palantir-performance-tuning

name	palantir-performance-tuning
description	Optimize Palantir Foundry API performance with caching, batching, and pagination. Use when experiencing slow API responses, optimizing transform builds, or improving request throughput for Foundry integrations. Trigger with phrases like "palantir performance", "optimize foundry", "foundry slow", "palantir caching", "foundry batch".
allowed-tools	Read, Write, Edit
version	1.5.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","palantir","foundry","performance","optimization"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

Palantir Performance Tuning

Overview

Optimize Foundry API performance: efficient pagination, client-side caching, batch object retrieval, and Spark transform tuning with @configure profiles.

Prerequisites

• Completed palantir-install-auth setup
• Working Foundry integration to optimize
• Access to Foundry build metrics (for transform tuning)

Instructions

Step 1: Efficient Pagination

from functools import lru_cache

def fetch_all_objects(client, ontology: str, object_type: str, page_size: int = 500):
    """Fetch all objects with maximum page size to minimize API calls."""
    all_objects = []
    page_token = None
    while True:
        result = client.ontologies.OntologyObject.list(
            ontology=ontology,
            object_type=object_type,
            page_size=min(page_size, 500),  # Foundry max is 500
            page_token=page_token,
        )
        all_objects.extend(result.data)
        page_token = result.next_page_token
        if not page_token:
            break
    return all_objects

Step 2: Client-Side Caching

from cachetools import TTLCache
import hashlib, json

_cache = TTLCache(maxsize=1000, ttl=300)  # 5-minute TTL

def cached_get_object(client, ontology, object_type, primary_key):
    """Cache Ontology object reads to reduce API calls."""
    cache_key = f"::"
     cache_key  _cache:
         _cache[cache_key]
    obj = client.ontologies.OntologyObject.get(
        ontology=ontology, object_type=object_type, primary_key=primary_key,
    )
    _cache[cache_key] = obj
     obj

 ():
    cache_key = 
    _cache.pop(cache_key, )