pipeline failed gitlab что это

GitLab CI для непрерывной интеграции и доставки в production. Часть 1: наш пайплайн

Итак, GitLab CI: что можно ещё рассказать о нём? На хабре уже есть статьи про установку, про настройку раннеров, про командное использование, про GitLab Flow. Пожалуй, не хватает описаний того, как используется GitLab CI в реальном проекте, где задействовано несколько команд. А в современном мире разработки ПО это действительно так: ведь есть (как минимум) разработчики, тестировщики, DevOps- и релиз-инженеры. С подобным разделением на команды мы работаем уже несколько лет. В этой статье я расскажу о том, как мы, используя и улучшая возможности GitLab CI, реализовали и применяем в production для коллектива из нескольких команд процессы непрерывной интеграции (CI) и отчасти доставки приложений (CD).

Наш пайплайн

Если вы сталкивались с CI-системами ранее, то понятие пайплайна вам знакомо — это последовательность выполнения стадий (здесь и далее в статье для термина stage использую перевод «стадия»), каждая из которых включает несколько задач (здесь и далее в статье job — «задача»). От момента внесения изменений в код до выката в production приложение по очереди оказывается в руках разных команд — подобному тому, как это происходит на конвейере. Отсюда и возник термин pipeline («конвейер» — один из вариантов дословного перевода). В нашем случае это выглядит так:

Краткие пояснения по используемым стадиям:

Как это используется?

Начну с рассказа о пайплайне с точки зрения его использования — то, что можно назвать user story. Тут выяснится, что на самом деле пайплайна у нас даже два: укороченный для веток и полноценный для тегов. И вот как выглядят эти последовательности:

Пайплайн и стадии в деталях

Задачи на стадии build собирают приложение. Для этого мы используем свою разработку — Open Source-утилиту dapp (о её основных возможностях читайте и смотрите в этой статье + видео), которая хорошо ускоряет инкрементальную сборку. Поэтому сборка запускается автоматически для веток с префиксами feature_ (код приложения), infra_ (код инфраструктуры) и тегов, а не только для нескольких традиционно «главных» веток (master/develop/production/release).

Обновлено 06 сентября 2019 г.: в настоящее время проект dapp переименован в werf, его код переписан на Go, а документация значительно улучшена.

Следующая стадия — staging — это набор сред для разработчиков, DevOps-инженеров и тестировщиков. Здесь объявлено несколько задач, разворачивающих приложения из веток с префиксами feature_ и infra_ в урезанных средах для быстрого тестирования новой функциональности или инфраструктурных изменений (код сборки приложения хранится в репозитории приложения).

Стадии pre-production и production доступны только для тегов. Обычно тег вешается после того, как релиз-инженеры принимают несколько merge-запросов из протестированных веток. В целом можно сказать, что мы используем GitLab Flow с тем лишь отличием, что нет автоматического развёртывания на production и потому нет отдельных веток, а используются теги.

Стадия approve — это две задачи: approve и not approve. Первая включает возможность развёртывания на production, вторая — выключает. Эти задачи существуют для того, чтобы в случае проблем на production было видно, что развёртывание происходило не просто так, а с согласия релиз-инженера. Однако суть не в лишении кого-то премии, а в том, что непосредственно развёртывание на production проводит зачастую не сам релиз-инженер, а команда DevOps. Релиз-инженер, запуская задачу approve, разрешает тем самым «нажать на кнопку» deploy to production команде DevOps. Можно сказать, что эта стадия выносит на поверхность то, что могло бы остаться в почтовой переписке или в устной форме.

Такая схема взаимодействия хорошо себя показала в работе, однако пришлось потрудиться, чтобы реализовать её. Как оказалось, GitLab CI не поддерживает из коробки некоторые нужные вещи…

Всё довольно просто и скорее всего понятно. Для каждой задачи используются следующие директивы:

Он демонстрирует возможность формата YAML определять общие блоки и подключать их в нужное место на нужном уровне. Подробнее см. в документации.

