Скачать или смотреть Solving AnalysisException: Path Does Not Exist in Databricks for Python Code

Discover how to fix the `AnalysisException` issue when running Python code on Databricks by ensuring your data is stored in DBFS. Learn how to copy files correctly into your Databricks environment.
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This video is based on the question https://stackoverflow.com/q/68202341/ asked by the user 'user3868051' ( https://stackoverflow.com/u/3868051/ ) and on the answer https://stackoverflow.com/a/68205154/ provided by the user 'Alex Ott' ( https://stackoverflow.com/u/18627/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Solving AnalysisException: Path Does Not Exist in Databricks for Python Code

When working with Python code in Databricks, you may encounter an error that can be quite frustrating: AnalysisException: Path does not exist. This error typically indicates that your code is trying to access a file or directory that the Databricks environment cannot find. In this guide, we’ll dive deeper into a common scenario that leads to this issue and provide you with a clear, structured solution to ensure that your Python code runs smoothly in Databricks.

Understanding the Problem

The issue arises when you package your Python code into a whl file and then attempt to run it on the Databricks File System (DBFS). In our example, a function is designed to read parquet files from a specified directory:

[[See Video to Reveal this Text or Code Snippet]]

When this code is executed locally, it works perfectly, but an error is triggered when it is run in Databricks:

[[See Video to Reveal this Text or Code Snippet]]

Upon investigating, it's clear that the directory dbfs:/databricks/python does not exist in the Databricks environment, leading to the failure.

The Root Cause

The core issue here lies in the way the path is constructed. By default, Spark on Databricks operates using the DBFS unless specified otherwise. The function path_to_model returns a string pointing to a local path within your Python environment:

[[See Video to Reveal this Text or Code Snippet]]

Since this path does not exist in the DBFS, Spark cannot locate the files, resulting in the error.

Implementing the Solution

To solve the problem, we need to ensure that the data files are copied to a path that Databricks can access—specifically within the DBFS. Below are the step-by-step instructions to adjust your code.

Step 1: Copy the Data to DBFS

Modify your read_data function to utilize the dbutils.fs.cp command. This command copies files from a local path to a temporary directory in DBFS. Here's how the updated code looks:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Explanation of Key Components

data_path: This variable holds the path to the data that the path_to_model function retrieves.

tmp_path: This is a temporary location in DBFS where your data will be copied.

dbutils.fs.cp: This function is used to copy files from the local environment into DBFS. The "file:" prefix indicates that you are dealing with a local file path.

Step 3: Running Your Code

After implementing the changes, re-run your Databricks notebook. You should no longer see the AnalysisException regarding the path, as the data will be correctly copied to a location that Spark can access.

Conclusion

By understanding how paths work in the Databricks environment and ensuring your files are in the appropriate location, you can avoid common pitfalls such as the AnalysisException. Always remember to confirm that data needed by your Spark jobs is properly copied to DBFS to ensure smooth operation.

If you follow the steps outlined in this post, you’ll be well-equipped to tackle similar issues that arise in your data workflows on Databricks. Happy coding!