Introduction

Dependency conflicts in Databricks notebooks can derail workflows, causing cryptic errors like ImportError or NoSuchMethodException. These issues arise when different libraries or versions clash across notebooks, jobs, or clusters. In this guide, we’ll explore why dependency conflicts happen, how to diagnose them, and proven strategies to keep your environments stable.

Common Causes of Dependency Conflicts

1. Version Mismatches:
   • Notebooks requiring different versions of the same library (e.g., pandas==1.5.0 vs. pandas==2.0.0).
2. Shared Cluster Environments:
   • Multiple teams using the same cluster with conflicting library requirements.
3. Implicit Dependencies:
   • Libraries with hidden dependencies (e.g., tensorflow pulling specific numpy versions).
4. Language Mixing:
   • Scala/Python/JAR files conflicting on a single cluster.

How to Detect Dependency Conflicts

1. Error Messages:
   • ModuleNotFoundError, ImportError, or ClassNotFoundException.
   • Warnings like “Version X of library Y conflicts with version Z.”
2. Cluster Logs:
   • Check driver/executor logs for failed imports or version mismatches.
3. Spark UI:
   • Inspect Environment tab for loaded libraries and versions.

Solutions to Fix Dependency Conflicts

1. Use Notebook-Scoped Libraries

Install libraries locally to a notebook session without affecting the cluster:

%pip install --user pandas==1.5.0  # Installs only for this notebook  

Pros: Isolates dependencies per notebook.
Cons: Not ideal for production jobs.

2. Cluster-Level Dependency Isolation

• Dedicated Clusters: Assign clusters to teams/projects with tailored libraries.
• Cluster Libraries: Attach version-pinned libraries via the UI or API:

# Using Databricks CLI  
databricks libraries install --cluster-id <ID> --pypi-package "scikit-learn==1.2.0"  

3. Init Scripts for Environment Control

Bootstrap clusters with consistent dependencies using init scripts:

#!/bin/bash  
/databricks/python/bin/pip install "numpy==1.23.5"  

Best Practices:

• Store scripts in DBFS.
• Test scripts on a single node first.

4. Virtual Environments (Conda)

Create isolated Python environments:

%sh  
conda create -n myenv python=3.8  
conda activate myenv  
pip install -r requirements.txt  

Note: Requires init scripts to persist across cluster restarts.

5. Dependency Management Tools

• Pipenv/Poetry: Generate Pipfile.lock or poetry.lock for reproducible builds.
• Wheel Files: Pre-build wheels for complex dependencies and upload to DBFS.

Best Practices to Avoid Conflicts

1. Pin Versions Explicitly:
   • Use requirements.txt or conda.yml with exact versions.
2. Test in Isolation:
   • Validate dependencies on a single-node cluster before scaling.
3. Leverage Job Clusters:
   • Run production jobs on isolated clusters with fixed libraries.
4. Monitor Dependencies:
   • Use tools like pipdeptree to visualize conflicts:

%pip install pipdeptree  
%pipdeptree  

Real-World Example: Resolving a PySpark/Pandas Conflict

Scenario: A notebook failed with AttributeError: 'DataFrame' object has no attribute 'iteritems' due to pandas==2.0.0 breaking PySpark compatibility.

Fix:

1. Identified conflicting versions via %pip list.
2. Created a notebook-scoped environment:

%pip install --user pandas==1.5.0  
dbutils.library.restartPython()  # Restart Python context  

3. Result: Notebook executed successfully without affecting other users.

Advanced: Managing Scala/JAR Conflicts

• Maven Coordinates: Specify exact versions for Scala libraries:

com.typesafe:config:1.4.2  

• Fat JARs: Bundle dependencies into a single JAR using sbt/shadow.

Conclusion

Dependency conflicts are inevitable in collaborative Databricks environments, but they’re manageable with isolation, version control, and proactive testing. By adopting notebook-scoped installs, cluster policies, and robust dependency tracking, teams can minimize downtime and maintain smooth workflows.