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---
stage: Foundations
group: Global Search
info: Any user with at least the Maintainer role can merge updates to this content. For details, see https://docs.gitlab.com/development/development_processes/#development-guidelines-review.
title: Advanced search development guidelines
---
This page includes information about developing and working with Elasticsearch.
Information on how to enable Elasticsearch and perform the initial indexing is in
the [Elasticsearch integration documentation](../integration/advanced_search/elasticsearch.md#enable-advanced-search).
## Deep Dive
In June 2019, Mario de la Ossa hosted a Deep Dive (GitLab team members only:
`https://gitlab.com/gitlab-org/create-stage/-/issues/1`) on the GitLab
[Elasticsearch integration](../integration/advanced_search/elasticsearch.md) to
share his domain specific knowledge with anyone who may work in this part of the
codebase in the future. You can find the
<i class="fa fa-youtube-play youtube" aria-hidden="true"></i>
[recording on YouTube](https://www.youtube.com/watch?v=vrvl-tN2EaA), and the slides on
[Google Slides](https://docs.google.com/presentation/d/1H-pCzI_LNrgrL5pJAIQgvLX8Ji0-jIKOg1QeJQzChug/edit) and in
[PDF](https://gitlab.com/gitlab-org/create-stage/uploads/c5aa32b6b07476fa8b597004899ec538/Elasticsearch_Deep_Dive.pdf).
Everything covered in this deep dive was accurate as of GitLab 12.0, and while
specific details might have changed, it should still serve as a good introduction.
In August 2020, a second Deep Dive was hosted, focusing on
[GitLab-specific architecture for multi-indices support](#zero-downtime-reindexing-with-multiple-indices). The
<i class="fa fa-youtube-play youtube" aria-hidden="true"></i>
[recording on YouTube](https://www.youtube.com/watch?v=0WdPR9oB2fg) and the
[slides](https://lulalala.gitlab.io/gitlab-elasticsearch-deepdive/) are available.
Everything covered in this deep dive was accurate as of GitLab 13.3.
In July 2024, Terri Chu hosted a Lunch and Learn on Advanced search basics, integration, indexing and search. The [Google Slides](https://docs.google.com/presentation/d/1Fy3pfFIGK_2ZCoB93EksRKhaS7uuNp81I3L5_joWa04/edit?usp=sharing_) (GitLab team members only) and
<i class="fa fa-youtube-play youtube" aria-hidden="true"></i>[recording on YouTube](https://youtu.be/5OXK1isDaks) (GitLab team members only) are available.
Everything covered in this deep dive was accurate as of GitLab 17.0.
## Supported Versions
See [Version Requirements](../integration/advanced_search/elasticsearch.md#version-requirements).
Developers making significant changes to Elasticsearch queries should test their features against all our supported versions.
## Setting up development environment
See the [Elasticsearch GDK setup instructions](https://gitlab.com/gitlab-org/gitlab-development-kit/blob/main/doc/howto/elasticsearch.md)
## Helpful Rake tasks
- `gitlab:elastic:test:index_size`: Tells you how much space the current index is using, as well as how many documents are in the index.
- `gitlab:elastic:test:index_size_change`: Outputs index size, reindexes, and outputs index size again. Useful when testing improvements to indexing size.
Additionally, if you need large repositories or multiple forks for testing, consider [following these instructions](rake_tasks.md#extra-project-seed-options)
## How does it work?
The Elasticsearch integration depends on an external indexer. We ship an
[indexer written in Go](https://gitlab.com/gitlab-org/gitlab-elasticsearch-indexer).
The user must trigger the initial indexing via a Rake task but, after this is done,
GitLab itself will trigger reindexing when required via `after_` callbacks on create,
update, and destroy that are inherited from
[`/ee/app/models/concerns/elastic/application_versioned_search.rb`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/ee/app/models/concerns/elastic/application_versioned_search.rb).
After initial indexing is complete, create, update, and delete operations for all
models except projects (see [#207494](https://gitlab.com/gitlab-org/gitlab/-/issues/207494))
are tracked in a Redis [`ZSET`](https://redis.io/docs/latest/develop/data-types/#sorted-sets).
A regular `sidekiq-cron` `ElasticIndexBulkCronWorker` processes this queue, updating
many Elasticsearch documents at a time with the
[Bulk Request API](https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-bulk.html).
Search queries are generated by the concerns found in
[`ee/app/models/concerns/elastic`](https://gitlab.com/gitlab-org/gitlab/-/tree/master/ee/app/models/concerns/elastic).
These concerns are also in charge of access control, and have been a historic
source of security bugs so pay close attention to them!
### Architecture
{{< alert type="note" >}}
We are migrating away from this architecture pattern in
[this epic](https://gitlab.com/groups/gitlab-org/-/epics/13873).
{{< /alert >}}
The traditional setup, provided by `elasticsearch-rails`, is to communicate through its internal proxy classes.
Developers would write model-specific logic in a module for the model to include in (for example, `SnippetsSearch`).
The `__elasticsearch__` methods would return a proxy object, for example:
- `Issue.__elasticsearch__` returns an instance of `Elasticsearch::Model::Proxy::ClassMethodsProxy`
- `Issue.first.__elasticsearch__` returns an instance of `Elasticsearch::Model::Proxy::InstanceMethodsProxy`.
These proxy objects would talk to Elasticsearch server directly (see top half of the diagram).
![Diagram showing how GitLab integrates with Elasticsearch across multiple indices](img/elasticsearch_architecture_v12_3.svg)
In the planned new design, each model would have a pair of corresponding sub-classed proxy objects, in which
model-specific logic is located. For example, `Snippet` would have `SnippetClassProxy` being a subclass
of `Elasticsearch::Model::Proxy::ClassMethodsProxy`. `Snippet` would have `SnippetInstanceProxy` being a subclass
of `Elasticsearch::Model::Proxy::InstanceMethodsProxy`.
