How Queries Flow in Snowflake: Execution, Metadata & Optimization

 

Introduction

In our previous blog, we explored how Snowflake's architecture is built on three powerful layers: Storage, Compute, and Cloud Services. Now, let's dive into how a query actually travels through these layers , because understanding what happens inside Snowflake is key to optimizing both performance and cost.

To make this easy to grasp, we are using a real-world analogy:

A Snowflake query is like an airline passenger moving through an airport.

From check-in → security → air traffic control → boarding → flying → arrival → fast-track immigration.

Each stop maps perfectly to a stage in Snowflake's query lifecycle.

By the end of this blog, you'll clearly understand:

• How Snowflake processes queries behind the scenes
• Why metadata is Snowflake's biggest competitive advantage
•  How query optimization reduces cost and improves speed

Let's board the flight!

---

1. Query Submitted → Check-in Counter

When you run a SQL query in Snowflake (via the web UI, a BI tool, Python, JDBC, or any other interface):

✔ The request first lands at the Cloud Services Layer

✔ Snowflake immediately checks: "Who are you?" and "What are you allowed to access?"

Airport Analogy:

The check-in staff verify your ticket and identity.

If something's wrong → you're stopped right here.

(Similarly, Snowflake returns an authentication or authorization error)

---

2. Parsing & Validation → Security Check

Snowflake now parses and validates your query:

• Is the SQL syntax valid?
• Do the tables and columns exist?
• Does your role have the necessary privileges?

If anything fails validation → Snowflake stops the query immediately and returns an error.

Airport Analogy:

Security checks your bags and documents.

If something's suspicious → you're not allowed to proceed.

Same here: only valid, authorized queries move forward.

---

3. Query Optimization → Air Traffic Control

This is where Snowflake really shines.

The Cloud Services Layer creates the most efficient execution plan using metadata from storage:

Metadata Used
                     Why It Matters
Min/Max values of micro-partitions
                      Skips scanning irrelevant partitions (partition pruning)
Column statistics & row counts
                      Chooses the most efficient join methods
Clustering information
                      Optimizes filtering and sorting operations

This reduces:

• Data scanned
• Query execution time
• Warehouse usage → Lower costs

Airport Analogy:

Air Traffic Control decides:

• Best runway to use
• Shortest flight route
• Path with least congestion

You haven't boarded yet—they're just planning the most efficient flight.

★ Key Point: Optimization is FREE

No warehouse credits are consumed during query planning and optimization.

---

4. Warehouse Allocation → Boarding Gate

Now Snowflake assigns the query to the appropriate Virtual Warehouse:

• If the warehouse is suspended → it auto-resumes (takes a few seconds)
• If there are too many concurrent queries → Snowflake may spin up additional clusters (if multi-cluster is enabled)
• If no warehouse is available → the query waits in queue

Airport Analogy:

You arrive at the boarding gate.

The plane is ready, pilots are in position, boarding begins. 

(The Compute layer is like the airplane—it actually carries you to your destination)