Handle OCI API rate limits with defensive retry patterns and known limits by service.
Use when automating bulk OCI operations, hitting 429 TooManyRequests errors, or building resilient API clients.
Trigger with "oraclecloud rate limits", "oci 429 error", "oci throttling", "oci backoff".
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name
oraclecloud-rate-limits
description
Handle OCI API rate limits with defensive retry patterns and known limits by service.
Use when automating bulk OCI operations, hitting 429 TooManyRequests errors, or building resilient API clients.
Trigger with "oraclecloud rate limits", "oci 429 error", "oci throttling", "oci backoff".
allowed-tools
Read, Write, Edit, Bash(pip:*), Bash(oci:*), Grep
version
1.7.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
["saas","oraclecloud","oci"]
compatibility
Designed for Claude Code
Oracle Cloud Rate Limits
Overview
OCI API rate limits vary by service and are not well documented. A 429 TooManyRequests response kills your automation, and unlike AWS or Azure, OCI does not return a Retry-After header. This skill maps known limits by service, implements exponential backoff with jitter, and provides circuit breaker patterns for bulk operations.
Purpose: Build resilient OCI API clients that handle throttling gracefully without data loss.
Prerequisites
OCI Python SDK — pip install oci
OCI config file at ~/.oci/config with valid credentials (user, fingerprint, tenancy, region, key_file)
Python 3.8+
Understanding of which OCI service you are calling (limits vary per service)
Instructions
Step 1: Know the Limits
OCI publishes some rate limits, but many are undocumented. Here are the known limits observed in production:
Service
Endpoint Type
Observed Limit
Notes
Compute
List/Get
~20 req/sec
Per-tenancy, not per-user
Compute
Create/Update/Delete
~10 req/sec
Stricter for mutating operations
Object Storage
List/Get
~100 req/sec
Per-bucket namespace
Object Storage
Put/Delete
~50 req/sec
Varies by region load
Identity
List/Get
~10 req/sec
Tenancy-wide shared limit
Identity
Create/Update
~5 req/sec
Very conservative
Database
All operations
~10 req/sec
Shared across DB family
Networking (VCN)
All operations
~20 req/sec
Per-compartment
Monitoring
Post metrics
~50 req/sec
Per-metric namespace
Events
Rule CRUD
~10 req/sec
Per-compartment
Critical: These are observed limits, not guaranteed SLAs. OCI may throttle lower under load.
Step 2: Implement Exponential Backoff with Jitter
OCI returns no Retry-After header on 429 responses, so you must implement your own backoff. The SDK's built-in retry handles some cases, but for bulk operations you need explicit control:
import oci
import time
import random
config = oci.config.from_file("~/.oci/config")
defcall_with_retry(fn, max_retries=5, base_delay=1.0):
"""Call an OCI SDK function with exponential backoff and jitter.
OCI returns 429 TooManyRequests with NO Retry-After header,
so we implement our own backoff strategy.
