117 lines
4.0 KiB
Markdown
117 lines
4.0 KiB
Markdown
---
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stage: Create
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group: Code Review
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info: To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/engineering/ux/technical-writing/#assignments
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---
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# Code Intelligence **(FREE)**
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> [Introduced](https://gitlab.com/groups/gitlab-org/-/epics/1576) in GitLab 13.1.
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This document describes the design behind [Code Intelligence](../../user/project/code_intelligence.md).
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The built-in Code Intelligence in GitLab is powered by
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[LSIF](https://lsif.dev) and comes down to generating an LSIF document for a
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project in a CI job, processing the data, uploading it as a CI artifact and
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displaying this information for the files in the project.
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Here is a sequence diagram for uploading an LSIF artifact:
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```mermaid
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sequenceDiagram
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participant Runner
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participant Workhorse
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participant Rails
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participant Object Storage
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Runner->>+Workhorse: POST /v4/jobs/:id/artifacts
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Workhorse->>+Rails: POST /:id/artifacts/authorize
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Rails-->>-Workhorse: Respond with ProcessLsif header
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Note right of Workhorse: Process LSIF file
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Workhorse->>+Object Storage: Put file
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Object Storage-->>-Workhorse: request results
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Workhorse->>+Rails: POST /:id/artifacts
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Rails-->>-Workhorse: request results
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Workhorse-->>-Runner: request results
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```
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1. The CI/CD job generates a document in an LSIF format (usually `dump.lsif`) using
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[an indexer](https://lsif.dev) for the language of a project. The format
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[describes](https://github.com/sourcegraph/sourcegraph/blob/main/doc/code_intelligence/explanations/writing_an_indexer.md)
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interactions between a method or function and its definitions or references. The
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document is marked to be stored as an LSIF report artifact.
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1. After receiving a request for storing the artifact, Workhorse asks
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GitLab Rails to authorize the upload.
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1. GitLab Rails validates whether the artifact can be uploaded and sends
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`ProcessLsif: true` header if the LSIF artifact can be processed.
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1. Workhorse reads the LSIF document line by line and generates code intelligence
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data for each file in the project. The output is a zipped directory of JSON
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files which imitates the structure of the project:
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Project:
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```code
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app
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controllers
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application_controller.rb
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models
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application.rb
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```
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Generated data:
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```code
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app
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controllers
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application_controller.rb.json
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models
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application.rb.json
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```
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1. The zipped directory is stored as a ZIP artifact. Workhorse replaces the
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original LSIF document with a set of JSON files in the ZIP artifact and
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generates metadata for it. The metadata makes it possible to view a single
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file in a ZIP file without unpacking or loading the whole file. That allows us
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to access code intelligence data for a single file.
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1. When a file is viewed in the GitLab application, frontend fetches code
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intelligence data for the file directly from the object storage. The file
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contains information about code units in the file. For example:
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```json
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[
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{
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"definition_path": "cmd/check/main.go#L4",
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"hover": [
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{
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"language": "go",
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"tokens": [
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[
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{
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"class": "kn",
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"value": "package"
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},
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{
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"value": " "
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},
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{
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"class": "s",
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"value": "\"fmt\""
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}
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]
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]
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},
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{
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"value": "Package fmt implements formatted I/O with functions analogous to C's printf and scanf. The format 'verbs' are derived from C's but are simpler. \n\n### hdr-PrintingPrinting\nThe verbs: \n\nGeneral: \n\n```\n%v\tthe value in a default format\n\twhen printing st..."
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}
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],
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"start_char": 2,
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"start_line": 33
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}
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...
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]
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```
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