Caching
Understand how caching and datasource queries work in Push.ai.
Push.ai uses a caching strategy to optimize application performance, while reducing the load on your data sources.
How it works?
Push.ai queries datasources through two methods:
- Calculations: metric and dimension queries using one of our calculations.
- Time-series: metric and dimension queries using a daily time grain.
When is caching used?
Caching is used when querying both calculations and timeseries data. Push.ai implements a caching strategy for each unique metric-dimension combination. For each combination, we maintain a daily cache, meaning that we will query that specific combination from the data source at most once per day.
When are datasource queries triggered?
Push.ai queries datasources using two methods:
- In-app Exploration: Datasource queries are triggered when you interact with a metrics and dimension in the Push.ai UI.
- Scheduled Updates: Datasource queries are triggered on a schedule defined in both Reports and Subscriptions. With Subscriptions, datasource queries are triggered for each Business Object a user is subscribed to, in addition to any related Business Objects. Related objects include any related metrics and are limited to AI-enabled dimensions only.
If multiple users are subscribed to the same Business Object, the datasource query will be triggered once for each object, rather than once for each user.
When is datasource compute not used?
Datasource compute is not used in any of Push.ai’s advanced analytics or AI applications. This includes time-series modeling, such as outlier detection and forecasting. In addition, Push.ai’s AI systems combine the existing calculations with large language models, and do not use additional datasource compute.