Kevinleary.net: Optimizing Slow Looker Studio Reports

Slow Looker Studio dashboards kill productivity and frustrate users.

This guide covers Google’s official recommendations for optimizing Looker Studio performance, broken down into actionable strategies you can implement immediately.

Optimize Data Sources

Data source configuration has the greatest impact on overall performance.

Custom Structures in Data Warehouses for Large Datasets

Use optimized data sources specifically designed for reporting, and consider leveraging data warehouses like Google BigQuery or Snowflake for large datasets. This allows Looker Studio to access your data quickly and efficiently from a centralized location. Reducing the number of data sources in your reports streamlines performance significantly. Instead of connecting to multiple scattered sources, consolidate your data into a single, well-structured table before uploading to Looker Studio.

Pre-Aggregate Data at Source Level

Pre-aggregate your data at the source level rather than pulling detailed records. Grouping and aggregating data beforehand reduces data size and speeds up loading times. This approach also reduces query costs and processing overhead.

Pre-aggregated data in BigQuery for faster Looker Studio reports — Pre-aggregated tables are generated in BigQuery with scheduled queries. This saves grouped data used by reports in a much more compact dataset.

Extract Data

Looker Studio’s Extract Data feature creates a static snapshot of up to 100 MB of data that improves report performance. Data requests go to this snapshot rather than the underlying dataset, making reports load faster.

When to Use Extract Data

Extract Data works best for frequently accessed reports that don’t require real-time data updates. For already aggregated datasets like Google Ads or Analytics, extracting creates a disaggregated dataset that offers more flexibility for custom aggregations than standard data sources.

How to Implement Extract Data

To implement extracted data sources, navigate to Create > Data Source > Extract Data in Looker Studio, select your source, choose relevant dimensions and metrics, and set up automatic refresh schedules. Extracted data sources contain static information, so configure auto-update schedules for your use case.

Extract Data Data Source in Looker Studio: Data extract creates a snapshot of your data. This can make your reports load faster and be more responsive when applying filters and date ranges than when working with a live connection to your data. — Data extract creates a snapshot of your data. This can make your reports load faster and be more responsive when applying filters and date ranges than when working with a live connection to your data.

Data Freshness

Data freshness settings let you balance up-to-date information against report performance and potential query costs. Looker Studio temporarily stores queries and results in memory, serving cached data when the freshness threshold still applies.

How Data Freshness Works

When Looker Studio encounters a previously seen query within the data freshness threshold, it serves data from memory instead of fetching from the data source. This speeds up report performance and reduces query costs.

Setting Optimal Refresh Intervals

Adjust data freshness based on your needs. For ad performance monitoring, daily updates might work. Social media analytics may require multiple daily refreshes. Configure longer refresh intervals for static data and shorter intervals only when real-time data is essential.

Optimizing Slow Looker Studio Reports — Frequent updates provide fresher data, but also cause slow performance and increase query costs for paid data services.

BigQuery BI Engine

BigQuery BI Engine is a fast, in-memory analysis service that accelerates SQL queries by intelligently caching frequently used data. BI Engine can accelerate SQL queries from any source, including data visualization tools, and manages cached tables for ongoing optimization without manual tuning.

How BI Engine Works

BI Engine uses vectorized processing to operate on batches of data efficiently, making better use of modern CPU architecture. It employs advanced data encodings, specifically dictionary run-length encoding, to compress data stored in the in-memory layer. BI Engine only caches the queried, required parts of columns and partitions rather than entire tables.

Google BigQuery BI Engine architecture — BigQuery BI Engine architecture diagram showing business intelligence tools connecting via SQL to BigQuery API, with BI Engine’s in-memory processing layer (metadata, vectorized runtime, reservations) accelerating queries between BigQuery’s storage and compute resources.

Short Query Optimized Mode

The BigQuery connector supports short query optimized mode, where BigQuery prioritizes returning immediate results instead of creating a BigQuery job when it determines queries can finish quickly. This optimization applies automatically to qualifying queries.

Use Cases for BI Engine

BI Engine works best in these scenarios:

BI tools and dashboards: Accelerates queries from Looker Studio, Tableau, or any tool using BigQuery API, REST, JDBC, or ODBC connections
Frequently queried tables: You can designate preferred tables for acceleration, helpful for high-visibility dashboards
Pre-joined or pre-aggregated data: Works best with fewer joins, particularly when joining large fact tables with smaller dimension tables

BI Engine Limitations

BI Engine acceleration is not available for:

Wildcard table queries
External tables including BigLake tables
JavaScript UDFs
Queries returning more than 1 GiB of data (under 1 MiB recommended for latency-sensitive applications)
Row-level security
JSON data types

Join Acceleration Requirements

BI Engine accelerates join queries with specific requirements:

INNER and LEFT OUTER JOINS only
Large fact table joined with up to 4 smaller dimension tables
Dimension tables must have less than 5 million rows
Unpartitioned dimension tables: 5 GiB or less
Partitioned dimension tables: Referenced partitions 1 GB or less

Preferred Tables Configuration

Use preferred tables to limit BI Engine acceleration to specific tables while other queries use regular BigQuery slots. This ensures acceleration for business-critical dashboards. If there’s insufficient RAM for all preferred tables, BI Engine offloads recently unused partitions and columns to free memory.

