What the data for this category looks like
Core data for search ad keyword management comes from search ad backends, search term reports, bidding ranking data, and landing page data. Daily processed documents include keyword lists, negative keyword tables, bid records, and search term reports. Fields covered include keyword text, match type, single bid amount, click volume, conversion cost, landing page jump links, and more. Data update frequency is typically daily or weekly, with synchronization rhythms varying across ad accounts. Unlike feed ad creative testing, this category centers on user-initiated search terms. This configuration explanation is based on FastGPT v4.17.0.
Constraints imposed by these characteristics on scheduled tasks and automation
Dispersed data sources and inconsistent update cycles require scheduled tasks to align with the actual update cycle of ad data, to avoid triggering processing tasks before data synchronization completes. When processing multiple types of documents together, field rules for different documents must be distinguished, and automated workflows must support input files of different formats. Many closely related data fields require field integrity checks during task execution, to prevent subsequent processing exceptions caused by missing fields. For the most time-consuming links such as keyword expansion, negative keyword filtering, and bid matching adjustment, automated workflows must prioritize high-priority tasks. Failures directly impact ad delivery effectiveness, so automated workflows must include timely failure alerts and retry mechanisms.
How to set configurations
| Config Item | Recommended Value | Rationale |
|---|---|---|
| Task trigger timing | Align with ad data update cycle (2 AM daily / 3 AM every Monday) | Match the data synchronization rhythm of the ad platform, avoid processing data sources that have not completed synchronization |
| Batch processing batch size | 50-100 items per run | Balance task execution efficiency and system resource usage, prevent single tasks from loading too much data and timing out |
| Task timeout threshold | 600 seconds | Fit the time range of routine ad data processing, prevent tasks from occupying execution resources for long periods |
| Number of failure retries | 2 times | Offset the impact of temporary network fluctuations or interface jitter, reduce unnecessary task failures |
| Alert trigger conditions | Task failure / duration exceeds twice the threshold | Notify operation and maintenance personnel of exceptions in a timely manner, avoid fault escalation affecting ad delivery |
| Log retention period | 30 days | Meet log query requirements for online troubleshooting, balance storage resource consumption |
Three common misconfigurations
- Phenomenon: When batch processing keyword lists, the first 10+ items execute normally, but subsequent tasks have no response or return empty results. Cause: Batch tasks are not split into small batches, and the number of keywords loaded by a single task exceeds the system's single-processing limit.
- Phenomenon: Scheduled tasks can only be executed with a minimum interval of 1 hour, and cannot be adjusted to a shorter cycle. Cause: The platform's preset default scheduling interval is used, and the configuration parameter for the task trigger cycle is not modified.
- Phenomenon: Keyword data associated with the knowledge base is not updated automatically, and manual synchronization must be triggered. Cause: Knowledge base automatic synchronization configuration is not enabled, and data update trigger rules are not bound.
How to confirm the configuration is properly set
- Manually trigger a configured scheduled task, check whether there are processing records for the corresponding data in the task execution log.
- Check the task scheduling list, confirm that the trigger interval matches the expected setting and aligns with the preset ad data update cycle.
- Simulate an exception scenario, such as disconnecting a temporary data source connection, verify that the alert notification triggers normally.
- Compare the results of manually processing a single piece of data with the results of automated batch processing, confirm field matching and data consistency.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.