Why optimization controls Snowflake costs - Why It Works

Introduction

Snowflake charges based on how much computing power and storage you use. Optimizing your queries and data helps reduce these costs by using resources more efficiently.

When you want to lower your monthly Snowflake bill by using fewer compute credits

When your queries take too long and use a lot of resources

When you have large data sets but want to avoid unnecessary scanning

When you want to schedule jobs that run faster and cheaper

When you want to avoid paying for idle or wasted compute time

Commands

This command shows how well your table is clustered, which affects query speed and cost by reducing data scanned.

Terminal

SELECT SYSTEM$CLUSTERING_INFORMATION('MY_SCHEMA.MY_TABLE');

Expected Output

CLUSTERING_DEPTH  CLUSTERING_RATIO

5                 0.75

This command changes the warehouse size to a smaller one to save costs when less compute power is needed.

Terminal

ALTER WAREHOUSE MY_WH SET WAREHOUSE_SIZE = 'XSMALL';

Expected Output

Statement executed successfully.

This command checks recent queries on your table to see how much time and credits they used, helping identify costly queries.

Terminal

SELECT QUERY_ID, EXECUTION_STATUS, TOTAL_ELAPSED_TIME, CREDITS_USED FROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY()) WHERE QUERY_TEXT LIKE '%MY_TABLE%' ORDER BY START_TIME DESC LIMIT 5;

Expected Output

QUERY_ID          EXECUTION_STATUS  TOTAL_ELAPSED_TIME  CREDITS_USED

01a2b3c4d5e6f7g8  SUCCESS           1200                0.5

09h8g7f6e5d4c3b2  SUCCESS           800                 0.3

...

This command reorganizes the table data to improve clustering, which can reduce the amount of data scanned and lower costs.

Terminal

ALTER TABLE MY_SCHEMA.MY_TABLE RECLUSTER;

Expected Output

Statement executed successfully.

Key Concept

If you remember nothing else, remember: optimizing data layout and query efficiency directly reduces Snowflake compute costs.

Common Mistakes

Ignoring table clustering and running full scans on large tables

This causes queries to scan more data than needed, increasing compute time and costs.

Use clustering keys and monitor clustering info to keep data organized for efficient scanning.

Using a large warehouse size all the time regardless of workload

This wastes compute credits when smaller warehouses could handle the load.

Adjust warehouse size based on workload demand to balance speed and cost.

Not reviewing query history to find expensive queries

Without this, costly queries go unnoticed and keep increasing your bill.

Regularly check query history to identify and optimize expensive queries.

Summary

Use SYSTEM$CLUSTERING_INFORMATION to check how well your tables are organized.

Adjust warehouse size with ALTER WAREHOUSE to save compute costs.

Review query history to find and optimize expensive queries.

Recluster tables to improve data layout and reduce scanned data.

Practice

1. Why does optimizing queries in Snowflake help reduce costs?

easy

A. Because it increases the size of the data stored

B. Because it uses fewer computing resources and runs faster

C. Because it requires more warehouses to run

D. Because it disables auto-suspend on warehouses

Solution

1. Step 1: Understand resource usage in Snowflake

   Snowflake charges based on compute resources used, so less compute means lower cost.

2. Step 2: Link query optimization to resource use

   Optimized queries run faster and scan less data, using fewer resources.

3. Final Answer:

   Because it uses fewer computing resources and runs faster -> Option B

4. Quick Check:

   Optimization reduces compute = lower cost [OK]

Hint: Less compute time means lower cost in Snowflake [OK]

Common Mistakes:

• Thinking more data scanned reduces cost
• Believing bigger warehouses always save money
• Ignoring auto-suspend settings

2. Which Snowflake warehouse setting helps save costs by stopping compute when not in use?

easy

A. Manual scaling

B. Auto-resume

C. Auto-suspend

D. Clustering keys

Solution

1. Step 1: Identify warehouse settings related to cost

   Auto-suspend stops the warehouse after inactivity, saving compute time.

2. Step 2: Differentiate from other options

   Auto-resume starts warehouse automatically but does not save cost alone; clustering keys optimize data but don't stop compute.

3. Final Answer:

   Auto-suspend -> Option C

4. Quick Check:

   Auto-suspend stops compute = saves cost [OK]

Hint: Auto-suspend pauses warehouse to save money [OK]

Common Mistakes:

• Confusing auto-resume with auto-suspend
• Thinking clustering keys stop compute
• Assuming manual scaling always saves cost

3. Given a query scanning 1TB of data on a large warehouse, what happens if clustering reduces scanned data to 100GB?

medium

A. Query runs faster and costs less

B. Query runs faster but costs more

C. Query runs slower and costs more

D. Query runs slower but costs less

Solution

1. Step 1: Understand clustering effect on data scanned

   Clustering reduces data scanned by organizing data, so less data is processed.

2. Step 2: Link data scanned to query speed and cost

   Less data scanned means faster query and lower compute cost.

3. Final Answer:

   Query runs faster and costs less -> Option A

4. Quick Check:

   Less data scanned = faster + cheaper query [OK]

Hint: Less data scanned means faster and cheaper queries [OK]

Common Mistakes:

• Assuming clustering increases cost
• Thinking faster queries cost more
• Ignoring data scanned impact

4. A Snowflake user notices high costs despite small warehouses. What is a likely cause?

medium

A. Queries scan large amounts of unnecessary data

B. Warehouses are set to auto-suspend after 1 minute

C. Clustering keys are properly defined

D. Warehouses are paused when idle

Solution

1. Step 1: Analyze cost factors with small warehouses

   Small warehouses reduce compute size but scanning large data still increases cost.

2. Step 2: Identify inefficient query behavior

   Queries scanning unnecessary data waste compute time and increase cost despite warehouse size.

3. Final Answer:

   Queries scan large amounts of unnecessary data -> Option A

4. Quick Check:

   Unoptimized queries cause high cost even on small warehouses [OK]

Hint: Check data scanned, not just warehouse size [OK]

Common Mistakes:

• Assuming auto-suspend causes high cost
• Believing clustering keys increase cost
• Ignoring query data scanned

5. You want to reduce Snowflake costs for a large dataset queried daily. Which combined approach is best?

hard

A. Use large warehouses with auto-resume and no clustering

B. Use manual scaling and avoid query tuning

C. Disable auto-suspend and run queries on medium warehouses

D. Use small warehouses with auto-suspend and optimize queries with clustering

Solution

1. Step 1: Evaluate warehouse size and auto-suspend

   Small warehouses with auto-suspend save cost by limiting compute time and size.

2. Step 2: Consider query optimization and clustering

   Optimizing queries and clustering reduce data scanned, speeding queries and lowering cost.

3. Step 3: Combine for best cost control

   Using small warehouses with auto-suspend plus query optimization and clustering balances cost and performance.

4. Final Answer:

   Use small warehouses with auto-suspend and optimize queries with clustering -> Option D

5. Quick Check:

   Small warehouse + auto-suspend + clustering = cost control [OK]

Hint: Combine small warehouse, auto-suspend, and clustering for savings [OK]

Common Mistakes:

• Choosing large warehouses without auto-suspend
• Ignoring query optimization
• Disabling auto-suspend increases cost