipulation is merging. There are 4 methods to perform it: merge, and, or, not. See Chewy::Search::QueryProxy for details. Also, only and except methods help to remove unneeded parts of the request.

Every other request part is covered by a bunch of additional methods, see Chewy::Search::Request for details:

CitiesIndex.limit(10).offset(30).order(:name, {population: {order: :desc}})

Request DSL also provides additional scope actions, like delete_all, exists?, count, pluck, etc.


The request DSL supports pagination with Kaminari. An extension is enabled on initialization if Kaminari is available. See Chewy::Search and Chewy::Search::Pagination::Kaminari for details.

Named scopes

Chewy supports named scopes functionality. There is no specialized DSL for named scopes definition, it is simply about defining class methods.

See Chewy::Search::Scoping for details.

Scroll API

ElasticSearch scroll API is utilized by a bunch of methods: scroll_batches, scroll_hits, scroll_wrappers and scroll_objects.

See Chewy::Search::Scrolling for details.

Loading objects

It is possible to load ORM/ODM source objects with the objects method. To provide additional loading options use load method:

CitiesIndex.load(scope: -> { active }).to_a # to_a returns `Chewy::Index` wrappers.
CitiesIndex.load(scope: -> { active }).objects # An array of AR source objects.

See Chewy::Search::Loader for more details.

In case when it is necessary to iterate through both of the wrappers and objects simultaneously, object_hash method helps a lot:

scope = CitiesIndex.load(scope: -> { active })
scope.each do |wrapper|

Rake tasks

For a Rails application, some index-maintaining rake tasks are defined.


Performs zero-downtime reindexing as described here. So the rake task creates a new index with unique suffix and then simply aliases it to the common index name. The previous index is deleted afterwards (see Chewy::Index.reset! for more details).

rake chewy:reset # resets all the existing indices
rake chewy:reset[users] # resets UsersIndex only
rake chewy:reset[users,cities] # resets UsersIndex and CitiesIndex
rake chewy:reset[-users,cities] # resets every index in the application except specified ones


Performs reset exactly the same way as chewy:reset does, but only when the index specification (setting or mapping) was changed.

It works only when index specification is locked in Chewy::Stash::Specification index. The first run will reset all indexes and lock their specifications.

See Chewy::Stash::Specification and Chewy::Index::Specification for more details.

rake chewy:upgrade # upgrades all the existing indices
rake chewy:upgrade[users] # upgrades UsersIndex only
rake chewy:upgrade[users,cities] # upgrades UsersIndex and CitiesIndex
rake chewy:upgrade[-users,cities] # upgrades every index in the application except specified ones


It doesn't create indexes, it simply imports everything to the existing ones and fails if the index was not created before.

rake chewy:update # updates all the existing indices
rake chewy:update[users] # updates UsersIndex only
rake chewy:update[users,cities] # updates UsersIndex and CitiesIndex
rake chewy:update[-users,cities] # updates every index in the application except UsersIndex and CitiesIndex


Provides a way to synchronize outdated indexes with the source quickly and without doing a full reset. By default field updated_at is used to find outdated records, but this could be customized by outdated_sync_field as described at Chewy::Index::Syncer.

Arguments are similar to the ones taken by chewy:update task.

See Chewy::Index::Syncer for more details.

rake chewy:sync # synchronizes all the existing indices
rake chewy:sync[users] # synchronizes UsersIndex only
rake chewy:sync[users,cities] # synchronizes UsersIndex and CitiesIndex
rake chewy:sync[-users,cities] # synchronizes every index in the application except except UsersIndex and CitiesIndex


This rake task is especially useful during the production deploy. It is a combination of chewy:upgrade and chewy:sync and the latter is called only for the indexes that were not reset during the first stage.

It is not possible to specify any particular indexes for this task as it doesn't make much sense.

Right now the approach is that if some data had been updated, but index definition was not changed (no changes satisfying the synchronization algorithm were done), it would be much faster to perform manual partial index update inside data migrations or even manually after the deploy.

Also, there is always full reset alternative with rake chewy:reset.


This rake task creates newly defined indexes in ElasticSearch and skips existing ones. Useful for production-like environments.

Parallelizing rake tasks

Every task described above has its own parallel version. Every parallel rake task takes the number for processes for execution as the first argument and the rest of the arguments are exactly the same as for the non-parallel task version.

https://github.com/grosser/parallel gem is required to use these tasks.

If the number of processes is not specified explicitly - parallel gem tries to automatically derive the number of processes to use.

rake chewy:parallel:reset
rake chewy:parallel:upgrade[4]
rake chewy:parallel:update[4,cities]
rake chewy:parallel:sync[4,-users]
rake chewy:parallel:deploy[4] # performs parallel upgrade and parallel sync afterwards


This namespace contains two tasks for the journal manipulations: chewy:journal:apply and chewy:journal:clean. Both are taking time as the first argument (optional for clean) and a list of indexes exactly as the tasks above. Time can be in any format parsable by ActiveSupport.

rake chewy:journal:apply["$(date -v-1H -u +%FT%TZ)"] # apply journaled changes for the past hour
rake chewy:journal:apply["$(date -v-1H -u +%FT%TZ)",users] # apply journaled changes for the past hour on UsersIndex only

When the size of the journal becomes very large, the classical way of deletion would be obstructive and resource consuming. Fortunately, Chewy internally uses delete-by-query ES function which supports async execution with batching and throttling.