`__elasticsearch__` would represent another layer of proxy object, keeping track of multiple actual proxy objects. It
would forward method calls to the appropriate index. For example:
- `model.__elasticsearch__.search` would be forwarded to the one stable index, since it is a read operation.
- `model.__elasticsearch__.update_document` would be forwarded to all indices, to keep all indices up-to-date.
The global configurations per version are now in the `Elastic::(Version)::Config` class. You can change mappings there.
### Custom routing
[Custom routing](https://www.elastic.co/guide/en/elasticsearch/reference/current/mapping-routing-field.html#_searching_with_custom_routing)
is used in Elasticsearch for document types that are associated with a project. The routing format is `project_<project_id>`. Routing is set
during indexing and searching operations. Some of the benefits and tradeoffs to using custom routing are:
- Project scoped searches are much faster.
- Routing is not used if too many shards would be hit for global and group scoped searches.
- Shard size imbalance might occur.
<!-- vale gitlab_base.Spelling = NO -->
## Existing analyzers and tokenizers
The following analyzers and tokenizers are defined in
[`ee/lib/elastic/latest/config.rb`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/ee/lib/elastic/latest/config.rb).
<!-- vale gitlab_base.Spelling = YES -->
### Analyzers
#### `path_analyzer`
Used when indexing blobs' paths. Uses the `path_tokenizer` and the `lowercase` and `asciifolding` filters.
See the `path_tokenizer` explanation below for an example.
#### `sha_analyzer`
Used in blobs and commits. Uses the `sha_tokenizer` and the `lowercase` and `asciifolding` filters.
See the `sha_tokenizer` explanation later below for an example.
#### `code_analyzer`
Used when indexing a blob's filename and content. Uses the `whitespace` tokenizer
and the [`word_delimiter_graph`](https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-word-delimiter-graph-tokenfilter.html),
`lowercase`, and `asciifolding` filters.
The `whitespace` tokenizer was selected to have more control over how tokens are split. For example the string `Foo::bar(4)` needs to generate tokens like `Foo` and `bar(4)` to be properly searched.
See the `code` filter for an explanation on how tokens are split.
### Tokenizers
#### `sha_tokenizer`
This is a custom tokenizer that uses the
[`edgeNGram` tokenizer](https://www.elastic.co/guide/en/elasticsearch/reference/5.5/analysis-edgengram-tokenizer.html)
to allow SHAs to be searchable by any sub-set of it (minimum of 5 chars).
Example:
`240c29dc7e` becomes:
- `240c2`
- `240c29`
- `240c29d`
- `240c29dc`
- `240c29dc7`
- `240c29dc7e`
#### `path_tokenizer`
This is a custom tokenizer that uses the
[`path_hierarchy` tokenizer](https://www.elastic.co/guide/en/elasticsearch/reference/5.5/analysis-pathhierarchy-tokenizer.html)
with `reverse: true` to allow searches to find paths no matter how much or how little of the path is given as input.
Example:
`'/some/path/application.js'` becomes:
- `'/some/path/application.js'`
- `'some/path/application.js'`
- `'path/application.js'`
- `'application.js'`
## Gotchas
- Searches can have their own analyzers. Remember to check when editing analyzers.
- `Character` filters (as opposed to token filters) always replace the original character. These filters can hinder exact searches.
## Add a new document type to Elasticsearch
If data cannot be added to one of the [existing indices in Elasticsearch](../integration/advanced_search/elasticsearch.md#advanced-search-index-scopes), follow these instructions to set up a new index and populate it.
### Recommendations
- Ensure [Elasticsearch is running](#setting-up-development-environment):
```shell
curl "http://localhost:9200"
```
<!-- vale gitlab_base.Spelling = NO -->
- [Run Kibana](https://www.elastic.co/guide/en/kibana/current/install.html#_install_kibana_yourself) to interact
with your local Elasticsearch cluster. Alternatively, you can use [Cerebro](https://github.com/lmenezes/cerebro) or a similar tool.
<!-- vale gitlab_base.Spelling = YES -->
- To tail the logs for Elasticsearch, run this command:
```shell
tail -f log/elasticsearch.log`
```
See [Recommended process for adding a new document type](#recommended-process-for-adding-a-new-document-type) for how to structure the rollout.
### Create the index
All new indexes must have:
- `project_id` and `namespace_id` fields (if available). One of the fields must be used for routing.
- A `traversal_ids` field for efficient global and group search. Populate the field with `object.namespace.elastic_namespace_ancestry`
- Fields for authorization:
- For project data - `visibility_level`
- For group data - `namespace_visibility_level`
- Any required access level fields. These correspond to project feature access levels such as `issues_access_level` or `repository_access_level`
- A `schema_version` integer field in a `YYWW` (year/week) format. This field is used for data migrations.
1. Create a `Search::Elastic::Types::` class in `ee/lib/search/elastic/types/`.
1. Define the following class methods:
- `index_name`: in the format `gitlab-<env>-<type>` (for example, `gitlab-production-work_items`).
- `mappings`: a hash containing the index schema such as fields, data types, and analyzers. Data types for primary and foreign keys must match the column type in the database. For example, the database column type `integer` maps to `integer` and `bigint` maps to `long` in the mapping.
- `settings`: a hash containing the index settings such as replicas and tokenizers.
The default is good enough for most cases.
1. Add a new [advanced search migration](search/advanced_search_migration_styleguide.md) to create the index
by executing `scripts/elastic-migration` and following the instructions.
The migration name must be in the format `Create<Name>Index`.
