TRINO Series

• PRESTO / Trino Basics
• Trino Series: Building a Sample Project on Local Installation
• Trino Series: Optimizing and Expanding the Sample Project
• Trino Series: Advanced Integrations with Cloud Storage
• Trino Series: Caching Strategies and Query Performance Tuning

Introduction: Enhancing Trino Performance

In our journey with Trino, we’ve explored its setup, integrated it with multiple data sources, added real-time data, and expanded to cloud storage. To wrap up, we’ll focus on strategies to improve query performance. Specifically, we’ll implement caching techniques and apply performance tuning to optimize queries for frequent data access. This final post aims to equip you with tools for building a highly responsive and efficient Trino-powered analytics environment.

Goals for This Post

1. Implement Caching for Frequent Queries: Set up a local cache for repeated queries to reduce data retrieval times and resource consumption.
2. Tune Query Performance: Explore query optimizations, including resource management and query rewriting, for faster processing.
3. Optimize Resource Allocation: Configure Trino’s memory and CPU settings for optimal utilization in a high-demand environment.

Step 1: Setting Up Caching for Frequent Queries

1.1 Configure a Local Cache

Trino’s caching feature allows frequently accessed data to be stored temporarily, reducing repeated calls to external data sources. This is particularly beneficial when working with large datasets or slow data sources like S3.

1. Add the Local Cache Plugin: Begin by configuring Trino to use a caching plugin. In etc/config.properties, enable local caching:
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   cache.enabled=true
cache.location=/path/to/cache
cache.max-size=1GB
cache.expiration-time=10m

   • cache.location: Specifies the directory where cached files are stored.
   • cache.max-size: Defines the maximum cache size.
   • cache.expiration-time: Sets the duration cached data remains before it’s refreshed.
2. Specify Cacheable Tables: For specific tables that are frequently queried, configure Trino to cache the results temporarily. For instance, if queries often access a product table stored in S3, caching will improve response times:
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   CREATE TABLE cached_products
WITH (
format = 'ORC',
external_location = 's3a://your_bucket_name/product_data.orc'
);


   After creating the cached table, frequently accessed data will be stored locally, reducing dependency on external data retrieval.

Step 2: Tuning Query Performance with Optimization Techniques

2.1 Using Predicate Pushdown

Predicate pushdown pushes filter conditions down to the data source, minimizing the amount of data fetched and processed. This feature is especially useful for querying large datasets in cloud storage.

Enable predicate pushdown in config.properties:

Example Query with Predicate Pushdown:

With pushdown enabled, only records with order_date >= DATE '2023-01-01' are fetched from the data source, speeding up query execution.

2.2 Leveraging Data Partitioning

Partitioning tables by fields like order_date or region allows Trino to read only the necessary partitions, further enhancing performance. For example:

This setup minimizes data scanning for date-based queries.

Step 3: Configuring Resource Allocation for High Demand

Optimizing Trino’s resource allocation ensures your queries run smoothly under heavy workloads. We’ll focus on memory and CPU settings that balance performance and resource usage.

1. Memory Settings: In jvm.config, allocate sufficient memory to handle large datasets:
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   -Xmx16G
-Xms8G
-XX:+UseG1GC

   • -Xmx: Maximum memory allocation.
   • -Xms: Initial memory allocation.
2. CPU Configuration: Trino allows you to limit CPU usage for each query. Configure cpu.cores-per-node in config.properties to manage parallelism:
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   cpu.cores-per-node=4

3. Session-Specific Settings: For specific sessions that require additional memory, use session settings to allocate memory directly in your queries:
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   SET SESSION query_max_memory = '4GB';


Conclusion: Building an Optimized Trino Environment

This final post has introduced performance-enhancing strategies to make Trino faster, more efficient, and scalable. With caching, predicate pushdown, partitioning, and resource tuning, you now have a highly optimized setup capable of handling complex and frequent analytics queries with ease.

By following this series, you’ve gained hands-on experience with Trino, from setting up a local project to integrating real-time and cloud data, and now to fine-tuning performance. The knowledge here not only applies to Trino but also serves as a foundation for understanding best practices in distributed SQL engines.

Thank you for following along, and I hope this series has been a valuable resource for exploring Trino’s potential in modern data analytics!