Push.ai uses a caching strategy to optimize application performance, while reducing the load on your data sources.

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How it works?

Push.ai queries datasources through two methods:
  1. Calculations: metric and dimension queries using one of our calculations.
  2. Time-series: metric and dimension queries using a daily time grain.

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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.

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When are datasource queries triggered?

Push.ai queries datasources using two methods:
  1. In-app Exploration: Datasource queries are triggered when you interact with a metrics and dimension in the Push.ai UI.
  2. Scheduled Updates: Datasource queries are triggered on a schedule defined in both Reviews 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.

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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.