Таким образом, задачи на стадиях build, testing, staging, pre-production, которые должны быть доступны для веток infra_, feature_ и тегов, принимают следующий вид:

А задачи на стадиях approve и production, которые доступны только для тегов, имеют такой вид:

Что дальше?

Полная реализация задуманного стала возможной только благодаря нескольким патчам к GitLab и использованию артефактов задач. Подробнее об этом читайте в следующей части статьи: «GitLab CI для непрерывной интеграции и доставки в production. Часть 2: преодолевая трудности».

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Why am I getting «Pipeline failed due to the user not being verified» & «Detached merge request pipeline» on a Gitlab merge request?

When a non-owner dev pushes a branch to our Gitlab repo, it returns a «pipeline failed» message, with the detail «Pipeline failed due to the user not being verified». On the dev’s account, he’s getting a prompt to add a credit card to verify him to be eligible for free pipeline minutes.

6 Answers 6

Hence you need a card with international transactions available. (if you’re not in the US).

I wonder why this declaration is not made on the website. Definitely doesn’t look good on part of such a large company as GitLab!

Gitlab updates about free pipeline minutes available on GitLab.com.

For all those still wondering, I contacted Gitlab recently & apparently it’s an open issue with them. They said it’s possible to merge the branches anyway, but in the end we just added the credit card details anyway (there was a temporary charge). Not ideal, but hopefully will get sorted soon.

All answer above is good, but maybe have a little bit misunderstanding about credit card preauthorization.

When we use credit card, shop will request bank freeze some credits (usually the total price) for this transaction. At a moment (depended on shop), they ask bank for payment and get cash. After this, bank send bill to user.

Preauthorization is a action of freeze credits.

If shop doesn’t ask bank for payment, bank will not give them cash and customer will not receive bill.

Preauthorization is a way that check validity of credit card. A common amount is one U.S. dollar. This is very common on Google Play and App Store when you add a new card.

Gitlab use this way to confirm whether the credit card is valid as same.

Although it depends on their internal operations, I think Gitlab does not need to cancel the transaction specifically, the only one thing need to do is that make sure they won’t ask bank pay for this preauthorization.

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Troubleshooting CI/CD

GitLab provides several tools to help make troubleshooting your pipelines easier.

This guide also lists common issues and possible solutions.

Verify syntax

An early source of problems can be incorrect syntax. The pipeline shows a yaml invalid badge and does not start running if any syntax or formatting problems are found.

Edit gitlab-ci.yml with the pipeline editor

If you prefer to use another editor, you can use a schema like the Schemastore gitlab-ci schema with your editor of choice.

Verify syntax with CI Lint tool

Verify variables

A key part of troubleshooting CI/CD is to verify which variables are present in a pipeline, and what their values are. A lot of pipeline configuration is dependent on variables, and verifying them is one of the fastest ways to find the source of a problem.

Export the full list of variables available in each problematic job. Check if the variables you expect are present, and check if their values are what you expect.

GitLab CI/CD documentation

You can also look at a large number of pipeline configuration examples and templates.

Documentation for pipeline types

Troubleshooting Guides for CI/CD features

Common CI/CD issues

A lot of common pipeline issues can be fixed by analyzing the behavior of the rules or only/except configuration. You shouldn’t use these two configurations in the same pipeline, as they behave differently. It’s hard to predict how a pipeline runs with this mixed behavior.

Jobs or pipelines don’t run when expected

The rules or only/except keywords are what determine whether or not a job is added to a pipeline. If a pipeline runs, but a job is not added to the pipeline, it’s usually due to rules or only/except configuration issues.

If a pipeline does not seem to run at all, with no error message, it may also be due to rules or only/except configuration, or the workflow: rules keyword.

If you are converting from only/except to the rules keyword, you should check the rules configuration details carefully. The behavior of only/except and rules is different and can cause unexpected behavior when migrating between the two.

The common if clauses for rules can be very helpful for examples of how to write rules that behave the way you expect.

