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Why performance is important in Looker Studio?
Performance is important in Looker Studio because it allows you to quickly access and analyze your data, leading to faster and more effective decisions.
Using data visualization makes information easier to understand. By optimizing the speed of your Looker Studio dashboard, you can save time on reporting and improve productivity.
Why is Looker Studio Slow?
Looker Studio can be slow for various reasons, often related to how it interacts with data. Key issues typically involve sourcing data, processing it, or handling complex datasets or slow hardware.
Additionally, Looker Studio has a six-minute limit on query handling. As a result, large amounts of data can slow down reports or even cause them to time out before completion.
Factors Impacting Performance:
Large datasets: Retrieving data from massive datasets takes longer to load and process compared to smaller datasets. The more data Looker Studio has to handle, the slower it will be.
API Pagination: API pagination divides large datasets into smaller, more manageable parts to improve load times and performance. Depending on the application's needs, several types of pagination can be used: offset pagination, cursor pagination, and page-based pagination. Despite these techniques, handling multiple pages can still slow down response times due to the large amount of data involved.
For more information: https://datastudio.jivrus.com/docs/articles/app-interface-concepts#h.jeg5ja9o81im
Page size: Page size impacts load times and performance.
Smaller page sizes can speed up individual page loads but may slow overall performance due to increased overhead from more requests.
Larger page sizes can slow down individual loads but reduce the number of requests, often improving efficiency.
Choosing the optimal page size depends on the specific needs of the application.
API Rate Limit: Looker Studio may have rate limits on API requests, restricting the number of requests that can be made within a certain time frame. Exceeding these limits can result in throttling or temporary blocks, impacting performance and response times.
For more information: https://datastudio.jivrus.com/docs/articles/app-interface-concepts#h.jeg5ja9o81im
Number of Objects / Multiple Data Sources: The number of objects of included charts, tables, or filters, in a Looker Studio report, can impact performance. Having a larger number of charts/data sources may increase load times and decrease overall performance, especially when rendering complex dashboards or reports.
Complexity of Reports: Reports that involve complex calculations, numerous filters, or intricate visualizations can require more processing power and time to render, leading to slower performance.
Network Latency: Slow network connections or high latency can impact the time it takes to retrieve data from external sources or to load dashboards and reports.
System Load: Heavy system load on Looker Studio servers or underlying infrastructure can affect performance, especially during peak usage times.
Browser Compatibility: Using older or incompatible web browsers may result in slower performance or rendering issues when accessing Looker Studio dashboards and reports.
How can we Improve Performance?
Restrict data size: Set restrictions on the data fetched and processed by queries. By limiting the data size, only essential information is retrieved, easing the workload on Looker Studio and boosting performance.
Date Range Control: Utilize date range controls to specify the timeframe for data analysis. By focusing on specific date ranges, you can narrow down the scope of queries and reduce the amount of data processed, leading to faster performance.
For further information, you can refer to this link: Date Range Controls
Reduce Blends: Minimize the number of data blends used in reports. While data blending can be useful for integrating information from multiple sources, excessive blending can increase query complexity and processing time. Limiting the number of blends simplifies queries and improves performance.
Caching and Data Extraction:
Caching:
Utilize caching mechanisms to store and retrieve frequently accessed data. By caching query results, Looker Studio can quickly deliver data without needing to reprocess queries, resulting in faster response times for users.
Our caching system is implemented internally within our code to improve performance for certain applications. If caching is supported, the data will be cached for 30 minutes. After this period, the cache will be refreshed.
Extract Data:
To make Looker Studio dashboards more responsive, use Looker's Extract Data connector from Google, which is free of cost. It allows users to explore data subsets by caching them from connectors. This speeds up report loading times and enhances the exploration of data subsets.
Leveraging both caching mechanisms and the Extract Data connector can significantly improve the performance and responsiveness of your Looker Studio dashboards, ensuring faster data delivery.
For further information on Extract Data, refer to this link: Extract Data
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