dosubot[bot] commented on issue #8181:
URL:
https://github.com/apache/incubator-devlake/issues/8181#issuecomment-2464157684
<!-- Answer -->
To optimize DevLake's performance, consider the following suggestions:
1. **Data Collection Configuration**:
- Use the **Time Filter** to limit the data collection to a specific time
range.
- Adjust the **Frequency** of data synchronization to balance between
data freshness and system load.
- Enable the **"Skip failed tasks"** option to allow the pipeline to
continue running even if some tasks fail, which helps in managing large data
volumes efficiently
<sup>[[1]](https://github.com/apache/incubator-devlake-website/blob/main/versioned_docs/version-v0.21/Configuration/Tutorial.md)</sup>.
2. **Parallel Processing**:
- Increase the `PIPELINE_MAX_PARALLEL` setting to allow multiple
pipelines to run concurrently, enhancing data collection speed
<sup>[[2]](https://github.com/apache/incubator-devlake-website/blob/main/versioned_docs/version-v0.20/Configuration/Tutorial.md)</sup>.
3. **Resource Allocation**:
- Ensure your system has sufficient CPU, memory, and disk resources. A
robust multi-core CPU, at least 16GB of RAM, and SSD storage are recommended
for handling large datasets.
4. **Data Collection Modes**:
- Use **Collect Data (Default)** mode for incremental refreshes where
supported.
- Opt for **Collect Data in Full Refresh Mode** if you need to re-collect
all data within a specific range.
- Use **Re-transform Data** to apply new transformation rules without
collecting new data
<sup>[[1]](https://github.com/apache/incubator-devlake-website/blob/main/versioned_docs/version-v0.21/Configuration/Tutorial.md)</sup>.
5. **Rate Limits**:
- Adjust rate limits for data sources like GitLab and Jira to optimize
data collection speed without exceeding API limits.
Implementing these strategies can help improve the performance and
efficiency of DevLake in handling large datasets.
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