Подготовка кода для анализа CodeQL

About preparing your code for analysis

Before you analyze your code using CodeQL, you need to create a CodeQL database containing all the data required to run queries on your code. You can create CodeQL databases yourself using the CodeQL CLI.

CodeQL analysis relies on extracting relational data from your code, and using it to build a CodeQL database. CodeQL databases contain all of the important information about a codebase, which can be analyzed by executing CodeQL queries against it.

Before you generate a CodeQL database, you need to:

1. Install and set up the CodeQL CLI. For more information, see "Setting up the CodeQL CLI."
2. Check out the code that you want to analyze:
   • For a branch, check out the head of the branch that you want to analyze.
   • For a pull request, check out either the head commit of the pull request, or check out a GitHub-generated merge commit of the pull request.
3. Set up the environment for the codebase, making sure that any dependencies are available.
4. For the best results with compiled languages, find the build command, if any, for the codebase. Typically this is available in a configuration file in the CI system.

Once the codebase is ready, you can run codeql database create to create the database. For more information, see "Creating databases for non-compiled languages" and "Creating databases for compiled languages."

Running codeql database create

CodeQL databases are created by running the following command from the checkout root of your project:

codeql database create <database> --language=<language-identifier>

You must specify:

• <database>: a path to the new database to be created. This directory will be created when you execute the command—you cannot specify an existing directory.

• --language: the identifier for the language to create a database for. When used with --db-cluster, the option accepts a comma-separated list, or can be specified more than once. CodeQL supports creating databases for the following languages:

  Language	Identifier
  C/C++	cpp
  C#	csharp
  Go	go
  Java/Kotlin	java
  JavaScript/TypeScript	javascript
  Python	python
  Ruby	ruby

  Note: CodeQL analysis for Kotlin is currently in beta. During the beta, analysis of Kotlin code, and the accompanying documentation, will not be as comprehensive as for other languages.

If your codebase has a build command or script that invokes the build process, we recommend that you specify it as well:

   codeql database create <database> --command <build> \
         --language=<language-identifier>

Options for creating databases

You can specify additional options depending on the location of your source file, if the code needs to be compiled, and if you want to create CodeQL databases for more than one language.

Option	Required	Usage
<database>		Specify the name and location of a directory to create for the CodeQL database. The command will fail if you try to overwrite an existing directory. If you also specify --db-cluster, this is the parent directory and a subdirectory is created for each language analyzed.
--language		Specify the identifier for the language to create a database for, one of: cpp, csharp, go, java, javascript, python, and ruby (use javascript to analyze TypeScript code and java to analyze Kotlin code). When used with --db-cluster, the option accepts a comma-separated list, or can be specified more than once.
--command		Recommended. Use to specify the build command or script that invokes the build process for the codebase. Commands are run from the current folder or, where it is defined, from --source-root. Not needed for Python and JavaScript/TypeScript analysis.
--db-cluster		Use in multi-language codebases to generate one database for each language specified by --language.
--no-run-unnecessary-builds		Recommended. Use to suppress the build command for languages where the CodeQL CLI does not need to monitor the build (for example, Python and JavaScript/TypeScript).
--source-root		Use if you run the CLI outside the checkout root of the repository. By default, the database create command assumes that the current directory is the root directory for the source files, use this option to specify a different location.
--codescanning-config		Advanced. Use if you have a configuration file that specifies how to create the CodeQL databases and what queries to run in later steps. For more information, see "Customizing your advanced setup for code scanning" and "database create."

You can specify extractor options to customize the behavior of extractors that create CodeQL databases. For more information, see "Extractor options."

For full details of all the options you can use when creating databases, see "database create."

Single language example

This example creates a single CodeQL database for the repository checked out at /checkouts/example-repo. It uses the JavaScript extractor to create a hierarchical representation of the JavaScript and TypeScript code in the repository. The resulting database is stored in /codeql-dbs/example-repo.

