Imported from jeremylongshore/tons-of-skills-marketplace (plugins/saas-packs/oraclecloud-pack/skills/oraclecloud-rate-limits/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill oraclecloud-rate-limits. Copyright stays with the author (MIT).

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:

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")

def call_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 in range(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 retryable
    raise 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

class OCICircuitBreaker:
    """Circuit breaker for bulk OCI API operations."""

    def __init__(self, failure_threshold=5, reset_timeout=60):
        self.failure_count = 0
        self.failure_threshold = failure_threshold
        self.reset_timeout = reset_timeout
        self.last_failure_time = 0
        self.state = "closed"  # closed = normal, open = blocking

    def call(self, fn, max_retries=3, base_delay=1.0):
        if self.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)
            if self.state == "half-open":
                self.state = "closed"
                self.failure_count = 0
                print("Circuit closed — resuming normal operation")
            return result
        except Exception:
            self.failure_count += 1
            self.last_failure_time = time.time()
            if self.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)

def batch_with_throttle(items, operation, requests_per_second=5):
    """Process items with proactive rate limiting."""
    delay = 1.0 / requests_per_second
    results = []

    for i, item in enumerate(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

Examples

Quick 429 test — intentionally trigger rate limiting:

import oci

config = oci.config.from_file("~/.oci/config")
compute = oci.core.ComputeClient(config)

# Rapid-fire requests to observe throttling behavior
for i in range(100):
    try:
        compute.list_instances(compartment_id="ocid1.compartment.oc1..example")
    except oci.exceptions.ServiceError as e:
        if e.status == 429:
            print(f"Throttled at request {i + 1}")
            break

Check current OCI SDK retry defaults:

import oci
print(f"SDK version: {oci.__version__}")
# Default retry: NoneRetryStrategy (no retries unless configured)
# Always set explicit retry strategies for production code

Resources

• OCI API Request Limits — official rate limit documentation
• Python SDK Retry Configuration — RetryStrategyBuilder reference
• SDK Troubleshooting — common SDK errors
• OCI Known Issues — known service issues
• OCI Status — service health dashboard

Next Steps

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.