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API Rate Limiting in Databricks: Causes, Troubleshooting, and Solutions

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Introduction

API rate limiting in Databricks can throttle requests, cause job failures, and disrupt data workflows. When Databricks or external services impose rate limits, exceeding these limits leads to errors like HTTP 429 (Too Many Requests), delayed executions, and failed API calls.

🚨 Common issues caused by API rate limiting in Databricks:

  • HTTP 429: Too Many Requests errors when calling APIs.
  • Slow or throttled responses from Databricks REST API, AWS S3, Azure ADLS, GCS, or third-party APIs.
  • Job failures or partial data ingestion due to API call rejections.
  • Intermittent API failures that succeed when retried later.

This guide explores common causes of API rate limiting, troubleshooting steps, and best practices to manage rate limits efficiently in Databricks.


Understanding API Rate Limits in Databricks

API rate limits affect:

  1. Databricks REST API – Limits on job submissions, cluster management, workspace operations.
  2. Cloud Storage APIs (AWS S3, Azure ADLS, GCS) – Limits on read/write operations.
  3. Third-Party APIs – APIs for data ingestion (e.g., Twitter API, OpenAI API, Salesforce API).

💡 Rate limits vary depending on:

  • The Databricks plan (Standard, Premium, Enterprise).
  • The cloud provider’s API usage quotas.
  • The specific API endpoint being called.

Common API Rate Limiting Issues and Fixes

1. Databricks REST API Rate Limiting (HTTP 429 Errors)

Symptoms:

  • Error: “HTTP 429 Too Many Requests.”
  • Job submissions fail when sending too many API requests at once.
  • Cluster management (start/stop) API calls are throttled or delayed.

Causes:

  • Too many simultaneous REST API requests hitting Databricks API limits.
  • Automated scripts sending frequent requests in a loop without handling retries.
  • The Databricks workspace has hit its API quota (especially for Standard plans).

Fix:
Enable Exponential Backoff with Retry Logic:

import time
import requests

def call_databricks_api_with_retry(url, headers, retries=5):
    for i in range(retries):
        response = requests.get(url, headers=headers)
        if response.status_code == 200:
            return response.json()
        elif response.status_code == 429:
            wait_time = 2 ** i  # Exponential backoff
            print(f"Rate limit exceeded. Retrying in {wait_time} seconds...")
            time.sleep(wait_time)
        else:
            response.raise_for_status()

api_url = "https://databricks-instance/api/2.0/clusters/list"
headers = {"Authorization": "Bearer YOUR_TOKEN"}

data = call_databricks_api_with_retry(api_url, headers)

Reduce API Request Frequency:

  • Instead of polling frequently, use event-driven notifications or Databricks webhooks.
  • Batch API calls instead of making many small requests.

Use the Databricks SDK Instead of Raw API Calls:

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()
clusters = w.clusters.list()

2. AWS S3, Azure ADLS, and Google Cloud Storage Rate Limiting

Symptoms:

  • Slow or throttled reads/writes from cloud storage.
  • Job failures due to S3, ADLS, or GCS API rejections.
  • Intermittent data ingestion failures.

Causes:

  • Too many simultaneous requests (e.g., too many workers hitting S3 at once).
  • Cloud storage API quotas exceeded for the region.
  • Inefficient partitioning of data queries, leading to excessive API calls.

Fix:
Use Parallel Reads/Writes Efficiently (Avoid Overloading API Limits)

df = spark.read.format("parquet").load("s3://mybucket/data/")
df = df.repartition(10)  # ✅ Reduces excessive API requests

Use AWS PrivateLink or Azure Private Endpoints to Reduce Throttling

  • These reduce latency and prevent throttling by routing traffic internally.

Enable Caching to Reduce Repeated API Calls

df.cache()  # ✅ Caches data in-memory to avoid repeated S3 reads
df.show()

Implement Retry Logic for Cloud Storage Reads

from pyspark.sql.utils import AnalysisException

def read_s3_with_retry(path, retries=5):
    for i in range(retries):
        try:
            return spark.read.parquet(path)
        except AnalysisException as e:
            if "Rate exceeded" in str(e):
                wait_time = 2 ** i
                print(f"API throttled. Retrying in {wait_time} seconds...")
                time.sleep(wait_time)
            else:
                raise e

df = read_s3_with_retry("s3://mybucket/data/")

3. Third-Party API Rate Limiting (Twitter API, OpenAI API, Salesforce, etc.)

Symptoms:

  • API requests fail with rate-limit errors.
  • Data ingestion scripts stop working intermittently.
  • API quotas reset after some time, allowing requests to succeed again.

Causes:

  • Too many requests within a short window.
  • API quota limits reached (e.g., Twitter API allows limited tweets per 15-minute window).
  • Not handling retry-after headers in API responses.

Fix:
Check the API Provider’s Rate Limits and Adjust Requests

  • Twitter API: 900 requests per 15 minutes for standard access.
  • OpenAI API: 60 requests per minute for free-tier users.
  • Salesforce API: 100,000 requests per day for enterprise users.

Use Rate Limit Headers to Adjust Request Timing

import requests

response = requests.get("https://api.twitter.com/2/tweets")
if response.status_code == 429:
    retry_after = int(response.headers.get("Retry-After", 60))  # Default 60 seconds
    print(f"Rate limit exceeded. Retrying in {retry_after} seconds...")
    time.sleep(retry_after)

Batch Requests Instead of Sending Many Small Requests

  • Use bulk APIs whenever possible to reduce total request count.
  • Group multiple requests into a single API call.

4. Slow Databricks Notebook Execution Due to API Rate Limiting

Symptoms:

  • REST API calls in notebooks are throttled.
  • Intermittent failures when fetching data from external sources.
  • Cluster UI shows slow API request execution times.

Causes:

  • Notebook making too many API calls in a short time.
  • No rate-limiting protection or retry mechanisms in API requests.
  • Databricks interactive notebooks may overload Databricks API.

Fix:
Limit the Number of API Calls per Notebook Execution

import time

for i in range(10):
    print(f"API request {i+1}")
    time.sleep(2)  # ✅ Add delay between requests to avoid throttling

Use Asynchronous Processing Instead of Serial Requests

from concurrent.futures import ThreadPoolExecutor

def fetch_data(url):
    response = requests.get(url)
    return response.json()

urls = ["https://api.example.com/data1", "https://api.example.com/data2"]
with ThreadPoolExecutor(max_workers=5) as executor:
    results = list(executor.map(fetch_data, urls))

Best Practices to Avoid API Rate Limiting in Databricks

Implement Exponential Backoff and Retry Logic

  • Start with a small retry delay (e.g., 2 seconds) and double the wait time on each retry.

Batch API Calls Whenever Possible

  • Reduce the number of API calls by sending bulk requests.

Use Caching to Reduce API Dependence

  • Cache frequently accessed data in-memory or on DBFS.

Monitor API Quotas and Logs for Throttling Alerts

  • Use Databricks logs and monitoring tools to track API failures.

Conclusion

API rate limiting in Databricks can impact REST API interactions, cloud storage performance, and third-party integrations. By implementing retry logic, optimizing request batching, and monitoring API quotas, teams can prevent failures and ensure seamless data processing.

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