What causes REQUEST_LIMIT_EXCEEDED?

A high-frequency polling loop queries job runs, cluster status, or DBSQL query results faster than the per-workspace rate limit allows. Multiple concurrent pipelines or external orchestrators all call the Jobs API or SQL Warehouse API at the same time, causing a burst that exceeds the limit. Frequent calls to information schema queries (INFORMATION_SCHEMA.COLUMNS, INFORMATION_SCHEMA.TABLES) overload the catalog service

How do I fix REQUEST_LIMIT_EXCEEDED?

Step 1: Implement exponential back-off with jitter in any code that calls Databricks APIs in a loop — retry after 429 or REQUEST_LIMIT_EXCEEDED with increasing delays.. Step 2: Reduce polling frequency; prefer event-driven callbacks or Databricks webhook notifications over tight polling loops.. Step 3: For information schema queries, cache results or use Delta Lake table metadata snapshots instead of querying INFORMATION_SCHEMA per run.. Step 4: Contact Databricks support to request a rate limit increase if your legitimate workload exceeds the default per-workspace quota.

High severityresource

Power BI Refresh Error:
REQUEST_LIMIT_EXCEEDED

What does this error mean?

The Databricks REST API rejected a request because the calling organization has exceeded its allocated API request rate limit for the current time window.

Common causes

1A high-frequency polling loop queries job runs, cluster status, or DBSQL query results faster than the per-workspace rate limit allows
2Multiple concurrent pipelines or external orchestrators all call the Jobs API or SQL Warehouse API at the same time, causing a burst that exceeds the limit
3Frequent calls to information schema queries (INFORMATION_SCHEMA.COLUMNS, INFORMATION_SCHEMA.TABLES) overload the catalog service

How to fix it

1Step 1: Implement exponential back-off with jitter in any code that calls Databricks APIs in a loop — retry after 429 or REQUEST_LIMIT_EXCEEDED with increasing delays.
2Step 2: Reduce polling frequency; prefer event-driven callbacks or Databricks webhook notifications over tight polling loops.
3Step 3: For information schema queries, cache results or use Delta Lake table metadata snapshots instead of querying INFORMATION_SCHEMA per run.
4Step 4: Contact Databricks support to request a rate limit increase if your legitimate workload exceeds the default per-workspace quota.

Frequently asked questions

Is there a specific HTTP status code for this error?

Yes — Databricks returns HTTP 429 Too Many Requests for API rate limit violations. The response body contains the REQUEST_LIMIT_EXCEEDED error class.

Does this affect SQL queries running inside a notebook?

Not directly. The rate limit applies to REST API calls from external clients. SQL queries running inside Databricks compute are governed by SQL Warehouse concurrency limits, not the API rate limit.