1. Use the [`Search::Elastic::MigrationCreateIndexHelper`](search/advanced_search_migration_styleguide.md#searchelasticmigrationcreateindexhelper)
helper and the `'migration creates a new index'` shared example for the specification file created.
1. Add the target class to `Gitlab::Elastic::Helper::ES_SEPARATE_CLASSES`.
1. To test the index creation, run `Elastic::MigrationWorker.new.perform` in a console and check that the index
has been created with the correct mappings and settings:
```shell
curl "http://localhost:9200/gitlab-development-<type>/_mappings" | jq .`
```
```shell
curl "http://localhost:9200/gitlab-development-<type>/_settings" | jq .`
```
### Create a new Elastic Reference
Create a `Search::Elastic::References::` class in `ee/lib/search/elastic/references/`.
The reference is used to perform bulk operations in Elasticsearch.
The file must inherit from `Search::Elastic::Reference` and define the following constant and methods:
```ruby
include Search::Elastic::Concerns::DatabaseReference # if there is a corresponding database record for every document
SCHEMA_VERSION = 24_46 # integer in YYWW format
override :serialize
def self.serialize(record)
# a string representation of the reference
end
override :instantiate
def self.instantiate(string)
# deserialize the string and call initialize
end
override :preload_indexing_data
def self.preload_indexing_data(refs)
# remove this method if `Search::Elastic::Concerns::DatabaseReference` is included
# otherwise return refs
end
def initialize
# initialize with instance variables
end
override :identifier
def identifier
# a way to identify the reference
end
override :routing
def routing
# Optional: an identifier to route the document in Elasticsearch
end
override :operation
def operation
# one of `:index`, `:upsert` or `:delete`
end
override :serialize
def serialize
# a string representation of the reference
end
override :as_indexed_json
def as_indexed_json
# a hash containing the document represenation for this reference
end
override :index_name
def index_name
# index name
end
def model_klass
# set to the model class if `Search::Elastic::Concerns::DatabaseReference` is included
end
```
To add data to the index, an instance of the new reference class is called in
`Elastic::ProcessBookkeepingService.track!()` to add the data to a queue of
references for indexing.
A cron worker pulls queued references and bulk-indexes the items into Elasticsearch.
To test that the indexing operation works, call `Elastic::ProcessBookkeepingService.track!()`
with an instance of the reference class and run `Elastic::ProcessBookkeepingService.new.execute`.
The logs show the updates. To check the document in the index, run this command:
```shell
curl "http://localhost:9200/gitlab-development-<type>/_search"
```
### Data consistency
Now that we have an index and a way to bulk index the new document type into Elasticsearch, we need to add data into the index. This consists of doing a backfill and doing continuous updates to ensure the index data is up to date.
The backfill is done by calling `Elastic::ProcessInitialBookkeepingService.track!()` with an instance of `Search::Elastic::Reference` for every document that should be indexed.
The continuous update is done by calling `Elastic::ProcessBookkeepingService.track!()` with an instance of `Search::Elastic::Reference` for every document that should be created/updated/deleted.
#### Backfilling data
Add a new [Advanced Search migration](search/advanced_search_migration_styleguide.md) to backfill data by executing `scripts/elastic-migration` and following the instructions.
The backfill should execute `Elastic::ProcessInitialBookkeepingService.track!()` with an instance of the `Search::Elastic::Reference` created before for every document that should be indexed. The `BackfillEpics` migration can be used as an example.
To test the backfill, run `Elastic::MigrationWorker.new.perform` in a console a couple of times and see that the index was populated.
Tail the logs to see the progress of the migration:
```shell
tail -f log/elasticsearch.log
```
#### Continuous updates
For `ActiveRecord` objects, the `ApplicationVersionedSearch` concern can be included on the model to index data based on callbacks. If that's not suitable, call `Elastic::ProcessBookkeepingService.track!()` with an instance of `Search::Elastic::Reference` whenever a document should be indexed.
Always check for `Gitlab::CurrentSettings.elasticsearch_indexing?` and `use_elasticsearch?` because some GitLab Self-Managed instances do not have Elasticsearch enabled and [namespace limiting](../integration/advanced_search/elasticsearch.md#limit-the-amount-of-namespace-and-project-data-to-index) can be enabled.
Also check that the index is able to handle the index request. For example, check that the index exists if it was added in the current major release by verifying that the migration to add the index was completed: `Elastic::DataMigrationService.migration_has_finished?`.
#### Transfers and deletes
Project and group transfers and deletes must make updates to the index to avoid orphaned data.
Indexes that contain a `project_id` field must use the [`Search::Elastic::DeleteWorker`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/ee/app/workers/search/elastic/delete_worker.rb). Indexes that contain a `namespace_id` field but no `project_id` field must use [`Search::ElasticGroupAssociationDeleteWorker`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/ee/app/workers/search/elastic_group_association_deletion_worker.rb).
1. Add the indexed class to `excluded_classes` in [`ElasticDeleteProjectWorker`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/ee/app/workers/elastic_delete_project_worker.rb)
1. Update the worker to remove documents from the index
### Recommended process for adding a new document type
Create the following MRs and have them reviewed by a member of the Global Search team:
1. [Create the index](#create-the-index).
1. [Create a new Elasticsearch reference](#create-a-new-elastic-reference).
1. Perform [continuous updates](#continuous-updates) behind a feature flag. Enable the flag fully before the backfill.
1. [Backfill the data](#backfilling-data).
After indexing is done, the index is ready for search.
### Adding a new scope to search service
Search data is available in [`SearchController`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/app/controllers/search_controller.rb) and
[Search API](https://gitlab.com/gitlab-org/gitlab/-/blob/master/lib/api/search.rb). Both use the [`SearchService`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/app/services/search_service.rb) to return results.
The `SearchService` can be used to return results outside of the `SearchController` and `Search API`.
