Turbocharge Snowpark Workloads: RESOURCE_CONSTRAINT for Optimized Warehouses Is Now GA!

Snowflake took another big step in empowering data engineers and ML practitioners by announcing General Availability (GA) of the RESOURCE_CONSTRAINT clause for Snowpark-optimized warehouses — for 16 GB and 256 GB configurations.

This enhancement gives developers and data teams precise control over memory size and CPU architecture, enabling high-performance Snowpark workloads such as ML model training and memory-intensive UDFs to run on finely tuned, single-node environments.

Key Use Case: Machine Learning training with Snowpark Python stored procedures — now with predictable performance and resource isolation.

What Problem Does This Solve?

Not all workloads are created equal.

While standard virtual warehouses are great for general SQL processing, Snowpark workloads often require:

• Higher memory for ML model training.
• Predictable CPU architecture (e.g., x86).
• A single-node setup with optimized execution environments.

Previously, this was hit-or-miss — but with RESOURCE_CONSTRAINT, you can now explicitly configure these parameters.

What Is RESOURCE_CONSTRAINT?

The RESOURCE_CONSTRAINT clause allows you to specify the memory and CPU architecture when creating or altering Snowpark-optimized warehouses using SQL or Python interfaces.

GA Resource Constraints

• MEMORY_1X, MEMORY_1X_x86 — 16 GB
• MEMORY_16X, MEMORY_16X_x86 — 256 GB

These configurations are now GA across all cloud platforms (AWS, Azure, GCP).

Still in Preview

• MEMORY_64X, MEMORY_64X_x86 — 1 TB
• Available only on AWS for now.

Creating a Snowpark-Optimized Warehouse (SQL)

-- Basic Snowpark-optimized warehouse (default 16x memory)
--Default: If you omit RESOURCE_CONSTRAINT, Snowflake uses MEMORY_16X.
CREATE OR REPLACE WAREHOUSE snowpark_opt_wh_m 
  WAREHOUSE_SIZE = 'MEDIUM'
  WAREHOUSE_TYPE = 'SNOWPARK-OPTIMIZED';

-- 256 GB with x86 CPU architecture
CREATE WAREHOUSE so_warehouse_l 
  WAREHOUSE_SIZE = 'LARGE'
  WAREHOUSE_TYPE = 'SNOWPARK-OPTIMIZED'
  RESOURCE_CONSTRAINT = 'MEMORY_16X_X86';

-- Create an X-Large Snowpark-optimized warehouse with 256 GB and x86 architecture
CREATE WAREHOUSE so_warehouse_xl 
  WAREHOUSE_TYPE = 'SNOWPARK-OPTIMIZED'
  WAREHOUSE_SIZE = XLARGE
  RESOURCE_CONSTRAINT = 'MEMORY_16X_X86';

Modifying an Existing Warehouse

Ensure the warehouse is SUSPENDED before changing resource constraints:

-- Suspend the warehouse
ALTER WAREHOUSE so_warehouse SUSPEND;

-- Change the resource constraint (e.g., to 16 GB x86)
ALTER WAREHOUSE so_warehouse_l 
  SET RESOURCE_CONSTRAINT = 'MEMORY_1X_X86';

-- Modify the memory resources to 256 GB and CPU to x86 for Snowpark workloads
ALTER WAREHOUSE so_warehouse_l 
  SET RESOURCE_CONSTRAINT = 'MEMORY_16X_X86';

When Should You Use This?

Ideal for:

• ML training using Snowpark Python stored procedures
• Data pipelines with memory-heavy UDFs/UDTFs
• Performance benchmarking between CPU architectures

Not Ideal for:

• Classic SQL workloads without Snowpark usage
• Multi-node distributed compute jobs (use standard warehouses instead)

Key Configurations at a Glance

| Value            | Memory | CPU     | Min Warehouse Size | Availability           |
| ---------------- | ------ | ------- | ------------------ | ---------------------- |
| MEMORY\_1X       | 16 GB  | Default | XSMALL             | GA (All Clouds)        |
| MEMORY\_1X\_x86  | 16 GB  | x86     | XSMALL             | GA (All Clouds)        |
| MEMORY\_16X      | 256 GB | Default | MEDIUM             | GA (All Clouds)        |
| MEMORY\_16X\_x86 | 256 GB | x86     | MEDIUM             | GA (All Clouds)        |
| MEMORY\_64X      | 1 TB   | Default | LARGE              | Preview (AWS only)     |
| MEMORY\_64X\_x86 | 1 TB   | x86     | LARGE              | Preview (AWS only)     |

ℹ️ Snowflake bills Snowpark-optimized warehouses based on memory and compute resources. Refer to the Snowflake Service Consumption Table for up-to-date credit pricing.

Things to Keep in Mind

• Snowpark-optimized warehouses are single-node — great for code execution, not large parallel queries.
• Startup time is longer than standard warehouses.
• If you’re on Azure or GCP, stick with 16 GB or 256 GB options for now.

Final Thoughts

The general availability of the RESOURCE_CONSTRAINT clause gives Snowpark power users exactly what they’ve been waiting for: fine-grained resource tuning. Whether you’re building ML workflows, running massive UDFs, or just want predictability in performance, this feature delivers.

It’s time to take your AI and Python workloads in Snowflake to the next level.

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