Supercharging Data Analytics: Unveiling the Power of Snowflake Query Acceleration Service (QAS)
With a history of collaboration alongside accomplished data analytics leaders on numerous mission-critical projects at Tableau and now at Snowflake, the relentless quest for optimal performance and efficiency has been an integral part of my journey.
Organizations, both large and small, are continually searching for innovative ways to fine-tune their data platform solutions, aiming to deliver lightning-fast query execution times and a seamless user experience. Addressing this ever-present challenge, Snowflake introduced the Snowflake Query Acceleration Service (QAS) about 6 months ago, a revolutionary feature that has the potential to reshape the landscape of query processing. In this comprehensive exploration, we will delve deep into the intricacies of the Snowflake Query Acceleration Service, uncover its multifaceted benefits, and understand the cost implications surrounding its implementation
This service empowers data warehouses to go beyond their standard capabilities by turbocharging specific segments of the query workload. How does it work? By temporarily infusing additional capacity into the warehouse, the service dramatically expedites query execution times, leading to remarkable performance enhancements across the board.
When to Use Query Acceleration Service (QAS)
- Ad Hoc Analytics: Delving into data with queries that involve scanning massive volumes of information can strain even the most robust systems. These analytical journeys are characterized by unpredictable query patterns that demand significant computing power. Here’s where the Query Acceleration Service truly shines, employing parallel processing to tackle complex tasks efficiently especially for users of self-service Business Intelligence tools, such as Tableau and Power BI.
- Unpredictable Data Volume: Workloads with varying data volumes per query can be seamlessly managed with the Snowflake Query Acceleration Service. Its ability to allocate resources on the fly caters to the dynamic nature of these queries, ensuring peak performance without compromise.
- Mixed Workloads: The dynamic nature of the data analytics landscape often translates to a mix of lightweight and resource-intensive queries. The Query Acceleration Service’s prowess lies in its capacity to optimize performance across this spectrum, delivering consistent and accelerated results, regardless of the query’s demands.
- Large Scans with Filters: Queries that encompass substantial data scans coupled with selective filters are the prime candidates for acceleration with the Query Acceleration Service. This service employs advanced parallelization and optimization techniques to streamline resource-intensive tasks, resulting in significant reductions in scanning and filtering times.
Top Query Acceleration Service Benefits
Enhanced Performance and Conquered Queues
The most apparent benefit of the Snowflake Query Acceleration Service is its transformative impact on performance. By temporarily allocating additional capacity, the service ensures that even resource-intensive queries are executed with remarkable speed. This reduction in query processing times translates to an enhanced user experience, allowing for quicker access to crucial data-driven insights.
Redefining Cost Efficiency and Management
The Snowflake Query Acceleration Service isn’t just about performance; it’s also a potent tool for cost optimization. Organizations can consider resizing their warehouses or adjusting runtime durations without compromising on performance. Thanks to the service’s serverless architecture, manual management of additional capacity becomes obsolete, simplifying warehouse operations and management, and best of all, much less capacity planning effort needed!
Query Acceleration Service Cost Considerations
- Costs are only incurred when the Query Acceleration Service accelerates queries. No eligible queries mean no costs.
- Query Acceleration Service costs are billed based on seconds (NO 1 minute minimum runtime), ensuring accurate and fair charges without any minimum runtime restrictions
- Query Acceleration Service costs mirror the pricing structure of equivalent warehouse capacity, devoid of any additional markups
- Customer overall bills stay approximately the same after enabling Query Acceleration Service, but performance boost on eligible queries are significant to make any data analyst happy
Validating Impact and Eligibility with QAS
To identify queries ripe for acceleration through the Snowflake Query Acceleration Service, Snowflake provides the Query_Acceleration_Eligible view. Moreover, the System$estimate_query_acceleration function empowers users to gauge whether specific queries would have reaped the rewards of QAS acceleration.
SELECT query_id,
query_text,
start_time,
end_time,
warehouse_name,
warehouse_size,
eligible_query_acceleration_time,
upper_limit_scale_factor,
DATEDIFF(second, start_time, end_time) AS total_duration,
eligible_query_acceleration_time / NULLIF(DATEDIFF(second, start_time, end_time), 0) AS eligible_time_ratio
FROM
SNOWFLAKE.ACCOUNT_USAGE.QUERY_ACCELERATION_ELIGIBLE
WHERE
start_time >= DATEADD(day, -30, CURRENT_TIMESTAMP())
AND eligible_time_ratio <= 1.0
AND total_duration BETWEEN 3 * 60 and 5 * 60
ORDER BY (eligible_time_ratio, upper_limit_scale_factor) DESC NULLS LAST
LIMIT 100;For a retrospective view, the Query_Acceleration_History view offers insights into past accelerated queries. This retrospective analysis empowers organizations to quantify the tangible benefits brought forth by the service.
SELECT start_time,
end_time,
warehouse_name,
credits_used,
num_files_scanned,
num_bytes_scanned
FROM TABLE(SNOWFLAKE.INFORMATION_SCHEMA.QUERY_ACCELERATION_HISTORY(
DATE_RANGE_START => DATEADD('days', -1, CURRENT_DATE()),
WAREHOUSE_NAME => '<YOUR_WH_NAME>'
));You can also find the warehouses that would benefit the most for query acceleration by determining which warehouses have the largest number of queries that are eligible for acceleration and/or the warehouses with the most query acceleration eligible time.
- Identify the warehouses with the most queries eligible for the query acceleration service in the last six months:
SELECT warehouse_name, count(query_id) as num_eligible_queries, MAX(upper_limit_scale_factor)
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_ACCELERATION_ELIGIBLE
WHERE start_time > DATEADD(month, -6, CURRENT_TIMESTAMP())
GROUP BY warehouse_name
ORDER BY num_eligible_queries DESC;- Identify the warehouses with the most eligible time for the query acceleration service in the last six months:
SELECT warehouse_name, SUM(eligible_query_acceleration_time) AS total_eligible_time, MAX(upper_limit_scale_factor)
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_ACCELERATION_ELIGIBLE
WHERE start_time > DATEADD(month, -6, CURRENT_TIMESTAMP())
GROUP BY warehouse_name
ORDER BY total_eligible_time DESC;Navigating the Future with Snowflake Query Acceleration Service
In the data-driven landscape, characterized by perpetual evolution, embracing innovations like the Snowflake Query Acceleration Service is pivotal to maintaining a competitive edge. The service emerges as an innovative beacon, promising to elevate performance, optimize costs, and streamline data platform management. By providing enterprises with the capability to efficiently accelerate query workloads, Snowflake’s Query Acceleration Service paves the way for smarter, faster, and more insightful data-driven decision-making.
As we conclude our exploration of the Snowflake Query Acceleration Service, I invite you to stay connected and continue this enriching journey together. Follow me for more insightful content, deep dives into emerging technologies, and practical tips to enhance your data analytics prowess.
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