What This Category’s Data Looks Like
Ad marketing yield rate and market trend daily report data primarily comes from ad placement management systems, media integration APIs, and financial reconciliation modules. Data is updated on a fixed daily schedule, generating a complete dataset for the previous day. The full update cycle is 24 hours. Documents use structured JSON or CSV formats. Each record corresponds to the daily delivery performance and revenue details of a single ad campaign, including fields such as 广告计划ID, 投放渠道, 当日曝光量, 当日点击量, 当日消耗总额, 当日转化订单数, 当日成交总金额, and others. Numeric fields use units of impressions, yuan, and individual units respectively.
Constraints for Deployment and Upgrade
Daily batch generation of fixed-field daily report data for ad marketing requires configuring scheduled synchronization tasks that match the data update cycle. This avoids premature or late triggers that cause missing data or conflicts with business peak hours. Structured data with multiple fields requires precise mapping to vector database index fields, ensuring enumeration fields (such as 投放渠道) can be correctly identified and embedded. Since data is updated in T+1 batches, upgrade operations must be performed during non-synchronization periods. Upgrade steps must also be compatible with legacy data formats to avoid interrupting existing broadcast services. In scenarios with multiple data source integrations, deployments must support multi-channel configuration, and upgrades must verify that pull logic for each data source functions correctly.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_DATA_BATCH_SIZE | 500-1000 records/batch | Adapts to the thousand to ten thousand scale of single daily report data in ad marketing, avoids excessive memory usage from single parsing operations |
DATA_SYNC_CRON | 0 3 * * * | Matches the fixed T+1 generation schedule of ad data, avoids daily business peak hours |
VECTOR_STORE_MAX_DIMENSION | 1024 | Adapts to the output dimensions of mainstream open-source embedding models, ensures normal creation of vector indexes for ad data fields |
RECALL_TOP_K | Top 10-15 entries | Balances the amount of information and conciseness of broadcast content, avoids excessive redundant data or missed key ad campaigns |
UPLOAD_FILE_TIMEOUT_SECONDS | 1800 seconds | Meets the import processing duration requirements for ten-thousand-scale batch data, prevents task interruptions from mid-run timeouts |
REDIS_IMAGE_SOURCE | Alibaba Cloud Container Image Service redis official image | Resolves slow or failed pulls of official images in domestic network environments |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Running
docker-compose up -dresults inpull access denied for redisor pull timeout logs. The cause is failure to specify a domestic image source. Official images cannot be pulled normally in domestic network environments. - After upgrading the PgVector plugin, existing vector indexes cannot be queried normally. The interface displays the
vector dimension mismatcherror. The cause is failure to adjust theVECTOR_STORE_MAX_DIMENSIONconfiguration before upgrading. The default dimension of the new plugin version does not match the existing index dimension. - The ad placement channel field is empty or displays
unknownin generated yield rate broadcast results. The cause is failure to configure enumeration mapping for the投放渠道field in data import configurations, leading to failure to recognize non-predefined channel values.
How to Verify Proper Configuration
- Manually trigger a scheduled synchronization task, check data import logs, and confirm all ad campaign data is successfully parsed and imported into the vector database.
- Call the vector retrieval interface, pass a query term related to ad placement, and verify that the number of returned results matches the value range specified in the
RECALL_TOP_Kconfiguration. - Execute the Redis service status check command, confirm the service is running normally and can properly store intermediate data for synchronization tasks.
- Verify that the PgVector plugin version is compatible with the current deployment environment, and confirm that vector index creation parameters match the configuration items.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.