Diagnose and fix Palantir Foundry common errors and API exceptions. Use when encountering Foundry errors, debugging failed API calls, or troubleshooting transform build failures. Trigger with phrases like "palantir error", "fix palantir", "foundry not working", "debug foundry", "palantir 401 403".
Quick reference for the top 10 most common Foundry API and transform errors with copy-paste solutions.
foundry-platform-sdk installedfoundry.ApiError: 401 Unauthorized — The provided token is invalid or expired.
Fix:
# Regenerate token in Developer Console
# Settings > Tokens > Generate new personal access token
# Or re-authenticate with OAuth2:
auth = foundry.ConfidentialClientAuth(
client_id=os.environ["FOUNDRY_CLIENT_ID"],
client_secret=os.environ["FOUNDRY_CLIENT_SECRET"],
hostname=os.environ["FOUNDRY_HOSTNAME"],
scopes=["api:read-data"],
)
auth.sign_in_as_service_user() # Gets a fresh token
foundry.ApiError: 403 Forbidden — Missing required scope: api:ontology-read
Fix: Add missing scopes in Developer Console > Your App > Scopes. Common scopes:
api:read-data — read datasetsapi:write-data — write datasetsapi:ontology-read — read Ontology objectsapi:ontology-write — apply actionsfoundry.ApiError: 404 ObjectTypeNotFound — Object type 'employee' not found
Fix: Object type names are camelCase API names, not display names. Check Ontology Manager:
# List all object types to find the correct api_name
for ot in client.ontologies.ObjectType.list(ontology="my-company"):
print(f" {ot.api_name} (display: {ot.display_name})")
foundry.ApiError: 404 DatasetNotFound — Dataset not found or you do not have access
Fix: Verify the dataset RID (right-click dataset in Foundry UI > Copy RID). Ensure your service user has Viewer/Editor role on the project.
pyspark.sql.utils.AnalysisException: cannot resolve 'fullname' given columns [fullName, department]
Fix: Spark column names are case-sensitive. Print columns to debug:
@transform_df(Output("/out"), data=Input("/in"))
def my_transform(data):
print(data.columns) # Check actual column names
return data.select("fullName") # Use exact casing
java.lang.OutOfMemoryError: Java heap space
Fix: Add @configure with a larger memory profile:
from transforms.api import configure
@configure(profile=["DRIVER_MEMORY_LARGE"]) # 16GB
@transform_df(Output("/out"), data=Input("/in"))
def heavy_transform(data):
return data.groupBy("region").agg({"amount": "sum"})
foundry.ApiError: ActionValidationFailed — Parameter 'salary' must be positive
Fix: Read the validation messages for specific constraint violations:
result = client.ontologies.Action.apply(
ontology="my-company",
action_type="updateSalary",
parameters={"employeeId": "EMP-001", "salary": 150000},
)
if result.validation != "VALID":
for msg in result.validation_messages:
print(f" Validation error: {msg}")
requests.exceptions.SSLError: SSL certificate verify failed
Fix: Common behind corporate proxies. Set the CA bundle:
export REQUESTS_CA_BUNDLE=/path/to/corporate-ca-bundle.crt
# Or for development only (NOT production):
export FOUNDRY_SSL_VERIFY=false
foundry.ApiError: 429 Too Many Requests — Rate limit exceeded
Fix: See palantir-rate-limits for full implementation. Quick fix:
import time
time.sleep(int(response.headers.get("Retry-After", 5)))
Build failed: Circular dependency detected between datasets
Fix: Dataset A's transform reads from B, and B reads from A. Break the cycle by introducing an intermediate dataset or restructuring the pipeline DAG.
| HTTP Code | Meaning | Retryable |
|---|---|---|
| 400 | Bad Request (invalid params) | No — fix request |
| 401 | Token expired/invalid | No — re-authenticate |
| 403 | Missing scopes | No — update app scopes |
| 404 | Resource not found | No — fix identifier |
| 429 | Rate limited | Yes — wait and retry |
| 500/502/503 | Server error | Yes — retry with backoff |
For deeper debugging, see palantir-debug-bundle.