#### Search scopes
The `SearchService` exposes searching at [global](https://gitlab.com/gitlab-org/gitlab/-/blob/master/app/services/search/global_service.rb),
[group](https://gitlab.com/gitlab-org/gitlab/-/blob/master/app/services/search/group_service.rb), and [project](https://gitlab.com/gitlab-org/gitlab/-/blob/master/app/services/search/project_service.rb) levels.
New scopes must be added to the following constants:
- `ALLOWED_SCOPES` (or override `allowed_scopes` method) in each EE `SearchService` file
- `ALLOWED_SCOPES` in `Gitlab::Search::AbuseDetection`
- `search_tab_ability_map` method in `Search::Navigation`. Override in the EE version if needed
{{< alert type="note" >}}
Global search can be disabled for a scope. You can do the following changes for disabling global search:
{{< /alert >}}
1. Add an application setting named `global_search_SCOPE_enabled` that defaults to `true` under the `search` jsonb accessor in [`app/models/application_setting.rb`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/app/models/application_setting.rb).
1. Add an entry in JSON schema validator file [`app/validators/json_schemas/application_setting_search.json`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/app/validators/json_schemas/application_setting_search.json)
1. Add the setting checkbox in the Admin UI by creating an entry in `global_search_settings_checkboxes` method in [`ApplicationSettingsHelper`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/app/helpers/application_settings_helper.rb`).
1. Add it to the `global_search_enabled_for_scope?` method in [`SearchService`](https://gitlab.com/gitlab-org/gitlab/-/blob/master/app/services/search_service.rb).
#### Results classes
The search results class available are:
| Search type | Search level | Class |
| -- | -- | -- |
| Basic search | global | `Gitlab::SearchResults` |
| Basic search | group | `Gitlab::GroupSearchResults` |
| Basic search | project | `Gitlab::ProjectSearchResults` |
| Advanced search | global | `Gitlab::Elastic::SearchResults` |
| Advanced search | group | `Gitlab::Elastic::GroupSearchResults` |
| Advanced search | project | `Gitlab::Elastic::ProjectSearchResults` |
| Exact code search | global | `Search::Zoekt::SearchResults` |
| Exact code search | group | `Search::Zoekt::SearchResults` |
| Exact code search | project | `Search::Zoekt::SearchResults` |
| All search types | All levels | `Search::EmptySearchResults` |
The result class returns the following data:
1. `objects` - paginated from Elasticsearch transformed into database records or POROs
1. `formatted_count` - document count returned from Elasticsearch
1. `highlight_map` - map of highlighted fields from Elasticsearch
1. `failed?` - if a failure occurred
1. `error` - error message returned from Elasticsearch
1. `aggregations` - (optional) aggregations from Elasticsearch
New scopes must add support to these methods within `Gitlab::Elastic::SearchResults` class:
- `objects`
- `formatted_count`
- `highlight_map`
- `failed?`
- `error`
### Building a query
The query builder framework is used to build Elasticsearch queries.
A query is built using:
- a query from `Search::Elastic::Queries`
- one or more filters from `::Search::Elastic::Filters`
- (optional) aggregations from `::Search::Elastic::Aggregations`
- one or more formats from `::Search::Elastic::Formats`
New scopes must create a new query builder class that inherits from `Search::Elastic::QueryBuilder`.
#### Understanding Queries vs Filters
Queries in Elasticsearch serve two key purposes: filtering documents and calculating relevance scores. When building
search functionality:
- **Queries** are essential when relevance scoring is required to rank results by how well they match search criteria.
They use the Boolean query's `must`, `should`, and `must_not` clauses, all of which influence the document's final
relevance score.
- **Filters** (within query context) determine whether documents appear in search results without affecting their score.
For search operations where results only need to be included/excluded without ranking by relevance, using filters
alone is more efficient and performs better at scale.
Choose the appropriate approach based on your search requirements - use queries with scoring clauses for ranked results,
and rely on filters for simple inclusion/exclusion logic.
#### Available Queries
All query builders must return a standardized `query_hash` structure that conforms to Elasticsearch's Boolean query
syntax. The `Search::Elastic::BoolExpr` class provides an interface for constructing Boolean queries.
The required query hash structure is:
```json
{
"query": {
"bool": {
"must": [],
"must_not": [],
"should": [],
"filters": [],
"minimum_should_match": null
}
}
}
```
##### `by_iid`
Query by `iid` field and document type. Requires `type` and `iid` fields.
```json
{
"query": {
"bool": {
"filter": [
{
"term": {
"iid": {
"_name": "milestone:related:iid",
"value": 1
}
}
},
{
"term": {
"type": {
"_name": "doc:is_a:milestone",
"value": "milestone"
}
}
}
]
}
}
}
```
##### `by_full_text`
Performs a full text search. This query will use `by_multi_match_query` or `by_simple_query_string` if Advanced search syntax is used in the query string. `by_multi_match_query` is behind the `search_uses_match_queries` feature flag.
##### `by_multi_match_query`
Uses `multi_match` Elasticsearch API. Can be customized with the following options:
- `count_only` - uses the Boolean query clause `filter`. Scoring and highlighting are not performed.
- `query` - if no query is passed, uses `match_all` Elasticsearch API
- `keyword_match_clause` - if `:should` is passed, uses the Boolean query clause `should`. Default: `must` clause
```json
{
"query": {
"bool": {
"must": [
{
"bool": {
"must": [],
"must_not": [],
"should": [
{
"multi_match": {
"_name": "project:multi_match:and:search_terms",
"fields": [
"name^10",
"name_with_namespace^2",
"path_with_namespace",
"path^9",
"description"
],
"query": "search",
"operator": "and",
"lenient": true
}
},
{
"multi_match": {
"_name": "project:multi_match_phrase:search_terms",
"type": "phrase",
"fields": [
"name^10",
"name_with_namespace^2",
"path_with_namespace",
"path^9",
"description"
],
"query": "search",
"lenient": true
}
}
],
"filter": [],
"minimum_should_match": 1
}
}
],
"must_not": [],
"should": [],
"filter": [],
"minimum_should_match": null
}
}
}
```
##### `by_simple_query_string`
Uses `simple_query_string` Elasticsearch API. Can be customized with the following options:
- `count_only` - uses the Boolean query clause `filter`. Scoring and highlighting are not performed.
