In June 2019, Mario de la Ossa hosted a [Deep Dive] on GitLab's [Elasticsearch integration] to share his domain specific knowledge with anyone who may work in this part of the code base in the future. You can find the [recording on YouTube], and the slides on [Google Slides] and in [PDF]. Everything covered in this deep dive was accurate as of GitLab 12.0, and while specific details may have changed since then, it should still serve as a good introduction.
-`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 repos or multiple forks for testing, please consider [following these instructions](rake_tasks.md#extra-project-seed-options)
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).
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 please pay close attention to them!
Used when indexing a blob's filename and content. Uses the `whitespace` tokenizer and the filters: `code`, `edgeNGram_filter`, `lowercase`, and `asciifolding`
The `whitespace` tokenizer was selected in order 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)` in order to be properly searched.
Please see the `code` filter for an explanation on how tokens are split.
Not directly used for indexing, but rather used to transform a search input. Uses the `whitespace` tokenizer and the `lowercase` and `asciifolding` filters.
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 searcheable by any sub-set of it (minimum of 5 chars).
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` in order to allow searches to find paths no matter how much or how little of the path is given as input.
Uses a [Pattern Capture token filter](https://www.elastic.co/guide/en/elasticsearch/reference/5.5/analysis-pattern-capture-tokenfilter.html) to split tokens into more easily searched versions of themselves.
Uses an [Edge NGram token filter](https://www.elastic.co/guide/en/elasticsearch/reference/5.5/analysis-edgengram-tokenfilter.html) to allow inputs with only parts of a token to find the token. For example it would turn `glasses` into permutations starting with `gl` and ending with `glasses`, which would allow a search for "`glass`" to find the original token `glasses`
## 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, so they're not a good choice as they can hinder exact searches
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.
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 (e.g. `SnippetsSearch`). The `__elasticsearch__` methods would return a proxy object, e.g.:
-`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).
In the planned new design, each model would have a pair of corresponding subclassed proxy objects, in which model-specific logic is located. For example, `Snippet` would have `SnippetClassProxy` and `SnippetInstanceProxy` (being subclass of `Elasticsearch::Model::Proxy::ClassMethodsProxy` and `Elasticsearch::Model::Proxy::InstanceMethodsProxy`, respectively).
`__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.
Folders like `ee/lib/elastic/v12p1` contain snapshots of search logic from different versions. To keep a continuous Git history, the latest version lives under `ee/lib/elastic/latest`, but its classes are aliased under an actual version (e.g. `ee/lib/elastic/v12p3`). When referencing these classes, never use the `Latest` namespace directly, but use the actual version (e.g. `V12p3`).
The version name basically follows GitLab's release version. If setting is changed in 12.3, we will create a new namespace called `V12p3` (p stands for "point"). Raise an issue if there is a need to name a version differently.