The available options, which can be set by ENV variables, are listed below:

  • WAIT_FOR_COMPLETION - a boolean flag. It controls async execution. It waits by default. When set to false (0, f, false or off in any case spelling is accepted as false), Elasticsearch performs some preflight checks, launches the request, and returns a task reference you can use to cancel the task or get its status.
  • REQUESTS_PER_SECOND - float. The throttle for this request in sub-requests per second. No throttling is enforced by default.
  • SCROLL_SIZE - integer. The number of documents to be deleted in single sub-request. The default batch size is 1000.
rake chewy:journal:clean WAIT_FOR_COMPLETION=false REQUESTS_PER_SECOND=10 SCROLL_SIZE=5000

RSpec integration

Just add require 'chewy/rspec' to your spec_helper.rb and you will get additional features:

update_index helper mock_elasticsearch_response helper to mock elasticsearch response mock_elasticsearch_response_sources helper to mock elasticsearch response sources build_query matcher to compare request and expected query (returns true/false)

To use mock_elasticsearch_response and mock_elasticsearch_response_sources helpers add include Chewy::Rspec::Helpers to your tests.

See chewy/rspec/ for more details.

Minitest integration

Add require 'chewy/minitest' to your test_helper.rb, and then for tests which you'd like indexing test hooks, include Chewy::Minitest::Helpers.

Since you can set :bypass strategy for test suites and manually handle import for the index and manually flush test indices using Chewy.massacre. This will help reduce unnecessary ES requests

But if you require chewy to index/update model regularly in your test suite then you can specify :urgent strategy for documents indexing. Add Chewy.strategy(:urgent) to test_helper.rb.

Also, you can use additional helpers:

mock_elasticsearch_response to mock elasticsearch response mock_elasticsearch_response_sources to mock elasticsearch response sources assert_elasticsearch_query to compare request and expected query (returns true/false)

See chewy/minitest/ for more details.


If you use DatabaseCleaner in your tests with the transaction strategy, you may run into the problem that ActiveRecord's models are not indexed automatically on save despite the fact that you set the callbacks to do this with the update_index method. The issue arises because chewy indices data on after_commit run as default, but all after_commit callbacks are not run with the DatabaseCleaner's' transaction strategy. You can solve this issue by changing the Chewy.use_after_commit_callbacks option. Just add the following initializer in your Rails application:

Chewy.use_after_commit_callbacks = !Rails.env.test?

Pre-request Filter

Should you need to inspect the query prior to it being dispatched to ElasticSearch during any queries, you can use the before_es_request_filter. before_es_request_filter is a callable object, as demonstrated below:

Chewy.before_es_request_filter = -> (method_name, args, kw_args) { ... }

While using the before_es_request_filter, please consider the following:

  • before_es_request_filter acts as a simple proxy before any request made via the ElasticSearch::Client. The arguments passed to this filter include:
    • method_name - The name of the method being called. Examples are search, count, bulk and etc.
    • args and kw_args - These are the positional arguments provided in the method call.
  • The operation is synchronous, so avoid executing any heavy or time-consuming operations within the filter to prevent performance degradation.
  • The return value of the proc is disregarded. This filter is intended for inspection or modification of the query rather than generating a response.
  • Any exception raised inside the callback will propagate upward and halt the execution of the query. It is essential to handle potential errors adequately to ensure the stability of your search functionality.

Import scope clean-up behavior

Whenever you set the import_scope for the index, in the case of ActiveRecord, options for order, offset and limit will be removed. You can set the behavior of chewy, before the clean-up itself.

The default behavior is a warning sent to the Chewy logger (:warn). Another more restrictive option is raising an exception (:raise). Both options have a negative impact on performance since verifying whether the code uses any of these options requires building AREL query.

To avoid the loading time impact, you can ignore the check (:ignore) before the clean-up.

Chewy.import_scope_cleanup_behavior = :ignore


  1. Fork it (http://github.com/toptal/chewy/fork)
  2. Create your feature branch (git checkout -b my-new-feature)
  3. Implement your changes, cover it with specs and make sure old specs are passing
  4. Commit your changes (git commit -am 'Add some feature')
  5. Push to the branch (git push origin my-new-feature)
  6. Create new Pull Request

Use the following Rake tasks to control the Elasticsearch cluster while developing, if you prefer native Elasticsearch installation over the dockerized one:

rake elasticsearch:start # start Elasticsearch cluster on 9250 port for tests
rake elasticsearch:stop # stop Elasticsearch


Copyright (c) 2013-2021 Toptal, LLC. See LICENSE.txt for further details.