- `query` - if no query is passed, uses `match_all` Elasticsearch API
- `keyword_match_clause` - if `:should` is passed, uses the Boolean query clause `should`. Default: `must` clause
```json
{
"query": {
"bool": {
"must": [
{
"simple_query_string": {
"_name": "project:match:search_terms",
"fields": [
"name^10",
"name_with_namespace^2",
"path_with_namespace",
"path^9",
"description"
],
"query": "search",
"lenient": true,
"default_operator": "and"
}
}
],
"must_not": [],
"should": [],
"filter": [],
"minimum_should_match": null
}
}
}
```
##### `by_knn`
Requires options: `vectors_supported` (set to `:elasticsearch` or `:opensearch`) and `embedding_field`. Callers may optionally provide options: `embeddings`
Performs a hybrid search using embeddings. Uses `full_text_search` unless embeddings are supported.
{{< alert type="warning" >}}
Elasticsearch and OpenSearch DSL for `knn` queries is different. To support both, this query must be used with the `by_knn` filter.
{{< /alert >}}
The example below is for Elasticsearch.
```json
{
"query": {
"bool": {
"must": [
{
"bool": {
"must": [],
"must_not": [],
"should": [
{
"multi_match": {
"_name": "work_item:multi_match:and:search_terms",
"fields": [
"iid^50",
"title^2",
"description"
],
"query": "test",
"operator": "and",
"lenient": true
}
},
{
"multi_match": {
"_name": "work_item:multi_match_phrase:search_terms",
"type": "phrase",
"fields": [
"iid^50",
"title^2",
"description"
],
"query": "test",
"lenient": true
}
}
],
"filter": [],
"minimum_should_match": 1
}
}
],
"must_not": [],
"should": [],
"filter": [],
"minimum_should_match": null
}
},
"knn": {
"field": "embedding_0",
"query_vector": [
0.030752448365092278,
-0.05360432341694832
],
"boost": 5,
"k": 25,
"num_candidates": 100,
"similarity": 0.6,
"filter": []
}
}
```
#### Available Filters
The query builder framework provides a collection of pre-built filters to handle common search scenarios. These filters
simplify the process of constructing complex query conditions without having to write raw Elasticsearch query DSL.
#### Filter Requirements and Usage
To use any filter:
1. The index mapping must include all required fields specified in each filter's documentation
1. Pass the appropriate parameters via the `options` hash when calling the filter
1. Each filter will generate the appropriate JSON structure and add it to your `query_hash`
Filters can be composed together to create sophisticated search queries while maintaining readable and maintainable
code. The following sections detail each available filter, its required fields, supported options, and example output.
##### `by_type`
Requires `type` field. Query with `doc_type` in options.
```json
{
"term": {
"type": {
"_name": "filters:doc:is_a:milestone",
"value": "milestone"
}
}
}
```
##### `by_group_level_confidentiality`
Requires `current_user` and `group_ids` fields. Query based on the permissions to user to read confidential group entities.
```json
{
"bool": {
"must": [
{
"term": {
"confidential": {
"value": true,
"_name": "confidential:true"
}
}
},
{
"terms": {
"namespace_id": [
1
],
"_name": "groups:can:read_confidential_work_items"
}
}
]
},
"should": {
"term": {
"confidential": {
"value": false,
"_name": "confidential:false"
}
}
}
}
```
##### `by_project_confidentiality`
Requires `confidential`, `author_id`, `assignee_id`, `project_id` fields. Query with `confidential` in options.
```json
{
"bool": {
"should": [
{
"term": {
"confidential": {
"_name": "filters:non_confidential",
"value": false
}
}
},
{
"bool": {
"must": [
{
"term": {
"confidential": {
"_name": "filters:confidential",
"value": true
}
}
},
{
"bool": {
"should": [
{
"term": {
"author_id": {
"_name": "filters:confidential:as_author",
"value": 1
}
}
},
{
"term": {
"assignee_id": {
"_name": "filters:confidential:as_assignee",
"value": 1
}
}
},
{
"terms": {
"_name": "filters:confidential:project:membership:id",
"project_id": [
12345
]
}
}
]
}
}
]
}
}
]
}
}
```
##### `by_label_ids`
Requires `label_ids` field. Query with `label_names` in options.
```json
{
"bool": {
"must": [
{
"terms": {
"_name": "filters:label_ids",
"label_ids": [
1
]
}
}
]
}
}
```
##### `by_archived`
Requires `archived` field. Query with `search_level` and `include_archived` in options.
```json
{
"bool": {
"_name": "filters:non_archived",
"should": [
{
"bool": {
"filter": {
"term": {
"archived": {
"value": false
}
}
}
}
},
{
"bool": {
"must_not": {
"exists": {
"field": "archived"
}
}
}
}
]
}
}
```
##### `by_state`
Requires `state` field. Supports values: `all`, `opened`, `closed`, and `merged`. Query with `state` in options.
```json
{
"match": {
"state": {
"_name": "filters:state",
"query": "opened"
}
}
}
```
##### `by_not_hidden`
Requires `hidden` field. Not applied for admins.