"""for attempt inrange(max_retries):
try:
return fn()
except oci.exceptions.ServiceError as e:
if e.status == 429:
# Exponential backoff with full jitter
delay = base_delay * (2 ** attempt)
jitter = random.uniform(0, delay)
wait_time = delay + jitter
print(f"Rate limited (429). Attempt {attempt + 1}/{max_retries}. "f"Waiting {wait_time:.1f}s")
time.sleep(wait_time)
elif e.status >= 500:
# Server errors — retry with backoff
delay = base_delay * (2 ** attempt)
print(f"Server error ({e.status}). Retrying in {delay}s")
time.sleep(delay)
else:
raise# 4xx errors (except 429) are not retryableraise Exception(f"Max retries ({max_retries}) exceeded")
Step 3: Use the SDK's Built-in Retry Configuration
The OCI Python SDK supports retry configuration natively. Use this for simple cases:
import oci
from oci.retry import RetryStrategyBuilder
config = oci.config.from_file("~/.oci/config")
# Build a custom retry strategy
retry_strategy = RetryStrategyBuilder(
max_attempts_check=True,
max_attempts=5,
total_elapsed_time_check=True,
total_elapsed_time_seconds=300,
retry_max_wait_between_calls_seconds=30,
retry_base_sleep_time_seconds=1,
service_error_check=True,
service_error_retry_on_any_5xx=True,
service_error_retry_config={429: []}, # Retry on 429 with any message
backoff_type=oci.retry.BACKOFF_DECORRELATED_JITTER
).get_retry_strategy()
compute = oci.core.ComputeClient(config, retry_strategy=retry_strategy)
# All calls through this client will automatically retry on 429 and 5xx
instances = compute.list_instances(
compartment_id="ocid1.compartment.oc1..example"
)
Step 4: Implement a Circuit Breaker for Bulk Operations
For operations that process hundreds of resources, a circuit breaker prevents cascading failures:
import oci
import time
import random
classOCICircuitBreaker:
"""Circuit breaker for bulk OCI API operations."""def__init__(self, failure_threshold=5, reset_timeout=60):
self.failure_count = 0self.failure_threshold = failure_threshold
self.reset_timeout = reset_timeout
self.last_failure_time = 0self.state = "closed"# closed = normal, open = blockingdefcall(self, fn, max_retries=3, base_delay=1.0):
ifself.state == "open":
if time.time() - self.last_failure_time > self.reset_timeout:
self.state = "half-open"print("Circuit half-open — testing one request")
else:
remaining = self.reset_timeout - (time.time() - self.last_failure_time)
raise Exception(f"Circuit open. Retry in {remaining:.0f}s")
try:
result = call_with_retry(fn, max_retries=max_retries, base_delay=base_delay)
ifself.state == "half-open":
self.state = "closed"self.failure_count = 0print("Circuit closed — resuming normal operation")
return result
except Exception:
self.failure_count += 1self.last_failure_time = time.time()
ifself.failure_count >= self.failure_threshold:
self.state = "open"print(f"Circuit OPEN after {self.failure_count} failures. "f"Pausing for {self.reset_timeout}s")
raise# Usage for bulk listing
breaker = OCICircuitBreaker(failure_threshold=3, reset_timeout=30)
config = oci.config.from_file("~/.oci/config")
compute = oci.core.ComputeClient(config)
compartment_ids = ["ocid1.compartment.oc1..aaa", "ocid1.compartment.oc1..bbb"]
all_instances = []
for cid in compartment_ids:
result = breaker.call(
lambda c=cid: compute.list_instances(compartment_id=c)
)
all_instances.extend(result.data)
time.sleep(0.1) # Courtesy delay between bulk calls
Step 5: Batch Operations with Rate Limiting
For operations that must process many items (e.g., tagging all instances), throttle proactively:
import oci
import time
config = oci.config.from_file("~/.oci/config")
compute = oci.core.ComputeClient(config)
defbatch_with_throttle(items, operation, requests_per_second=5):
"""Process items with proactive rate limiting."""
delay = 1.0 / requests_per_second
results = []
for i, item inenumerate(items):
result = call_with_retry(lambda it=item: operation(it))
results.append(result)
if (i + 1) % 50 == 0:
print(f"Processed {i + 1}/{len(items)}")
time.sleep(delay)
return results
Output
Successful implementation produces:
A retry wrapper function that handles 429 responses with exponential backoff and jitter
SDK-level retry configuration applied to all OCI client calls
A circuit breaker that prevents cascading failures during bulk operations
Proactive rate limiting for batch processing scripts
Error Handling
Error
Code
Cause
Solution
TooManyRequests
429
API rate limit exceeded (no Retry-After header)
Use exponential backoff with jitter — start at 1s, max 30s
After implementing rate limit handling, see oraclecloud-security-basics for IAM policy patterns, or oraclecloud-observability for monitoring your API call patterns and error rates.
