HTTP Interfaces and External Systems for Water Treatment

What the data for this category looks like

Data for this category is sourced from SCADA collection systems at water treatment sites, water utility operation management platforms, and third-party environmental monitoring APIs. Data updates follow a natural daily cycle, with full operational data from the previous day compiled and generated each early morning. Each individual data entry includes fields such as site identifier, treatment scale, influent water quality parameters, chemical dosage, unit treatment cost, unit effluent yield, and others. All fields are bound to clear physical units, such as cubic meters, yuan per cubic meter, milligrams per liter, etc. The data structure is grouped by site, with no nested redundant layers.

Constraints Imposed on HTTP Interfaces and External Systems

The data characteristics of this category create multiple constraints for HTTP interfaces and external systems. Multi-source data sources require interfaces to support parallel pulls from multiple addresses. Configure independent authentication parameters for each data source. The fixed daily update cycle requires interfaces to use scheduled trigger logic. Set a reasonable polling interval to avoid repeatedly pulling processed historical data. Fields with clear physical units require interfaces to return unit fields alongside data, or enforce unit mapping in interface specifications. This prevents external systems from losing unit information during parsing. The site-grouped data structure requires interfaces to support filtering queries by site ID. Configure pagination parameters to adapt to batch pulling scenarios for multiple sites. This prevents excessive single-return data volumes from triggering timeout limits.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`api_request_timeout`	`60 seconds`	Water treatment single-site operational data volume is small. A 60-second timeout covers most scenarios and prevents request timeout interruptions.
`schedule_cron`	`0 2 * * *`	Data updates follow a natural daily cycle. Pulling data at 2 AM ensures full previous day operational data is obtained, adapting to daily report generation requirements.
`auth_type`	`API_KEY`	Most water treatment data sources are internal enterprise operation and maintenance systems. API_KEY authentication is simple to configure and supports independent authentication for multiple data sources.
`batch_pull_size`	`20 items per request`	Balances single request data volume and pulling efficiency. Prevents excessive single pulls from overloading the interface.
`unit_mapping_strategy`	`Retain original units forcibly`	All data fields for this category are bound to clear physical units. Unit information must be retained for external system parsing.
`max_return_items`	`100 items`	Adapts to the scale of most enterprise water treatment sites. Prevents excessive single-return data volumes from triggering external system restrictions.

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 individual analysis. It is recommended to conduct tests on local samples before finalizing settings.

Three Common Configuration Mistakes

Setting batch_pull_size to 6 items results in the external system only retrieving a fixed 6 items of data, with no way to adjust. Cause: The globally effective batch pull parameter was not configured correctly. Only a temporary parameter was set in a single interface call, and the global configuration was not updated synchronously.
SQL statements generated by interface calls cannot be parsed by databases due to built-in punctuation. Cause: The sql_sanitize parameter was not enabled. Field values returned by the interface were not filtered for special punctuation, leading to syntax errors when splicing SQL.
Calls to external interfaces return the error lookup spark-api.xf-yun.com i/o timeout. Cause: The api_request_timeout configuration value is less than the actual data pull time, or network link fluctuations occurred without a retry mechanism configured, causing the request to time out before completion.

How to Confirm Configuration Is Complete

Invoke the test interface to verify that returned data fields and units match preset data source fields, and confirm the unit mapping configuration is effective.
Review scheduled task logs to confirm that daily early morning pull tasks trigger normally, with no authentication failure or timeout errors.
Adjust the batch pull parameter to verify that the number of returned sites matches the configured requirements, and confirm the batch pull logic is working correctly.
Simulate a multi-data-source pull scenario to verify that authentication parameters for different data sources take effect independently, with no cross-data-source authentication exceptions.

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.