The speed with which a Looker Studio report loads and responds to viewer changes, such as applying filters or changing the date range depends on a number of factors, including:
- the performance of the underlying data set
- the amount of data being queried by the visualizations in the report
- the complexity of those queries
- network latency
Some of these factors are beyond your (or Looker Studio's) ability to control. For example, there may not be much you can do to improve the responsiveness of the underlying data platform or speed up your network connection. There are, however, some things you can do to fine tune your report performance in Looker Studio.
Adjust the data freshness rate
Looker Studio already uses some performance tuning features internally (and we are always working to make these better). For example, Looker Studio improves report performance by fetching the data from a temporary storage system called the cache. Fetching cached data can be much faster than fetching it directly from the underlying data set. Fetching cached data also minimizes costs for paid services, such as BigQuery, by reducing the number of queries that need to be served directly from the data set.
The frequency with which data in the cache is updated is called the data refresh rate. The actual refresh rates vary by connector, but if possible, consider making the refresh interval longer. This can help your report performance by using the cache to answer repetitive queries, the tradeoff being possibly not having the most up to date information.
Learn more about managing data freshness.
Use an extracted data source
By default, data sources maintain a live connection to your underlying data set. When the cache (described above) expires, or if you execute a new query that can't be served from the cache, Looker Studio goes to your data set to get the data. You can avoid these potentially slow data fetches by extracting up to 100MB of data from any existing data source into an extracted data source.
Choose the specific fields you need, apply filters, and add a date range to create a snapshot of your data. This can make your reports and explorations load faster and be more responsive than when working with a live connection to your data. The tradeoff here is that the extracted data source is static: your data won't change in the report until the data source itself is refreshed. This may only be a minor inconvenience though, as you can schedule an extracted data source to update automatically.
Learn more about extracting data.
Accelerate BigQuery data sources with BI Engine
BigQuery BI Engine is a fast, in-memory analysis service. By using BI Engine you can analyze data stored in BigQuery with sub-second query response time and with high concurrency.
BI Engine integrates with Looker Studio to accelerate data exploration and analysis. With BI Engine, you can build rich, interactive dashboards and reports in Looker Studio without compromising performance, scale, security, or data freshness.