palantir-rate-limits | palantir-pack

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palantir-rate-limits | palantir-pack

Palantir Rate Limits

Overview

Handle Foundry API rate limits with exponential backoff, request queuing, and monitoring. Foundry rate limits vary by endpoint and enrollment tier.

Prerequisites

foundry-platform-sdk installed
Understanding of HTTP 429 responses

Instructions

Step 1: Understand Foundry Rate Limits

Foundry rate limits are per-user and per-endpoint. Key limits:

Endpoint Category	Typical Limit	Burst
Ontology reads	100 req/s	200
Ontology writes (Actions)	50 req/s	100
Dataset reads	50 req/s	100
Search queries	20 req/s	50

Rate limit headers returned:

X-RateLimit-Limit — max requests per window
X-RateLimit-Remaining — requests left in window
Retry-After — seconds to wait (on 429)

Step 2: Implement Retry with Backoff (Python)

import time
import random
import foundry

def retry_foundry_call(fn, *args, max_retries=5, base_delay=1.0, **kwargs):
    """Retry Foundry API calls with jittered exponential backoff."""
    for attempt in range(max_retries + 1):
        try:
            return fn(*args, **kwargs)
        except foundry.ApiError as e:
            if attempt == max_retries:
                raise
            if e.status_code not in (429, 500, 502, 503):
                raise  # Non-retryable error
            delay = base_delay * (2 ** attempt) + random.uniform(0, 0.5)
            retry_after = getattr(e, "retry_after", None)
            if retry_after:
                delay = max(delay, float(retry_after))
            print(f"  Retry {attempt+1}/{max_retries} in {delay:.1f}s (HTTP {e.status_code})")
            time.sleep(delay)

# Usage
employees = retry_foundry_call(
    client.ontologies.OntologyObject.list,
    ontology="my-company", object_type="Employee", page_size=100,
)

Step 3: Request Queue for Batch Operations

import asyncio
from collections import deque

class FoundryRateLimiter:
    """Token bucket rate limiter for batch Foundry operations."""
    def __init__(self, max_per_second: int = 50):
        self.max_per_second = max_per_second
        self.tokens = max_per_second
        self._last_refill = time.monotonic()

    def _refill(self):
        now = time.monotonic()
        elapsed = now - self._last_refill
        self.tokens = min(self.max_per_second, self.tokens + elapsed * self.max_per_second)
        self._last_refill = now

    def acquire(self):
        self._refill()
        if self.tokens < 1:
            wait = (1 - self.tokens) / self.max_per_second
            time.sleep(wait)
            self._refill()
        self.tokens -= 1

limiter = FoundryRateLimiter(max_per_second=40)  # 80% of limit

def rate_limited_call(fn, *args, **kwargs):
    limiter.acquire()
    return retry_foundry_call(fn, *args, **kwargs)

Step 4: Batch Operations with Rate Limiting

def batch_update_objects(client, ontology, action_type, items, batch_size=10):
    """Apply actions in rate-limited batches."""
    results = []
    for i in range(0, len(items), batch_size):
        batch = items[i:i+batch_size]
        for item in batch:
            result = rate_limited_call(
                client.ontologies.Action.apply,
                ontology=ontology,
                action_type=action_type,
                parameters=item,
            )
            results.append({"item": item, "status": result.validation})
        print(f"  Processed {min(i+batch_size, len(items))}/{len(items)}")
    return results

Output

Automatic retry on 429/5xx with exponential backoff
Token bucket rate limiter for batch operations
Rate-limited batch processing for bulk updates

Error Handling

HTTP Code	Meaning	Action
429	Rate limited	Wait `Retry-After` seconds, then retry
500	Server error	Retry with backoff
502/503	Gateway error	Retry with backoff
400/403/404	Client error	Do not retry — fix the request

Resources

Next Steps

For security best practices, see palantir-security-basics.