$ codeql database create /codeql-dbs/example-repo --language=javascript \
    --source-root /checkouts/example-repo

> Initializing database at /codeql-dbs/example-repo.
> Running command [/codeql-home/codeql/javascript/tools/autobuild.cmd]
    in /checkouts/example-repo.
> [build-stdout] Single-threaded extraction.
> [build-stdout] Extracting
...
> Finalizing database at /codeql-dbs/example-repo.
> Successfully created database at /codeql-dbs/example-repo.

Multiple language example

This example creates two CodeQL databases for the repository checked out at /checkouts/example-repo-multi. It uses:

• --db-cluster to request analysis of more than one language.
• --language to specify which languages to create databases for.
• --command to tell the tool the build command for the codebase, here make.
• --no-run-unnecessary-builds to tell the tool to skip the build command for languages where it is not needed (like Python).

The resulting databases are stored in python and cpp subdirectories of /codeql-dbs/example-repo-multi.

$ codeql database create /codeql-dbs/example-repo-multi \
    --db-cluster --language python,cpp \
    --command make --no-run-unnecessary-builds \
    --source-root /checkouts/example-repo-multi
Initializing databases at /codeql-dbs/example-repo-multi.
Running build command: [make]
[build-stdout] Calling python3 /codeql-bundle/codeql/python/tools/get_venv_lib.py
[build-stdout] Calling python3 -S /codeql-bundle/codeql/python/tools/python_tracer.py -v -z all -c /codeql-dbs/example-repo-multi/python/working/trap_cache -p ERROR: 'pip' not installed.
[build-stdout] /usr/local/lib/python3.6/dist-packages -R /checkouts/example-repo-multi
[build-stdout] [INFO] Python version 3.6.9
[build-stdout] [INFO] Python extractor version 5.16
[build-stdout] [INFO] [2] Extracted file /checkouts/example-repo-multi/hello.py in 5ms
[build-stdout] [INFO] Processed 1 modules in 0.15s
[build-stdout] <output from calling 'make' to build the C/C++ code>
Finalizing databases at /codeql-dbs/example-repo-multi.
Successfully created databases at /codeql-dbs/example-repo-multi.
$

Progress and results

Errors are reported if there are any problems with the options you have specified. For interpreted languages and when you specify --build-mode none for Java, the extraction progress is displayed in the console. For each source file, the console shows if extraction was successful or if it failed. When a compiled language is built, the console will display the output of the build system.

When the database is successfully created, you’ll find a new directory at the path specified in the command. If you used the --db-cluster option to create more than one database, a subdirectory is created for each language. Each CodeQL database directory contains a number of subdirectories, including the relational data (required for analysis) and a source archive—a copy of the source files made at the time the database was created—which is used for displaying analysis results.

Creating databases for non-compiled languages

The CodeQL CLI includes extractors to create databases for non-compiled languages—specifically, JavaScript (and TypeScript), Python, and Ruby. These extractors are automatically invoked when you specify JavaScript, Python, or Ruby as the --language option when executing database create. When creating databases for these languages you must ensure that all additional dependencies are available.

JavaScript and TypeScript

Creating databases for JavaScript requires no additional dependencies, but if the project includes TypeScript files, Node.js 14 or higher must be installed and available on the PATH as node. In the command line you can specify --language=javascript to extract both JavaScript and TypeScript files:

codeql database create --language=javascript --source-root <folder-to-extract> <output-folder>/javascript-database

Here, we have specified a --source-root path, which is the location where database creation is executed, but is not necessarily the checkout root of the codebase.

By default, files in node_modules and bower_components directories are not extracted.

Python

When creating databases for Python you must ensure:

• You have Python 3 installed and available to the CodeQL extractor.
• You have the version of Python used by your code installed.
• You have access to the pip packaging management system and can install any packages that the codebase depends on.
• You have installed the virtualenv pip module.

In the command line you must specify --language=python. For example:

codeql database create --language=python <output-folder>/python-database

This executes the database create subcommand from the code’s checkout root, generating a new Python database at <output-folder>/python-database.

Ruby

Creating databases for Ruby requires no additional dependencies. In the command line you must specify --language=ruby. For example:

codeql database create --language=ruby --source-root <folder-to-extract> <output-folder>/ruby-database

Here, we have specified a --source-root path, which is the location where database creation is executed, but is not necessarily the checkout root of the codebase.