Two pipelines run at the same time

Two pipelines can run when pushing a commit to a branch that has an open merge request associated with it. Usually one pipeline is a merge request pipeline, and the other is a branch pipeline.

This situation is usually caused by the rules configuration, and there are several ways to prevent duplicate pipelines.

A job is not in the pipeline

GitLab determines if a job is added to a pipeline based on the only/except or rules defined for the job. If it didn’t run, it’s probably not evaluating as you expect.

No pipeline or the wrong type of pipeline runs

Before a pipeline can run, GitLab evaluates all the jobs in the configuration and tries to add them to all available pipeline types. A pipeline does not run if no jobs are added to it at the end of the evaluation.

If a pipeline did not run, it’s likely that all the jobs had rules or only/except that blocked them from being added to the pipeline.

If the wrong pipeline type ran, then the rules or only/except configuration should be checked to make sure the jobs are added to the correct pipeline type. For example, if a merge request pipeline did not run, the jobs may have been added to a branch pipeline instead.

It’s also possible that your workflow: rules configuration blocked the pipeline, or allowed the wrong pipeline type.

A job runs unexpectedly

A common reason a job is added to a pipeline unexpectedly is because the changes keyword always evaluates to true in certain cases. For example, changes is always true in certain pipeline types, including scheduled pipelines and pipelines for tags.

The changes keyword is used in combination with only/except or rules ). It’s recommended to use changes with rules or only/except configuration that ensures the job is only added to branch pipelines or merge request pipelines.

“fatal: reference is not a tree” error

This occurs because the previous pipeline cannot find a checkout-SHA (which is associated with the pipeline record) from the example branch that the commit history has already been overwritten by the force-push. Similarly, Pipelines for merged results might have failed intermittently due to the same reason.

Merge request pipeline messages

The merge request pipeline widget shows information about the pipeline status in a merge request. It’s displayed above the ability to merge status widget.

“Checking pipeline status” message

After the pipeline is created, the message updates with the pipeline status.

Merge request status messages

The merge request status widget shows the Merge button and whether or not a merge request is ready to merge. If the merge request can’t be merged, the reason for this is displayed.

If the pipeline is still running, the Merge button is replaced with the Merge when pipeline succeeds button.

If Merge Trains are enabled, the button is either Add to merge train or Add to merge train when pipeline succeeds.

“A CI/CD pipeline must run and be successful before merge” message

This message is shown if the Pipelines must succeed setting is enabled in the project and a pipeline has not yet run successfully. This also applies if the pipeline has not been created yet, or if you are waiting for an external CI service. If you don’t use pipelines for your project, then you should disable Pipelines must succeed so you can accept merge requests.

“The pipeline for this merge request did not complete. Push a new commit to fix the failure or check the troubleshooting documentation to see other possible actions.” message

This message is shown if the merge request pipeline, merged results pipeline, or merge train pipeline has failed or been canceled.

Project group/project not found or access denied

“The parsed YAML is too big” message

This message displays when the YAML configuration is too large or nested too deeply. YAML files with a large number of includes, and thousands of lines overall, are more likely to hit this memory limit. For example, a YAML file that is 200kb is likely to hit the default memory limit.

On a self-managed instance, you can increase the size limits.

Pipeline warnings

“Job may allow multiple pipelines to run for a single action” warning

When you use rules with a when clause without an if clause, multiple pipelines may run. Usually this occurs when you push a commit to a branch that has an open merge request associated with it.

To prevent duplicate pipelines, use workflow: rules or rewrite your rules to control which pipelines can run.

Console workaround if job using resource_group gets stuck

How to get help

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CI/CD pipelines

Pipelines are the top-level component of continuous integration, delivery, and deployment.

Jobs are executed by runners. Multiple jobs in the same stage are executed in parallel, if there are enough concurrent runners.

If all jobs in a stage succeed, the pipeline moves on to the next stage.

If any job in a stage fails, the next stage is not (usually) executed and the pipeline ends early.

In general, pipelines are executed automatically and require no intervention once created. However, there are also times when you can manually interact with a pipeline.