```json
{
"term": {
"hidden": {
"_name": "filters:not_hidden",
"value": false
}
}
}
```
##### `by_work_item_type_ids`
Requires `work_item_type_id` field. Query with `work_item_type_ids` or `not_work_item_type_ids` in options.
```json
{
"bool": {
"must_not": {
"terms": {
"_name": "filters:not_work_item_type_ids",
"work_item_type_id": [
8
]
}
}
}
}
```
##### `by_author`
Requires `author_id` field. Query with `author_username` or `not_author_username` in options.
```json
{
"bool": {
"should": [
{
"term": {
"author_id": {
"_name": "filters:author",
"value": 1
}
}
}
],
"minimum_should_match": 1
}
}
```
##### `by_target_branch`
Requires `target_branch` field. Query with `target_branch` or `not_target_branch` in options.
```json
{
"bool": {
"should": [
{
"term": {
"target_branch": {
"_name": "filters:target_branch",
"value": "master"
}
}
}
],
"minimum_should_match": 1
}
}
```
##### `by_source_branch`
Requires `source_branch` field. Query with `source_branch` or `not_source_branch` in options.
```json
{
"bool": {
"should": [
{
"term": {
"source_branch": {
"_name": "filters:source_branch",
"value": "master"
}
}
}
],
"minimum_should_match": 1
}
}
```
##### `by_search_level_and_group_membership`
Requires `current_user`, `group_ids`, `traversal_id`, `search_level` fields. Query with `search_level` and
filter on `namespace_visibility_level` based on permissions user has for each group.
{{< alert type="note" >}}
This filter can be used in place of `by_search_level_and_membership` if the data being searched does not contain the `project_id` field.
{{< /alert >}}
{{< alert type="note" >}}
Examples are shown for an authenticated user. The JSON may be different for users with authorizations, admins, external, or anonymous users
{{< /alert >}}
###### global
```json
{
"bool": {
"should": [
{
"bool": {
"filter": [
{
"term": {
"namespace_visibility_level": {
"value": 20,
"_name": "filters:namespace_visibility_level:public"
}
}
}
]
}
},
{
"bool": {
"filter": [
{
"term": {
"namespace_visibility_level": {
"value": 10,
"_name": "filters:namespace_visibility_level:internal"
}
}
}
]
}
},
{
"bool": {
"filter": [
{
"term": {
"namespace_visibility_level": {
"value": 0,
"_name": "filters:namespace_visibility_level:private"
}
}
},
{
"terms": {
"namespace_id": [
33,
22
]
}
}
]
}
}
],
"minimum_should_match": 1
}
}
```
###### group
```json
[
{
"bool": {
"_name": "filters:level:group",
"minimum_should_match": 1,
"should": [
{
"prefix": {
"traversal_ids": {
"_name": "filters:level:group:ancestry_filter:descendants",
"value": "22-"
}
}
}
]
}
},
{
"bool": {
"should": [
{
"bool": {
"filter": [
{
"term": {
"namespace_visibility_level": {
"value": 20,
"_name": "filters:namespace_visibility_level:public"
}
}
}
]
}
},
{
"bool": {
"filter": [
{
"term": {
"namespace_visibility_level": {
"value": 10,
"_name": "filters:namespace_visibility_level:internal"
}
}
}
]
}
},
{
"bool": {
"filter": [
{
"term": {
"namespace_visibility_level": {
"value": 0,
"_name": "filters:namespace_visibility_level:private"
}
}
},
{
"terms": {
"namespace_id": [
22
]
}
}
]
}
}
],
"minimum_should_match": 1
}
},
{
"bool": {
"_name": "filters:level:group",
"minimum_should_match": 1,
"should": [
{
"prefix": {
"traversal_ids": {
"_name": "filters:level:group:ancestry_filter:descendants",
"value": "22-"
}
}
}
]
}
}
]
```
##### `by_search_level_and_membership`
Requires `project_id`, `traversal_id` and project visibility (defaulting to `visibility_level` but can set with the `project_visibility_level_field` option) fields. Supports feature `*_access_level` fields. Query with `search_level`
and optionally `project_ids`, `group_ids`, `features`, and `current_user` in options.
Filtering is applied for:
- search level for global, group, or project
- membership for direct membership to groups and projects or shared membership through direct access to a group
- any feature access levels passed through `features`
{{< alert type="note" >}}
Examples are shown for a logged in user. The JSON may be different for users with authorizations, admins, external, or anonymous users
{{< /alert >}}
###### global
```json
{
"bool": {
"_name": "filters:permissions:global",
"should": [
{
"bool": {
"must": [
{
"terms": {
"_name": "filters:permissions:global:visibility_level:public_and_internal",
"visibility_level": [
20,
10
]
}
}
],
"should": [
{
"terms": {
"_name": "filters:permissions:global:repository_access_level:enabled",
"repository_access_level": [
20
]
}
}
],
"minimum_should_match": 1
}
},
{
"bool": {
"must": [
{
"bool": {
"should": [
{
"terms": {
"_name": "filters:permissions:global:repository_access_level:enabled_or_private",
"repository_access_level": [
20,
10
]
}
}
],
"minimum_should_match": 1
}
}
],
"should": [
{
"prefix": {
"traversal_ids": {
"_name": "filters:permissions:global:ancestry_filter:descendants",
"value": "123-"
}
}
},
{
"terms": {
"_name": "filters:permissions:global:project:member",
"project_id": [
456
]
}
}
],
"minimum_should_match": 1
}
}
],
"minimum_should_match": 1
}
}
```
###### group
```json
[
{
"bool": {
"_name": "filters:level:group",
"minimum_should_match": 1,
"should": [
{
"prefix": {
"traversal_ids": {
"_name": "filters:level:group:ancestry_filter:descendants",
"value": "123-"
}
}
}
]
}
},
{
"bool": {
"_name": "filters:permissions:group",
"should": [
{
"bool": {
"must": [
{
"terms": {
"_name": "filters:permissions:group:visibility_level:public_and_internal",
"visibility_level": [
20,
10
]
}
}
],
"should": [
{
"terms": {
