debian-mirror-gitlab/doc/development/elasticsearch.md

Elasticsearch knowledge (STARTER ONLY)

This area is to maintain a compendium of useful information when working with elasticsearch.

Information on how to enable Elasticsearch and perform the initial indexing is kept in ../integration/elasticsearch.md#enabling-elasticsearch

Deep Dive

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.

Initial installation on OS X

It is recommended to use the Docker image. After installing docker you can immediately spin up an instance with

docker run --name elastic56 -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" docker.elastic.co/elasticsearch/elasticsearch:5.6.12

and use docker stop elastic56 and docker start elastic56 to stop/start it.

Installing on the host

We currently only support Elasticsearch 5.6 to 6.x

Version 5.6 is available on homebrew and is the recommended version to use in order to test compatibility.

brew install elasticsearch@5.6

There is no need to install any plugins

New repo indexer (beta)

If you're interested on working with the new beta repo indexer, all you need to do is:

• git clone git@gitlab.com:gitlab-org/gitlab-elasticsearch-indexer.git
• make
• make install

this adds gitlab-elasticsearch-indexer to $GOPATH/bin, please make sure that is in your $PATH. After that GitLab will find it and you'll be able to enable it in the admin settings area.

note: make will not recompile the executable unless you do make clean beforehand

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 repos or multiple forks for testing, please consider following these instructions

How does it work?

The Elasticsearch integration depends on an external indexer. We ship a ruby indexer by default but are also working on an indexer written in Go. 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_search.rb.

All indexing after the initial one is done via ElasticIndexerWorker (sidekiq jobs).

Search queries are generated by the concerns found in 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!

Existing Analyzers/Tokenizers/Filters

These are all defined in https://gitlab.com/gitlab-org/gitlab-ee/blob/master/ee/lib/elasticsearch/git/model.rb

Analyzers

path_analyzer

Used when indexing blobs' paths. Uses the path_tokenizer and the lowercase and asciifolding filters.

Please 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.

Please 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 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.

code_search_analyzer

Not directly used for indexing, but rather used to transform a search input. Uses the whitespace tokenizer and the lowercase and asciifolding filters.

Tokenizers

sha_tokenizer

This is a custom tokenizer that uses the edgeNGram tokenizer to allow SHAs to be searcheable 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 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.

Example:

'/some/path/application.js' becomes:

• '/some/path/application.js'
• 'some/path/application.js'
• 'path/application.js'
• 'application.js'

Filters

code

Uses a Pattern Capture token filter to split tokens into more easily searched versions of themselves.

Patterns:

• "(\\p{Ll}+|\\p{Lu}\\p{Ll}+|\\p{Lu}+)": captures CamelCased and lowedCameCased strings as separate tokens
• "(\\d+)": extracts digits
• "(?=([\\p{Lu}]+[\\p{L}]+))": captures CamelCased strings recursively. Ex: ThisIsATest => [ThisIsATest, IsATest, ATest, Test]
• '"((?:\\"|[^"]|\\")*)"': captures terms inside quotes, removing the quotes
• "'((?:\\'|[^']|\\')*)'": same as above, for single-quotes
• '\.([^.]+)(?=\.|\s|\Z)': separate terms with periods in-between
• '\/?([^\/]+)(?=\/|\b)': separate path terms like/this/one

edgeNGram_filter

Uses an Edge NGram token filter 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

Architecture

GitLab uses elasticsearch-rails for handling communication with Elasticsearch server. However, in order to achieve zero-downtime deployment during schema changes, an extra abstraction layer is built to allow:

• Indexing (writes) to multiple indexes, with different mappings
• Switching to different index for searches (reads) on the fly

Currently we are on the process of migrating models to this new design (e.g. Snippet), and it is hardwired to work with a single version for now.

Traditionally, elasticsearch-rails provides class and instance level __elasticsearch__ proxy methods. If you call Issue.__elasticsearch__, you will get an instance of Elasticsearch::Model::Proxy::ClassMethodsProxy, and if you call Issue.first.__elasticsearch__, you will get an instance of Elasticsearch::Model::Proxy::InstanceMethodsProxy. These proxy objects would talk to Elasticsearch server directly.

In the new design, __elasticsearch__ instead represents one extra layer of proxy. It would keep multiple versions of the actual proxy objects, and it would forward read and write calls to the proxy of the intended version.

The elasticsearch-rails's way of specifying each model's mappings and other settings is to create a module for the model to include. However in the new design, each model would have its own corresponding subclassed proxy object, where the settings reside in. For example, snippet related setting in the past reside in SnippetsSearch module, but in the new design would reside in SnippetClassProxy (which is a subclass of Elasticsearch::Model::Proxy::ClassMethodsProxy). This reduces namespace pollution in model classes.

The global configurations per version are now in the Elastic::(Version)::Config class. You can change mappings there.

Creating new version of schema

Currently GitLab would still work with a single version of setting. Once it is implemented, multiple versions of setting can exists in different folders (e.g. ee/lib/elastic/v12p1 and ee/lib/elastic/v12p3). To keep a continuous git history, the latest version lives under the /latest folder, but is aliased as the latest version.

If the current version is v12p1, and we need to create a new version for v12p3, the steps are as follows:

1. Copy the entire folder of v12p1 as v12p3
2. Change the namespace for files under v12p3 folder from V12p1 to V12p3 (which are still aliased to Latest)
3. Delete v12p1 folder
4. Copy the entire folder of latest as v12p1
5. Change the namespace for files under v12p1 folder from Latest to V12p1
6. Make changes to Latest as needed

Troubleshooting

Getting flood stage disk watermark [95%] exceeded

You might get an error such as

[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

curl "http://localhost:9200/gitlab-development/_settings?pretty"

Add this to your elasticsearch.yml file:

# turn off the disk allocator
cluster.routing.allocation.disk.threshold_enabled: false

or

# 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 it's own.

from "Disk-based Shard Allocation | Elasticsearch Reference" 5.6 and 6.x