Types of pipelines

Configure a pipeline

For a list of configuration options in the CI pipeline file, see the GitLab CI/CD Pipeline Configuration Reference.

Ref specs for runners

When a runner picks a pipeline job, GitLab provides that job’s metadata. This includes the Git refspecs, which indicate which ref (branch, tag, and so on) and commit (SHA1) are checked out from your project repository.

This table lists the refspecs injected for each pipeline type:

The refs refs/heads/ and refs/tags/ exist in your project repository. GitLab generates the special ref refs/pipelines/ during a running pipeline job. This ref can be created even after the associated branch or tag has been deleted. It’s therefore useful in some features such as automatically stopping an environment, and merge trains that might run pipelines after branch deletion.

View pipelines

You can find the current and historical pipeline runs under your project’s CI/CD > Pipelines page. You can also access pipelines for a merge request by navigating to its Pipelines tab.

Click a pipeline to open the Pipeline Details page and show the jobs that were run for that pipeline. From here you can cancel a running pipeline, retry jobs on a failed pipeline, or delete a pipeline.

Starting in GitLab 14.2, you can change the pipeline column to display the pipeline ID or the pipeline IID.

If you use VS Code to edit your GitLab CI/CD configuration, the GitLab Workflow VS Code extension helps you validate your configuration and view your pipeline status.

Run a pipeline manually

Pipelines can be manually executed, with predefined or manually-specified variables.

You might do this if the results of a pipeline (for example, a code build) are required outside the normal operation of the pipeline.

The pipeline now executes the jobs as configured.

Prefill variables in manual pipelines

You can use the value and description keywords to define pipeline-level (global) variables that are prefilled when running a pipeline manually.

The description is displayed next to the variable. It can be used to explain what the variable is used for, what the acceptable values are, and so on:

You cannot set job-level variables to be pre-filled when you run a pipeline manually.

Run a pipeline by using a URL query string

The format of the pipelines/new URL is:

Add manual interaction to your pipeline

Manual jobs, allow you to require manual interaction before moving forward in the pipeline.

You can do this straight from the pipeline graph. Just click the play button to execute that particular job.

For example, your pipeline can start automatically, but require a manual action to deploy to production. In the example below, the production stage has a job with a manual action:

Start multiple manual actions in a stage

Multiple manual actions in a single stage can be started at the same time using the “Play all manual” button. After you click this button, each individual manual action is triggered and refreshed to an updated status.

Skip a pipeline

Alternatively, if you are using Git 2.10 or later, use the ci.skip Git push option. The ci.skip push option does not skip merge request pipelines.

Delete a pipeline

Users with the Owner role in a project can delete a pipeline by clicking on the pipeline in the CI/CD > Pipelines to get to the Pipeline Details page, then using the Delete button.

Quotas of CI/CD minutes

Each user has a personal pipeline quota that tracks the usage of shared runners in all personal projects. Each group has a quota of CI/CD minutes that tracks the usage of shared runners for all projects created within the group.

When a pipeline is triggered, regardless of who triggered it, the quota of CI/CD minutes for the project owner’s namespace is used. In this case, the namespace can be the user or group that owns the project.

How pipeline duration is calculated

Total running time for a given pipeline excludes retries and pending (queued) time.

Visually, it can be viewed as:

The union of A, B, and C is (1, 4) and (6, 7). Therefore, the total running time is:

How pipeline quota usage is calculated

Pipeline quota usage is calculated as the sum of the duration of each individual job. This is slightly different to how pipeline duration is calculated. Pipeline quota usage doesn’t consider any overlap of jobs running in parallel.

The pipeline quota usage is the sum of each job’s duration. In this example, 8 runner minutes would be used, calculated as: 3 + 3 + 2.

Pipeline security on protected branches

A strict security model is enforced when pipelines are executed on protected branches.

Variables marked as protected are accessible only to jobs that run on protected branches, preventing untrusted users getting unintended access to sensitive information like deployment credentials and tokens.