Creating databases for compiled languages

For compiled languages, CodeQL needs to invoke the required build system to generate a database, therefore the build method must be available to the CLI. This approach creates databases that include generated code. CodeQL has two methods for building codebases:

• "Automatic build detection (autobuild)"
• "User-specified build commands"

Automatically detecting the build system

The CodeQL CLI includes autobuilders for C/C++, C#, Go, and Java code. CodeQL autobuilders allow you to build projects for compiled languages without specifying any build commands. When an autobuilder is invoked, CodeQL examines the source for evidence of a build system and attempts to run the optimal set of commands required to extract a database. For more information, see "CodeQL code scanning for compiled languages."

An autobuilder is invoked automatically when you execute codeql database create for a compiled language if you don’t include a --command option. For example, for a C/C++ codebase, you could simply run:

codeql database create --language=cpp <output-folder>/cpp-database

If a codebase uses a standard build system, relying on an autobuilder is often the simplest way to create a database. For sources that require non-standard build steps, you may need to explicitly define each step in the command line.

Specifying build commands

The following examples are designed to give you an idea of some of the build commands that you can specify for compiled languages.

• C/C++ project built using make:

  codeql database create cpp-database --language=cpp --command=make
  

• C# project built using dotnet build:

  It is a good idea to add /t:rebuild to ensure that all code will be built, or do a prior dotnet clean (code that is not built will not be included in the CodeQL database):

  codeql database create csharp-database --language=csharp --command='dotnet build /t:rebuild'
  

• Go project built using the CODEQL_EXTRACTOR_GO_BUILD_TRACING=on environment variable:

  CODEQL_EXTRACTOR_GO_BUILD_TRACING=on codeql database create go-database --language=go
  

• Go project built using a custom build script:

  codeql database create go-database --language=go --command='./scripts/build.sh'
  

• Java project built using Gradle:

  # Use `--no-daemon` because a build delegated to an existing daemon cannot be detected by CodeQL.
  # To ensure isolated builds without caching, add `--no-build-cache` on persistent machines.
  codeql database create java-database --language=java --command='gradle --no-daemon clean test'
  

• Java project built using Maven:

  codeql database create java-database --language=java --command='mvn clean install'
  

• Java project built using Ant:

  codeql database create java-database --language=java --command='ant -f build.xml'
  

• Project built using Bazel:

  # Navigate to the Bazel workspace.
  
  # Before building, remove cached objects
  # and stop all running Bazel server processes.
  bazel clean --expunge
  
  # Build using the following Bazel flags, to help CodeQL detect the build:
  # `--spawn_strategy=local`: build locally, instead of using a distributed build
  # `--nouse_action_cache`: turn off build caching, which might prevent recompilation of source code
  # `--noremote_accept_cached`, `--noremote_upload_local_results`: avoid using a remote cache
  # `--disk_cache=`: avoid using a disk cache. Note that a disk cache is no longer considered a remote cache as of Bazel 6.
  codeql database create new-database --language=<language> \
  --command='bazel build --spawn_strategy=local --nouse_action_cache --noremote_accept_cached --noremote_upload_local_results --disk_cache= //path/to/package:target'
  
  # After building, stop all running Bazel server processes.
  # This ensures future build commands start in a clean Bazel server process
  # without CodeQL attached.
  bazel shutdown
  

• Project built using a custom build script:

  codeql database create new-database --language=<language> --command='./scripts/build.sh'
  

This command runs a custom script that contains all of the commands required to build the project.

Using indirect build tracing

If the CodeQL CLI autobuilders for compiled languages do not work with your CI workflow and you cannot wrap invocations of build commands with codeql database trace-command, you can use indirect build tracing to create a CodeQL database. To use indirect build tracing, your CI system must be able to set custom environment variables for each build action.

To create a CodeQL database with indirect build tracing, run the following command from the checkout root of your project:

codeql database init ... --begin-tracing <database>

You must specify:

• <database>: a path to the new database to be created. This directory will be created when you execute the command—you cannot specify an existing directory.
• --begin-tracing: creates scripts that can be used to set up an environment in which build commands will be traced.