"_name": "filters:permissions:group:repository_access_level:enabled",
"repository_access_level": [
20
]
}
}
],
"minimum_should_match": 1
}
},
{
"bool": {
"must": [
{
"bool": {
"should": [
{
"terms": {
"_name": "filters:permissions:group:repository_access_level:enabled_or_private",
"repository_access_level": [
20,
10
]
}
}
],
"minimum_should_match": 1
}
}
],
"should": [
{
"prefix": {
"traversal_ids": {
"_name": "filters:permissions:group:ancestry_filter:descendants",
"value": "123-"
}
}
}
],
"minimum_should_match": 1
}
}
],
"minimum_should_match": 1
}
}
]
```
###### project
```json
[
{
"bool": {
"_name": "filters:level:project",
"must": {
"terms": {
"project_id": [
456
]
}
}
}
},
{
"bool": {
"_name": "filters:permissions:project",
"should": [
{
"bool": {
"must": [
{
"terms": {
"_name": "filters:permissions:project:visibility_level:public_and_internal",
"visibility_level": [
20,
10
]
}
}
],
"should": [
{
"terms": {
"_name": "filters:permissions:project:repository_access_level:enabled",
"repository_access_level": [
20
]
}
}
],
"minimum_should_match": 1
}
},
{
"bool": {
"must": [
{
"bool": {
"should": [
{
"terms": {
"_name": "filters:permissions:project:repository_access_level:enabled_or_private",
"repository_access_level": [
20,
10
]
}
}
],
"minimum_should_match": 1
}
}
],
"should": [
{
"prefix": {
"traversal_ids": {
"_name": "filters:permissions:project:ancestry_filter:descendants",
"value": "123-"
}
}
}
],
"minimum_should_match": 1
}
}
],
"minimum_should_match": 1
}
}
]
```
##### `by_knn`
Requires options: `vectors_supported` (set to `:elasticsearch` or `:opensearch`) and `embedding_field`. Callers may optionally provide options: `embeddings`
{{< alert type="warning" >}}
Elasticsearch and OpenSearch DSL for `knn` queries is different. To support both, this filter must be used with the
`by_knn` query.
{{< /alert >}}
### Creating a filter
Filters are essential components in building effective Elasticsearch queries. They help narrow down search results
without affecting the relevance scoring.
- All filters must be documented.
- Filters are created as class level methods in `Search::Elastic::Filters`
- The method should start with `by_`.
- The method must take `query_hash` and `options` parameters only.
- `query_hash` is expected to contain a hash with this format.
```json
{ "query":
{ "bool":
{
"must": [],
"must_not": [],
"should": [],
"filters": [],
"minimum_should_match": null
}
}
}
```
- Use `add_filter` to add the filter to the query hash. Filters should add to the `filters` to avoid calculating score. The score calculation is done by the query itself.
- Use `context.name(:filters)` around the filter to add a name to the filter. This helps identify which part of a query and filter have allowed a result to be returned by the search
```ruby
def by_new_filter_type(query_hash:, options:)
filter_selected_value = options[:field_value]
context.name(:filters) do
add_filter(query_hash, :query, :bool, :filter) do
{ term: { field_name: { _name: context.name(:field_name), value: filter_selected_value } } }
end
end
end
```
### Sending queries to Elasticsearch
The queries are sent to `::Gitlab::Search::Client` from `Gitlab::Elastic::SearchResults`.
Results are parsed through a `Search::Elastic::ResponseMapper` to translate
the response from Elasticsearch.
#### Model requirements
The model must respond to the `to_ability_name` method so that the redaction logic can check if it has
`Ability.allowed?(current_user, :"read_#{object.to_ability_name}", object)?`. The method must be added if
it does not exist.
The model must define a `preload_search_data` scope to avoid N+1s.
### Permissions tests
Search code has a final security check in `SearchService#redact_unauthorized_results`. This prevents
unauthorized results from being returned to users who don't have permission to view them. The check is
done in Ruby to handle inconsistencies in Elasticsearch permissions data due to bugs or indexing delays.
New scopes must add visibility specs to ensure proper access control.
To test that permissions are properly enforced, add tests using the [`'search respects visibility'` shared example](https://gitlab.com/gitlab-org/gitlab/-/blob/a489ad0fe4b4d1e392272736b020cf9bd43646da/ee/spec/support/shared_examples/services/search_service_shared_examples.rb)
in the EE specs:
- `ee/spec/services/search/global_service_spec.rb`
- `ee/spec/services/search/group_service_spec.rb`
- `ee/spec/services/search/project_service_spec.rb`
### Testing the new scope
Test your new scope in the Rails console
```ruby
search_service = ::SearchService.new(User.first, { search: 'foo', scope: 'SCOPE_NAME' })
search_service.search_objects
```
### Recommended process for implementing search for a new document type
Create the following MRs and have them reviewed by a member of the Global Search team:
1. [Enable the new scope](#search-scopes).
1. Create a [query builder](#building-a-query).
1. Implement all [model requirements](#model-requirements).
1. [Add the new scope to `Gitlab::Elastic::SearchResults`](#results-classes) behind a feature flag.
1. Add specs which must include [permissions tests](#permissions-tests)
1. [Test the new scope](#testing-the-new-scope)
1. Update documentation for [Advanced search](../user/search/advanced_search.md) and [Search API](../api/search.md) (if applicable)
## Zero-downtime reindexing with multiple indices
{{< alert type="note" >}}
This is not applicable yet as multiple indices functionality is not fully implemented.