Runners marked as protected can run jobs only on protected branches, preventing untrusted code from executing on the protected runner and preserving deployment keys and other credentials from being unintentionally accessed. In order to ensure that jobs intended to be executed on protected runners do not use regular runners, they must be tagged accordingly.

Visualize pipelines

Pipelines can be complex structures with many sequential and parallel jobs.

To make it easier to understand the flow of a pipeline, GitLab has pipeline graphs for viewing pipelines and their statuses.

Pipeline graphs can be displayed as a large graph or a miniature representation, depending on the page you access the graph from.

GitLab capitalizes the stages’ names in the pipeline graphs.

View full pipeline graph

The pipeline details page displays the full pipeline graph of all the jobs in the pipeline.

You can group the jobs by:

Stage, which arranges jobs in the same stage together in the same column:

Job dependencies, which arranges jobs based on their needs dependencies.

Multi-project pipeline graphs help you visualize the entire pipeline, including all cross-project inter-dependencies.

View job dependencies in the pipeline graph

You can arrange jobs in the pipeline graph based on their needs dependencies.

Jobs in the leftmost column run first, and jobs that depend on them are grouped in the next columns.

For example, test-job1 depends only on jobs in the first column, so it displays in the second column from the left. deploy-job1 depends on jobs in both the first and second column and displays in the third column:

To add lines that show the needs relationships between jobs, select the Show dependencies toggle. These lines are similar to the needs visualization:

To see the full needs dependency tree for a job, hover over it:

Pipeline mini graphs

Pipeline mini graphs allow you to see all related jobs for a single commit and the net result of each stage of your pipeline. This allows you to quickly see what failed and fix it.

Pipeline mini graphs only display jobs by stage.

Stages in pipeline mini graphs are collapsible. Hover your mouse over them and click to expand their jobs.

Mini graph	Mini graph expanded

Pipeline success and duration charts

Pipeline analytics are available on the CI/CD Analytics page.

Pipeline badges

Pipeline status and test coverage report badges are available and configurable for each project. For information on adding pipeline badges to projects, see Pipeline badges.

Pipelines API

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Pipeline failed gitlab что это

\\App\\Providers::AppServiceProvider\n Methods: 100.00% ( 2\/ 2) Lines: 100.00% ( 3\/ 3)\n\\App\\Providers::AuthServiceProvider\n Methods: 100.00% ( 1\/ 1) Lines: 100.00% ( 2\/ 2)\n\\App\\Providers::EventServiceProvider\n Methods: 100.00% ( 1\/ 1) Lines: 100.00% ( 2\/ 2)\n\\App\\Providers::RouteServiceProvider\n Methods: 100.00% ( 4\/ 4) Lines: 100.00% ( 14\/ 14)\nsection_end:1508582118:build_script\nsection_start:1508582118:after_script\nsection_end:1508582119:after_script\nERROR: Job failed: exit code 1\n\n \n»,»body_in_markdown»:»Anyone knows what this error means? I can’t see what the problem is\r\n\r\n

\r\n\\App\\Providers::AppServiceProvider\r\n Methods: 100.00% ( 2\/ 2) Lines: 100.00% ( 3\/ 3)\r\n\\App\\Providers::AuthServiceProvider\r\n Methods: 100.00% ( 1\/ 1) Lines: 100.00% ( 2\/ 2)\r\n\\App\\Providers::EventServiceProvider\r\n Methods: 100.00% ( 1\/ 1) Lines: 100.00% ( 2\/ 2)\r\n\\App\\Providers::RouteServiceProvider\r\n Methods: 100.00% ( 4\/ 4) Lines: 100.00% ( 14\/ 14)\r\nsection_end:1508582118:build_script\r\nsection_start:1508582118:after_script\r\nsection_end:1508582119:after_script\r\nERROR: Job failed: exit code 1\r\n\r\n

I can’t see that the issue is either, I found pipeline’s be to a little unreliable. \n

true, easy enough with the secret token. \n

And it works on the next pipeline. \n

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