Optimizing Looker Performance: Tips for Analysts

For Every Business, Looker has become an essential tool for organizations seeking to extract actionable insights from their data. Its robust visualization and data modeling capabilities make it a favorite among analysts. However, as datasets grow larger and queries become more complex, performance can become a bottleneck. Slow dashboards and queries can frustrate users and hinder timely decision-making. In this post, we’ll explore practical tips and best practices for analysts to optimize Looker performance and deliver faster, more reliable insights.

1. Understand the Basics of Looker’s Architecture

Before diving into optimization techniques, it’s crucial to understand how Looker processes data:

• Database-First Approach: Looker doesn’t store data. Instead, it sends queries directly to your connected database and retrieves results in real-time.
• LookML Models: LookML acts as a blueprint for queries, defining relationships, dimensions, measures, and logic.
• Browser Rendering: Once data is retrieved, Looker Performance processes and renders visualizations in the browser.

Understanding this flow helps identify where bottlenecks occur — whether in the database, LookML logic, or the visualization layer. (Ref: Implementing Unit Testing for LookML Models)

2. Optimize Your Database Queries

Since Looker Performance relies on the database for query execution, optimizing the underlying queries is critical.

a. Indexing and Partitioning

• Ensure that frequently queried columns are indexed.
• Use partitioning strategies for large datasets to reduce the scan size.

b. Limit Data Scans

• Use LIMIT clauses in exploratory queries.
• Avoid querying unnecessary columns by explicitly selecting only the required fields.

c. Analyze Execution Plans

• Use database tools to analyze query execution plans and identify inefficiencies such as full table scans or unnecessary joins.

d. Pre-Aggregate Data

• Create summary tables for commonly used aggregations. This can significantly speed up queries that involve large datasets.

3. Streamline LookML Models

Efficient LookML design is essential for performance. Here are some tips:

a. Avoid Excessive Joins

• Limit the number of joins in your Explores. Too many joins can slow down queries and lead to higher resource consumption.
• Use derived tables or aggregate tables to pre-join data when necessary.

b. Minimize N+1 Queries

• N+1 queries occur when a single query spawns multiple subsequent queries, slowing performance. Use the ref: parameter to reuse dimensions and measures instead of creating new ones.

c. Use Persistent Derived Tables (PDTs)

• Instead of recalculating derived tables for every query, use PDTs to store the results of expensive calculations in your database. Ensure PDTs are refreshed at optimal intervals to balance performance and data freshness.

d. Avoid Overly Complex Dimensions and Measures

• Simplify SQL logic within dimensions and measures. Offload heavy computations to the database or pre-aggregated tables when possible.

4. Optimize Dashboards and Visualizations

Dashboards are the end product for many Looker Performance users. Here’s how to keep them fast:

a. Reduce the Number of Tiles

• Avoid overcrowding dashboards with too many visualizations. Each tile generates a query, which can slow down performance.
• Combine related visualizations into a single tile where appropriate.

b. Limit Data Returned by Tiles

• Use filters to narrow the scope of data returned in each visualization.
• Set row limits for tables to prevent excessive data from being retrieved and rendered.

c. Leverage Caching

• Enable Looker Performance query caching to store results for frequently used queries.
• Be mindful of cache expiration settings to ensure data remains relevant.

d. Optimize Visualization Types

• Avoid overly complex visualizations that require significant browser rendering resources.
• Use simpler chart types or aggregated data when possible.

e. Preload Critical Dashboards

• Use Looker Performance scheduled delivery feature to run and cache dashboards during off-peak hours, ensuring faster performance during peak usage.

5. Leverage User Filters Effectively

Filters allow users to customize their views without overwhelming dashboards with unnecessary data.

a. Use Required Filters

• Set required filters on Explores or dashboards to ensure users always narrow down large datasets before querying.

b. Dynamic Filters

• Use Looker’s user_attribute parameters to create filters tailored to individual users or groups. For example, restrict data by region for specific teams.

c. Filter Optimization

• Avoid redundant or conflicting filters that can confuse users or generate inefficient queries.

6. Monitor and Troubleshoot Performance

Regular monitoring helps identify bottlenecks and areas for improvement.

a. Use the Performance Tile

• Looker performance tile provides insights into query execution times, cache usage, and load times for dashboards.

b. Analyze Query Statistics

• Use the SQL Runner tool to preview and debug SQL queries generated by Looker Performance.
• Analyze query logs to identify slow queries and optimize them.

c. Database Monitoring Tools

• Use database-specific monitoring tools to track query execution and resource usage.
• Identify and resolve database-level bottlenecks such as contention or high memory usage.

d. User Feedback

• Solicit feedback from users to identify slow-performing dashboards or Explores and prioritize optimization efforts accordingly.

7. Adopt Best Practices for Collaboration

Efficient collaboration ensures that teams build high-performance data models without redundancy.

a. Centralize LookML Development

• Maintain a centralized LookML repository to avoid duplication and inconsistencies across models.

b. Implement Code Reviews

• Conduct peer reviews of LookML changes to catch inefficiencies early.

c. Standardize Naming Conventions

• Use consistent naming conventions for dimensions, measures, and views to enhance clarity and maintainability.

d. Document Your Models

• Include clear documentation within LookML to help other analysts understand complex logic and dependencies.

8. Plan for Scalability

As datasets grow and user adoption increases, plan ahead to ensure scalability:

a. Data Archiving

• Archive historical data that isn’t frequently accessed to reduce dataset size and improve query performance.

b. Sharding Large Tables

• Split large tables into smaller, more manageable ones based on logical divisions such as time or region.

c. Leverage Data Warehousing Features

• Use modern data warehouse features like materialized views, clustering, and BigQuery’s BI Engine for faster query performance.

d. Implement Usage Monitoring

• Monitor user activity and dashboard usage patterns to identify underutilized resources and optimize them.

Final Thoughts

Optimizing Looker performance requires a combination of database tuning, efficient LookML modeling, and thoughtful dashboard design. By following these tips, analysts can ensure that Looker Performance delivers fast, reliable, and actionable insights. Regular monitoring, user feedback, and collaboration will further enhance performance, making Looker a powerful tool for data-driven decision-making.

Performance optimization isn’t a one-time effort — it’s an ongoing process that evolves with your data and user needs. Start implementing these best practices today and transform your Looker Performance experience into one that’s both efficient and impactful.

Reference