How to Fix "max memory exceeded" in BigQuery

How do I resolve BigQuery's "max memory exceeded" error?

The error occurs when a query’s working data exceeds the RAM BigQuery allocates for the slot reservation, forcing the job to abort.

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Description

Example H2

Example H3

What triggers the “max memory exceeded” error?

BigQuery allocates memory per slot. When joins, aggregations, or large result sets need more RAM than those slots supply, the job stops and raises the error.

How can I quickly confirm the cause?

Check the Query Plan tab. Look for stages marked “Repartition” or “Hash Join” with high shuffle or peak memory. These stages usually exhaust memory.

Which tactics lower memory use fast?

1️⃣ Prune columns early

Use explicit column lists instead of *.Less data flows to later stages, lowering memory.

2️⃣ Filter before joining

Push WHERE clauses or use sub-queries to shrink tables before expensive joins.

3️⃣ Break queries with temp tables

Materialize intermediate results into CREATE TEMP TABLE.Each step gets fresh memory limits.

4️⃣ Enable partitioned & clustered tables

Store Orders partitioned by order_date and clustered by customer_id so scans stay small.

5️⃣ Use APPROX* functions

APPROX_COUNT_DISTINCT and friends use sketches that require far less memory than exact counts.

6️⃣ Apply join hints

Force broadcast joins with JOIN EACH or /*+ BROADCAST_JOIN */ when one side is tiny.

Can SQL syntax help avoid the error?

BigQuery offers table decorators, sub-query filters, and join hints that directly reduce memory usage.See syntax below.

When should I raise slot capacity?

If the query is already optimized, consider flex slots or reservation autoscale. More slots equal more aggregate RAM.

Best practices checklist

• Select only needed columns
• Filter early and aggressively
• Aggregate after reducing rows
• Prefer partitioned, clustered storage
• Break monolith queries
• Use approximate aggregations
• Monitor stages in Query Plan

Why How to Fix "max memory exceeded" in BigQuery is important

How to Fix "max memory exceeded" in BigQuery Example Usage


-- Aggregate last-year revenue with reduced memory
WITH filtered_orders AS (
  SELECT id, customer_id, total_amount
  FROM   `project.dataset.Orders`
  WHERE  order_date BETWEEN '2023-01-01' AND '2023-12-31'
), customer_totals AS (
  SELECT customer_id, SUM(total_amount) AS yearly_spend
  FROM   filtered_orders
  GROUP  BY customer_id
)
SELECT c.name, ct.yearly_spend
FROM   customer_totals ct
JOIN   `project.dataset.Customers` c ON c.id = ct.customer_id
ORDER  BY ct.yearly_spend DESC
LIMIT  20;

How to Fix "max memory exceeded" in BigQuery Syntax


-- Prune columns and filter early
SELECT c.id, c.name, SUM(oi.quantity) AS total_items
FROM `project.dataset.Customers` AS c
JOIN `project.dataset.Orders`      AS o  ON o.customer_id = c.id AND o.order_date >= '2023-01-01'
JOIN /*+ BROADCAST_JOIN */ `project.dataset.OrderItems` AS oi ON oi.order_id = o.id
GROUP BY c.id, c.name;

-- Break into temp tables
declare start_date date default '2023-01-01';
CREATE TEMP TABLE recent_orders AS
SELECT id, customer_id
FROM   `project.dataset.Orders`
WHERE  order_date >= start_date;

SELECT p.name, SUM(oi.quantity) AS qty
FROM   recent_orders ro
JOIN   `project.dataset.OrderItems` oi ON oi.order_id = ro.id
JOIN   `project.dataset.Products`   p ON p.id = oi.product_id
GROUP  BY p.name;

Common Mistakes

Running SELECT * on multi-GB tables forces BigQuery to shuffle all columns, inflating memory. Always specify only needed columns.
Joining large tables before filtering ignores predicate pushdown, generating bigger hash maps and exceeding memory. Filter in sub-queries first.