BI Engine Setup Requirements

To leverage BI Engine:

Store data in BigQuery
Have a billing project with BigQuery API enabled
Create a BI Engine reservation in your region
Optionally designate preferred tables for guaranteed acceleration
Consider partitioning tables by time if dashboards only show recent data

Optimization Tips

Combine BI Engine with materialized views for best performance. Materialized views can join and flatten data to optimize structure for BI Engine, avoiding repeated joins for each query. Use clustering and partitioning on large tables to further optimize BI Engine performance.

Reduce Complexity & Chart Count

Browser performance can be measured roughly as (amount of data per cell) × (number of rows) × (number of columns). For optimal browser performance, Google recommends 50 or fewer columns.

Limit Charts Per Page

Multiple charts and widgets result in more queries upon loading, which can slow down report performance. Having fewer charts or separating them across multiple pages improves organization and loading speed.

Avoid Query Overload

Limit visualizations per page and consider creating navigation between dashboards instead of cramming everything into one view. Avoid dashboards with 25 or more queries, and try concatenating similar measures into single value visualizations to reduce the total number of tiles.

Calculated Fields & Data Blends

Looker Studio can handle many operations including filters, charts, dimensions, and calculated fields. However, excessive custom calculations, filters, and data blends can significantly lower performance.

Why Calculations Slow Performance

Excessive calculated fields and blending multiple datasets within Looker can significantly slow down reports. This is why it’s crucial to join, aggregate, and perform necessary calculations beforehand using key columns to create a single master table.

Clean Up Existing Reports

Remove unnecessary calculated fields from your data sources, optimize data blends in your reports, and delete outdated filters. Use the Resource tab in Looker Studio to locate and manage these elements. When possible, perform calculations in your data source or data warehouse rather than in Looker Studio.

Default Filters & Date Ranges

Set default filters when creating views by selecting shorter date ranges like “Last week” or “Last month.” Broader ranges like “Last quarter” or “Last year” require Looker Studio to load and process significantly more data.

Use Required Filters

Use required filters to prevent users from running dashboards without necessary filters applied. This ensures that reports always load with appropriate data scope limits.

Configure Smart Defaults

Configure defaults that provide useful insights while limiting initial data loads. Users can always expand date ranges or remove filters as needed, but starting with focused data sets improves initial loading performance.

Optimized date ranges and filters — Use the shortest date range possible, and pre-select default filters if/when it makes sense. The less data you load on the initial view the fast it will be.

Native Google Connectors

Rather than choosing any third-party connector, select those developed by Google for optimal performance and reliability. Native Google connectors are optimized for Looker Studio and typically offer better performance, more features, and greater stability than third-party alternatives.

Benefits of Native Connectors

Google’s native connectors for services like Google Analytics, Google Ads, Google Sheets, and BigQuery include built-in optimizations and caching mechanisms that third-party connectors may lack. When you must use third-party connectors, research their performance characteristics and user reviews before implementation.

Monitor & Test

Test dashboard performance after adding elements. As you build, continue navigating to the dashboard and refreshing the page to determine how performance is impacted as you add additional components.

Establish Performance Baselines

Monitor loading times, query execution duration, and user feedback about report responsiveness. Use Looker Studio’s built-in performance indicators and consider implementing usage analytics to track report performance over time.

Set Up Performance Alerts

Document performance baselines and set up alerts for performance degradation. This helps maintain optimal user experience as your reports evolve and data volumes grow.

Performance Bottlenecks

Looker Studio performance depends on four key elements: database load, instance load, browser load, and network latency. Database queries take time to process, especially large queries or when multiple queries run simultaneously.

Identify Bottleneck Sources

Report loading speed depends on underlying dataset performance, data volume being queried, query complexity, and network latency. Some factors are beyond your control, but data source optimization and report configuration can dramatically improve performance.

Focus Optimization Efforts

Understanding these bottlenecks helps you identify where to focus optimization efforts. Database-related slowdowns require query optimization or infrastructure improvements, while browser-related issues need data volume reduction or visualization simplification.

Implementation Summary

Start with data source consolidation and pre-aggregation, as these changes provide the biggest impact. Next, implement extract data sources for frequently accessed reports and configure appropriate data freshness settings.

Prioritize High-Impact Changes

For BigQuery users, enabling BI Engine acceleration delivers large performance improvements with minimal configuration effort. Finally, optimize individual reports by reducing complexity, minimizing calculated fields, and setting smart defaults.

Balance Performance Trade-offs

Performance optimization involves tradeoffs between speed, data freshness, and user customization options. These recommendations may not be appropriate for every use case, so evaluate each optimization against your specific requirements.

Maintain Long-term Performance

Monitor and test regularly to ensure that performance optimizations continue delivering value as your data and reporting needs evolve. Following these Google-recommended practices transforms slow, frustrating Looker Studio reports into fast, responsive analytical tools.