"Complexity budget exhaustion."
If you are building a scalable integration on Monday.com, you have seen this error. It usually happens right when you move from "Dev" (10 items) to "Production" (1000 items).
The official docs tell you that limits exist (5,000,000 points/minute), but they don't do a great job explaining how not to hit them.
Here is the operational guide to API Complexity.
The Cost of Nesting
GraphQL allows you to fetch everything in one go. That is its superpower, and its trap.
This query is dangerous:
query {
boards (ids: [123]) {
items_page {
items {
column_values {
text
}
subitems { # <--- THE KILLER
column_values {
text
}
}
}
}
}
}
Why? Monday calculates complexity based on potential return size, not actual return size.
* 1 Board
* x 50 Items (default page)
* x 30 Columns
* x 50 Subitems (potential)
* x 30 Subitem Columns
You just requested 2,250,000 data points in a single call. You will hit the complexity limit instantly.
Strategy 1: The "ID-Only" Bridge
Never query Subitems nested inside Items if you can avoid it. It is cheaper to do two calls.
Call 1: Get the Item IDs.
graphql
query {
boards(ids: [MainBoard]) { items_page { items { id } } }
}
Call 2: Query the Subitems directly (if you have their board ID) or query the Items by ID.
But the real trick is Specific Column Selection.
Strategy 2: Don't ask for column_values (Generic)
Asking for column_values returns every column on the board. If you only need Status and Date, ask for them specifically by ID.
Expensive:
graphql
column_values { text }
Cheap:
graphql
column_values(ids: ["status", "date4"]) { text }
This reduces the complexity multiplier from ~30 (all columns) to 2.
Strategy 3: The "Cursor" Pagination Pattern
For production implementations, I never just "get all items". I implemented a Cursor Iterator.
def fetch_all_items(board_id)
cursor = nil
loop do
response = query_items(board_id, cursor: cursor)
items = response['data']['boards'][0]['items_page']['items']
yield items
cursor = response['data']['boards'][0]['items_page']['cursor']
break unless cursor
end
end
def fetch_all_items(board_id):
cursor = None
while True:
response = query_items(board_id, cursor=cursor)
data = response['data']['boards'][0]['items_page']
yield data['items']
cursor = data.get('cursor')
if not cursor:
break
async function* fetchAllItems(boardId) {
let cursor = null;
while (true) {
const response = await queryItems(boardId, cursor);
const data = response.data.boards[0].items_page;
yield data.items;
cursor = data.cursor;
if (!cursor) break;
}
}
This keeps each individual request small (low complexity score) while effectively draining the entire database.
The "Gotcha": Rate Limits vs Complexity
Remember:
* Complexity: How much data you asked for (Cost per Query).
* Rate Limit: How many times you asked (Calls per Minute).
Optimizing Complexity often means increasing the number of calls (pagination). This is a tradeoff. For Monday.com, Complexity is usually the first wall you hit. Optimize for small, specific payloads over massive "God Queries".
This is part of the "Universal PMO Architecture" series.