Performance Optimization Flashcards
What are the key factors affecting query performance in Snowflake?
Key factors include query complexity, data distribution, virtual warehouse size, and data partitioning.
Example sentence: Data distribution plays a crucial role in query performance as uneven distribution can lead to slower query execution.
How do you optimize query performance in Snowflake?
Optimize query performance by using proper indexing, clustering keys, query caching, and optimizing SQL queries.
SQL query optimization involves rewriting queries to be more efficient by reducing unnecessary joins and filters.
What is the role of clustering keys in Snowflake?
Clustering keys in Snowflake improve query performance by organizing data in a way that enhances data locality for specific queries.
Clustering keys help reduce the amount of data scanned by grouping related data together.
How does Snowflake handle query caching?
Snowflake handles query caching by storing the results of previously executed queries to speed up subsequent queries.
What are the best practices for using virtual warehouses?
Best practices include sizing warehouses appropriately, using auto-suspend and auto-resume, and scaling warehouses based on workload.
How do you monitor query performance in Snowflake?
Query performance can be monitored using the QUERY_HISTORY view, performance dashboards, and monitoring tools.
What is the use of the QUERY_HISTORY view?
The QUERY_HISTORY view provides detailed information about executed queries, including execution time, resources used, and query text.
How do you handle large datasets in Snowflake?
Handle large datasets by using clustering keys, partitioning, and optimizing storage formats to improve query performance.
Describe the use of materialized views for performance optimization.
Materialized views store the results of complex queries, enabling faster query performance for subsequent queries on the same data.
How do you manage compute resources in Snowflake?
Manage compute resources by appropriately sizing virtual warehouses, using auto-suspend and auto-resume, and monitoring resource usage.
What is the role of auto-suspend and auto-resume?
Auto-suspend and auto-resume help manage compute costs by automatically suspending idle warehouses and resuming them when needed.
How do you use the EXPLAIN plan in Snowflake?
The EXPLAIN plan provides insights into how Snowflake will execute a query, helping identify performance bottlenecks and optimize queries.
What are the best practices for designing efficient schemas?
Best practices include normalizing data, using appropriate data types, indexing, and partitioning to optimize query performance.
How does partitioning improve performance in Snowflake?
Partitioning improves performance by dividing large tables into smaller, more manageable pieces, enhancing data access and query speed.
What is the purpose of result caching?
Result caching stores the results of queries, enabling faster retrieval for identical queries run in the future.
How do you balance cost and performance in Snowflake?
Balance cost and performance by appropriately sizing virtual warehouses, using auto-suspend, and optimizing queries to reduce resource usage.
What are the benefits of using micro-partitions?
Micro-partitions improve performance by organizing data into small, manageable blocks, enhancing query performance and reducing storage costs.
How does Snowflake’s architecture support performance optimization?
Snowflake’s architecture separates compute and storage, allowing independent scaling and optimizing performance based on workload needs.
Describe a scenario where performance tuning improved query speed.
A scenario could involve adding clustering keys to a table to improve query performance by reducing scan times for frequently queried columns.
What are the tools available for performance monitoring in Snowflake?
Tools include the QUERY_HISTORY view, performance dashboards, third-party monitoring tools, and built-in Snowflake monitoring features.
