Understanding Concurrency in Snowflake - Virtual Warehouses, Resource Allocation, and Multi-Cluster Warehouses

In this article let’s dive into Concurrency within Snowflake and by using efficent resource utilization can lead to performance and efficency gains.

Snowflake distinguishes itself with its innovative approach to managing concurrent query workloads. Effective concurrency management is crucial for optimising performance, ensuring efficient resource utilisation, and delivering consistent user experiences. This article delves into the intricacies of concurrency within Snowflake, focusing on Virtual Warehouses, their resource allocation, and the role of Multi-Cluster Warehouses in enhancing concurrency.

Features change from time to time with new features being added regularly, it is recommended that you review the documentation for the latest on what specific features are included with any of the Editions.

Table of Contents

Virtual Warehouses and Resource Allocation

Architecture of Virtual Warehouses

In Snowflake, a Virtual Warehouse is a cluster of compute resources that provides the necessary CPU, memory, and temporary storage for query execution. Warehouses come in various sizes, ranging from X-Small to 4X-Large, with each size doubling the resources of the previous one. For instance, an X-Small warehouse consists of a single server, while a Small warehouse comprises two servers, effectively doubling the compute capacity. ​

Resource Allocation for Query Execution

Each server within a warehouse supports a set number of parallel execution threads. When a query is executed, Snowflake endeavours to utilise all available threads to process various operations such as scans, joins, and aggregations. This parallel processing ensures that increasing the warehouse size can lead to proportional improvements in query performance, provided the query’s complexity and data volume justify the additional resources. ​

Handling Concurrent Queries

Concurrency in Single Virtual Warehouses

When multiple queries are submitted to a single virtual warehouse simultaneously, the warehouse’s resources are shared among these queries. Snowflake’s architecture allows for a default maximum of 8 concurrent queries per warehouse cluster. Additional queries beyond this limit are queued until resources become available. It’s important to note that while increasing the warehouse size provides more resources per query, it does not increase the number of concurrent queries that can be executed; instead, it enhances the performance of individual queries. ​

Adjusting Concurrency Levels

Snowflake allows administrators to adjust the MAX_CONCURRENCY_LEVEL parameter to limit the number of concurrent queries running in a warehouse. Lowering this parameter can allocate more resources to individual queries, potentially improving their performance. However, this may lead to increased queuing for other queries, so adjustments should be made based on specific workload requirements. ​

Multi-Cluster Warehouses for Enhanced Concurrency

Concept and Benefits

To address the limitations of single-cluster warehouses in handling high concurrency, Snowflake offers Multi-Cluster Warehouses (MCWs). An MCW consists of multiple independent clusters that share the same name and access the same data but operate separately. This configuration allows Snowflake to distribute concurrent queries across different clusters, effectively mitigating resource contention and maintaining consistent performance levels during periods of high concurrency. ​

Scaling Modes: Auto-Scale and Maximised

MCWs can operate in two modes:​

• Auto-Scale Mode: Snowflake automatically starts and stops additional clusters based on the current workload, scaling out during peak times and scaling in during periods of low activity. This dynamic scaling ensures efficient resource utilisation and cost management. ​

• Maximised Mode: All clusters run simultaneously, always providing maximum resources. This mode is suitable for workloads with consistently high concurrency demands. ​

Configuration Parameters

When configuring an MCW, administrators can set the following parameters:​

• MIN_CLUSTER_COUNT: The minimum number of clusters to run.​

• MAX_CLUSTER_COUNT: The maximum number of clusters to run.​

• SCALING_POLICY: Determines how aggressively Snowflake adds or removes clusters. Options include STANDARD (default) and ECONOMY, with the latter scaling in more conservatively to reduce costs. ​

Practical Implementation

To create an MCW with auto-scaling capabilities, you can use the following SQL command:

CREATE WAREHOUSE my_mcw
  WAREHOUSE_SIZE = 'MEDIUM'
  MIN_CLUSTER_COUNT = 1
  MAX_CLUSTER_COUNT = 3
  SCALING_POLICY = 'STANDARD'
  AUTO_SUSPEND = 300
  AUTO_RESUME = TRUE;

This configuration sets up a Medium-sized warehouse that can scale between 1 and 3 clusters based on the workload, with auto-suspend after 5 minutes of inactivity and auto-resume enabled.​

Best Practices for Managing Concurrency

• Right-Size Your Warehouses: Choose a warehouse size that aligns with your query performance requirements. Larger warehouses provide more resources per query but do not increase the number of concurrent queries. ​

• Implement Multi-Cluster Warehouses for High Concurrency: For workloads with high concurrent query demands, configure MCWs to automatically scale out and handle increased load, ensuring consistent performance.

Conclusion

Effectively managing concurrency in Snowflake unlocks the platform’s full potential, ensuring optimal query performance and efficient resource utilisation. By gaining a thorough understanding of virtual warehouse architecture, implementing multi-cluster warehouses to seamlessly accommodate increased workloads, and adhering to best practices for warehouse sizing and scaling, organisations can achieve streamlined and cost-effective data operations. Proactive monitoring and timely adjustments to warehouse configurations in response to evolving workload demands will further enhance performance and elevate user satisfaction.