@nguyenthuymo
Library import failures in Databricks notebooks that resolve after cluster restarts are a common challenge,
especially for production workloads that need to run reliably at scheduled times.
Common Root Causes
1. Library Installation State Inconsistency
- Libraries installed during a session might not properly persist across notebook executions
- Cluster-installed vs. notebook-installed library conflicts
2. Dependency Conflicts
- Multiple versions of the same library in different initialization paths
- Conflicting dependencies between libraries
3. Cluster Resource Issues
- Memory pressure causing Python interpreter issues
- JVM memory allocation problems affecting PySpark libraries
4. Init Script Timing Issues
- Race conditions in cluster startup scripts
- Library installation order dependencies
5. Library Caching Problems
- Corrupted library cache
- Stale library metadata
Preventative Solutions for Production
1. Use Cluster Libraries (Recommended)
Instead of notebook-level installs, install libraries at the cluster level:
- Go to your cluster configuration
- Navigate to the "Libraries" tab
- Add the required libraries (PyPI, Maven, etc.)
- Restart the cluster once
2. Implement Robust Error Handling
Add retry logic for imports:
# At the top of your notebook
import time
max_attempts = 3
attempt = 0
while attempt < max_attempts:
try:
# Your imports
import problematic_library
break # Success - exit the retry loop
except ImportError as e:
attempt += 1
if attempt >= max_attempts:
# Log clearly and raise
error_msg = f"Failed to import after {max_attempts} attempts: {str(e)}"
dbutils.notebook.exit({"status": "FAILED", "error": error_msg})
print(f"Import attempt {attempt} failed, retrying in 10 seconds...")
time.sleep(10)
3. Use Init Scripts for Guaranteed Installation
Create a cluster init script:
#!/bin/bash
/databricks/python/bin/pip install your_library==x.y.z
Add this to your cluster's init scripts in the advanced options.
4. Consider Using Job Clusters
For scheduled production jobs:
- Create a job-specific cluster configuration
- Set "Terminate after" to a value slightly longer than your job's typical runtime
- This ensures a fresh environment for each job run
5. Use Requirements.txt for Deterministic Builds
Create a requirements.txt with exact versions and install at notebook start:
# First cell of notebook
%pip install -r /dbfs/path/to/requirements.txt
By implementing these preventative measures, particularly using cluster-level library installation,
you should be able to make your 5am production job more reliable.
LR
