dosubot[bot] commented on issue #7826:
URL:
https://github.com/apache/incubator-devlake/issues/7826#issuecomment-2265291246
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The disappearance of Jira issues from the dataset in DevLake, especially for
projects with a large number of issues, could be due to the way the `id` field
is constructed and used as a primary key. According to the schema, the `id` for
Jira issues is composed of several parts, including the Jira source ID and the
Jira issue ID. If there are inconsistencies or collisions in these IDs, it
could lead to issues not being properly recorded or retrieved.
To resolve this, ensure that the `id` field is correctly and uniquely
constructed for each Jira issue. Verify that the Jira source ID and Jira issue
ID are correctly captured and concatenated to form a unique identifier for each
issue. Additionally, check for any potential data truncation or transformation
issues that might affect the uniqueness of the `id` field.
Here is the relevant part of the schema for reference:
```md
| `id` | varchar | 255 | An issue's `id` is
composed of < plugin >:< Entity >:< PK0 >[:PK1]..." <ul><li>For Github issues,
a Github issue's id is like "github:GithubIssues:< GithubIssueId >". Eg.
'github:GithubIssues:1049355647'</li> <li>For Jira issues, a Jira issue's id is
like "jira:JiraIssues:< JiraSourceId >:< JiraIssueId >". Eg.
'jira:JiraIssues:1:10063'. < JiraSourceId > is used to identify which jira
source the issue came from, since DevLake users can import data from several
different Jira instances at the same time.</li></ul>
| PK |
```
Ensure that the `JiraSourceId` and `JiraIssueId` are correctly and uniquely
assigned to avoid any conflicts or data loss. Additionally, consider reviewing
the domain layer schema and the data flow architecture to ensure that the data
transformation processes are correctly handling the unique identifiers and not
causing any data inconsistencies
<sup>[[1]](https://github.com/apache/incubator-devlake-website/blob/main/versioned_docs/version-v0.15/DataModels/DevLakeDomainLayerSchema.md)</sup><sup>[[2]](https://github.com/apache/incubator-devlake-website/blob/main/versioned_docs/version-v0.16/DataModels/DevLakeDomainLayerSchema.md)</sup>.
Furthermore, if you are using webhooks, ensure that each project has its own
webhook. DevLake determines the project an issue or deployment belongs to by
segregating between the webhooks. If a webhook is used by multiple projects,
all issues or deployments published by that webhook will be replicated among
those projects, leading to potential data inconsistencies. Each project should
have its own dedicated webhook to avoid such issues
<sup>[[3]](https://github.com/apache/incubator-devlake-website/blob/main/docs/Troubleshooting/Dashboard.md)</sup><sup>[[4]](https://github.com/apache/incubator-devlake-website/blob/main/versioned_docs/version-v0.21/Troubleshooting/Dashboard.md)</sup><sup>[[5]](https://github.com/apache/incubator-devlake-website/blob/main/versioned_docs/version-v0.19/Troubleshooting/Dashboard.md)</sup>.
For further troubleshooting, you can refer to the DORA Validation Dashboard
in DevLake, which provides a step-by-step guide to check which step went wrong
for all four DORA metrics. This can help you identify if the issue lies in the
data mapping or querying process. Each chart in the dashboard has a hidden
button in the top-right corner to access the context menu, where you can click
`Edit` to view the SQL scripts behind the charts and understand how the data is
queried
<sup>[[3]](https://github.com/apache/incubator-devlake-website/blob/main/docs/Troubleshooting/Dashboard.md)</sup><sup>[[4]](https://github.com/apache/incubator-devlake-website/blob/main/versioned_docs/version-v0.21/Troubleshooting/Dashboard.md)</sup><sup>[[5]](https://github.com/apache/incubator-devlake-website/blob/main/versioned_docs/version-v0.19/Troubleshooting/Dashboard.md)</sup>.
If none of these solutions resolve your problem, consider creating an issue
on the DevLake GitHub repository for further assistance.
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