{{< /alert >}}
Currently GitLab can only handle a single version of setting. Any setting/schema changes would require reindexing everything from scratch. Since reindexing can take a long time, this can cause search functionality downtime.
To avoid downtime, GitLab is working to support multiple indices that
can function at the same time. Whenever the schema changes, the administrator
will be able to create a new index and reindex to it, while searches
continue to go to the older, stable index. Any data updates will be
forwarded to both indices. Once the new index is ready, an administrator can
mark it active, which will direct all searches to it, and remove the old
index.
This is also helpful for migrating to new servers, for example, moving to/from AWS.
Currently we are on the process of migrating to this new design. Everything is hardwired to work with one single version for now.
## Performance Monitoring
### Prometheus
GitLab exports [Prometheus metrics](../administration/monitoring/prometheus/gitlab_metrics.md)
relating to the number of requests and timing for all web/API requests and Sidekiq jobs,
which can help diagnose performance trends and compare how Elasticsearch timing
is impacting overall performance relative to the time spent doing other things.
#### Indexing queues
GitLab also exports [Prometheus metrics](../administration/monitoring/prometheus/gitlab_metrics.md)
for indexing queues, which can help diagnose performance bottlenecks and determine
whether or not your GitLab instance or Elasticsearch server can keep up with
the volume of updates.
### Logs
All of the indexing happens in Sidekiq, so much of the relevant logs for the
Elasticsearch integration can be found in
[`sidekiq.log`](../administration/logs/_index.md#sidekiqlog). In particular, all
Sidekiq workers that make requests to Elasticsearch in any way will log the
number of requests and time taken querying/writing to Elasticsearch. This can
be useful to understand whether or not your cluster is keeping up with
indexing.
Searching Elasticsearch is done via ordinary web workers handling requests. Any
requests to load a page or make an API request, which then make requests to
Elasticsearch, will log the number of requests and the time taken to
[`production_json.log`](../administration/logs/_index.md#production_jsonlog). These
logs will also include the time spent on Database and Gitaly requests, which
may help to diagnose which part of the search is performing poorly.
There are additional logs specific to Elasticsearch that are sent to
[`elasticsearch.log`](../administration/logs/_index.md#elasticsearchlog)
that may contain information to help diagnose performance issues.
### Performance Bar
Elasticsearch requests will be displayed in the
[`Performance Bar`](../administration/monitoring/performance/performance_bar.md), which can
be used both locally in development and on any deployed GitLab instance to
diagnose poor search performance. This will show the exact queries being made,
which is useful to diagnose why a search might be slow.
### Correlation ID and `X-Opaque-Id`
Our [correlation ID](distributed_tracing.md#developer-guidelines-for-working-with-correlation-ids)
is forwarded by all requests from Rails to Elasticsearch as the
[`X-Opaque-Id`](https://www.elastic.co/guide/en/elasticsearch/reference/current/tasks.html#_identifying_running_tasks)
header which allows us to track any
[tasks](https://www.elastic.co/guide/en/elasticsearch/reference/current/tasks.html)
in the cluster back the request in GitLab.
## Development tips
- [Kibana](advanced_search/tips.md#kibana)
- [Viewing index status](advanced_search/tips.md#viewing-index-status)
- [Creating indices from scratch](advanced_search/tips.md#creating-all-indices-from-scratch-and-populating-with-local-data)
- [Testing migrations](advanced_search/tips.md#testing-migrations)
- [Index data](advanced_search/tips.md#index-data)
## Troubleshooting
### Debugging Elasticsearch queries
The `ELASTIC_CLIENT_DEBUG` environment variable enables the [debug option for the Elasticsearch client](https://gitlab.com/gitlab-org/gitlab/-/blob/76bd885119795096611cb94e364149d1ef006fef/ee/lib/gitlab/elastic/client.rb#L50)
in development or test environments. If you need to debug Elasticsearch HTTP queries generated from
code or tests, it can be enabled before running specs or starting the Rails console:
```console
ELASTIC_CLIENT_DEBUG=1 bundle exec rspec ee/spec/workers/search/elastic/trigger_indexing_worker_spec.rb
export ELASTIC_CLIENT_DEBUG=1
rails console
```
### Getting `flood stage disk watermark [95%] exceeded`
You might get an error such as
```plaintext
[2018-10-31T15:54:19,762][WARN ][o.e.c.r.a.DiskThresholdMonitor] [pval5Ct]
flood stage disk watermark [95%] exceeded on
[pval5Ct7SieH90t5MykM5w][pval5Ct][/usr/local/var/lib/elasticsearch/nodes/0] free: 56.2gb[3%],
all indices on this node will be marked read-only
```
This is because you've exceeded the disk space threshold - it thinks you don't have enough disk space left, based on the default 95% threshold.
In addition, the `read_only_allow_delete` setting will be set to `true`. It will block indexing, `forcemerge`, etc
```shell
curl "http://localhost:9200/gitlab-development/_settings?pretty"
```
Add this to your `elasticsearch.yml` file:
```yaml
# turn off the disk allocator
cluster.routing.allocation.disk.threshold_enabled: false
```
_or_
```yaml
# set your own limits
cluster.routing.allocation.disk.threshold_enabled: true
cluster.routing.allocation.disk.watermark.flood_stage: 5gb # ES 6.x only
cluster.routing.allocation.disk.watermark.low: 15gb
cluster.routing.allocation.disk.watermark.high: 10gb
```
Restart Elasticsearch, and the `read_only_allow_delete` will clear on its own.
_from "Disk-based Shard Allocation | Elasticsearch Reference" [5.6](https://www.elastic.co/guide/en/elasticsearch/reference/5.6/disk-allocator.html#disk-allocator) and [6.x](https://www.elastic.co/guide/en/elasticsearch/reference/6.7/disk-allocator.html)_
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