How to Fix "Max Memory Exceeded" in Snowflake

Galaxy Glossary

How do I fix Snowflake max memory exceeded error?

The error appears when a query’s execution memory surpasses the limit Snowflake allocates to the session or warehouse.

Welcome to the Galaxy, Guardian!

Oops! Something went wrong while submitting the form.

Description

Example H2

Example H3

Why does Snowflake show “max memory exceeded”?

Snowflake allocates fixed memory per warehouse cluster. Hash-heavy joins, large intermediate results, or missing partition pruning can surpass that limit and abort the query with this error.

How do I check memory usage during a query?

Run SELECT * FROM TABLE(SYSTEM$MEMORY_USAGE()) in a separate tab. The view reveals current and peak memory for each running query, helping you spot spikes early.

Can I raise the memory cap quickly?

Yes. Scale the warehouse to the next size: ALTER WAREHOUSE dev_wh SET WAREHOUSE_SIZE = LARGE; or add clusters for multi-cluster warehouses. Both actions grant more memory instantly.

Which query patterns waste memory?

Cartesian joins, CROSS JOIN without filters, selecting all columns from wide tables, and window functions over unbounded partitions create massive intermediates. Rewrite or add filters/partition keys.

How do I rewrite a heavy join?

Filter early, project only needed columns, and aggregate before joining. Use SELECT customer_id, SUM(total_amount) grouped in a CTE, then join to cut memory by orders of magnitude.

Should I rely on result caching?

Yes. If the data is unchanged and the query text matches, Snowflake serves results from cache using almost no memory. Encourage users to rerun identical queries instead of copy-pasting slight variations.

Full example: optimizing an orders-products analysis

The example below aggregates OrderItems first, joins on reduced datasets, and runs comfortably inside a SMALL warehouse.

Why How to Fix "Max Memory Exceeded" in Snowflake is important

How to Fix "Max Memory Exceeded" in Snowflake Example Usage


-- Original failing query (hash joins on full tables)
SELECT c.id, c.name, SUM(oi.quantity * p.price) AS lifetime_value
FROM Customers c
JOIN Orders o        ON o.customer_id = c.id
JOIN OrderItems oi   ON oi.order_id   = o.id
JOIN Products p      ON p.id          = oi.product_id
GROUP BY c.id, c.name;

-- Optimized version that avoids max-memory exceeded
WITH item_totals AS (
  SELECT order_id, SUM(quantity * p.price) AS order_value
  FROM OrderItems oi
  JOIN Products p ON p.id = oi.product_id
  GROUP BY order_id
)
SELECT c.id, c.name, SUM(it.order_value) AS lifetime_value
FROM Customers c
JOIN Orders o  ON o.customer_id = c.id
JOIN item_totals it ON it.order_id = o.id
GROUP BY c.id, c.name;

How to Fix "Max Memory Exceeded" in Snowflake Syntax


-- Increase warehouse memory
ALTER WAREHOUSE dev_wh SET WAREHOUSE_SIZE = LARGE;  -- XS, S, M, L, XL, 2XL, etc.

-- Monitor memory while a query runs
SELECT * FROM TABLE(SYSTEM$MEMORY_USAGE());

-- Efficient join pattern
WITH item_totals AS (
  SELECT order_id, SUM(quantity * p.price) AS order_value
  FROM OrderItems oi
  JOIN Products p ON p.id = oi.product_id
  GROUP BY order_id
)
SELECT c.id, c.name, SUM(it.order_value) AS lifetime_value
FROM Customers c
JOIN Orders o ON o.customer_id = c.id
JOIN item_totals it ON it.order_id = o.id
WHERE c.created_at >= '2023-01-01'
GROUP BY c.id, c.name;

Common Mistakes

Blindly scaling to a larger warehouse. While it works, it raises costs. Always attempt query rewrites and early filtering first.
Selecting * in exploratory joins. Pulling every column inflates intermediate data; list only the fields you need to keep memory use small.