You may specify other options for the codeql database init command as normal.

The codeql database init command will output a message:

Created skeleton <database>. This in-progress database is ready to be populated by an extractor. In order to initialise tracing, some environment variables need to be set in the shell your build will run in. A number of scripts to do this have been created in <database>/temp/tracingEnvironment. Please run one of these scripts before invoking your build command.

Based on your operating system, we recommend you run: ...

The codeql database init command creates <database>/temp/tracingEnvironment with files that contain environment variables and values that will enable CodeQL to trace a sequence of build steps. These files are named start-tracing.{json,sh,bat,ps1}. Use one of these files with your CI system’s mechanism for setting environment variables for future steps. You can:

• Read the JSON file, process it, and print out environment variables in the format expected by your CI system. For example, Azure DevOps expects echo "##vso[task.setvariable variable=NAME]VALUE".
• Or, if your CI system persists the environment, source the appropriate start-tracing script to set the CodeQL variables in the shell environment of the CI system.

Build your code; optionally, unset the environment variables using an end-tracing.{json,sh,bat,ps1} script from the directory where the start-tracing scripts are stored; and then run the command codeql database finalize <database>.

Once you have created a CodeQL database using indirect build tracing, you can work with it like any other CodeQL database. For example, analyze the database, and upload the results to GitHub if you use code scanning.

Example of creating a CodeQL database using indirect build tracing

The following example shows how you could use indirect build tracing in an Azure DevOps pipeline to create a CodeQL database:

steps:
    # Download the CodeQL CLI and query packs...
    # Check out the repository ...

    # Run any pre-build tasks, for example, restore NuGet dependencies...

    # Initialize the CodeQL database.
    # In this example, the CodeQL CLI has been downloaded and placed on the PATH.
    - task: CmdLine@1
       displayName: Initialize CodeQL database
      inputs:
          # Assumes the source code is checked out to the current working directory.
          # Creates a database at `<current working directory>/db`.
          # Running on Windows, so specifies a trace process level.
          script: "codeql database init --language csharp --trace-process-name Agent.Worker.exe --source-root . --begin-tracing db"

    # Read the generated environment variables and values,
    # and set them so they are available for subsequent commands
    # in the build pipeline. This is done in PowerShell in this example.
    - task: PowerShell@1
       displayName: Set CodeQL environment variables
       inputs:
          targetType: inline
          script: >
             $json = Get-Content $(System.DefaultWorkingDirectory)/db/temp/tracingEnvironment/start-tracing.json | ConvertFrom-Json
             $json.PSObject.Properties | ForEach-Object {
                 $template = "##vso[task.setvariable variable="
                 $template += $_.Name
                 $template += "]"
                 $template += $_.Value
                 echo "$template"
             }

    # Execute the pre-defined build step. Note the `msbuildArgs` variable.
    - task: VSBuild@1
        inputs:
          solution: '**/*.sln'
          msbuildArgs: /p:OutDir=$(Build.ArtifactStagingDirectory)
          platform: Any CPU
          configuration: Release
          # Execute a clean build, in order to remove any existing build artifacts prior to the build.
          clean: True
       displayName: Visual Studio Build

    # Read and set the generated environment variables to end build tracing. This is done in PowerShell in this example.
    - task: PowerShell@1
       displayName: Clear CodeQL environment variables
       inputs:
          targetType: inline
          script: >
             $json = Get-Content $(System.DefaultWorkingDirectory)/db/temp/tracingEnvironment/end-tracing.json | ConvertFrom-Json
             $json.PSObject.Properties | ForEach-Object {
                 $template = "##vso[task.setvariable variable="
                 $template += $_.Name
                 $template += "]"
                 $template += $_.Value
                 echo "$template"
             }

    - task: CmdLine@2
       displayName: Finalize CodeQL database
       inputs:
          script: 'codeql database finalize db'

    # Other tasks go here, for example:
    # `codeql database analyze`
    # then `codeql github upload-results` ...

Next steps

• To learn how to use the CodeQL CLI to analyze the database you created from your code, see "Analyzing your code with CodeQL queries."