Are you working with Databricks and facing issues when trying to check the existence of a path? Do you encounter exceptions when reading CSV files from a data lake store due to non-existent paths? If so, we’ve got you covered. In this blog post, we’ll explore different approaches to handle this issue and avoid exceptions, ensuring smooth execution of your code.
Understanding the Problem
When working with multiple paths to read CSV files, it can be challenging to handle scenarios where one or more paths do not exist. By default, if any of the specified paths is not found, Databricks throws a “path does not exist” exception, causing disruption in your workflow. We’ll discuss alternatives to tackle this problem effectively.
Option 1: Filtering Relevant Subdirectories
One approach to mitigate this issue is to filter out the relevant subdirectories before reading the data. Let’s consider an example where you want to read subdirectories and select specific ones to include in your processing.
First, you can list all subfolders and files in a directory using the dbutils.fs.ls() function. Then, you can filter out the relevant subdirectories based on your criteria, such as containing specific keywords or having a specific structure.
Here’s an example code snippet to achieve this:
code
dir = dbutils.fs.ls("/mnt/adls2/demo") # List all subfolders and files in the directory
pathes = ''
for i in range(0, len(dir)):
subpath = dir[i]
.path
if '/corr' in subpath or '/deci' in subpath and subpath.startswith('dbfs:/'):
pathes = pathes + (dir[i]
.path) + ' '
# Convert the string to a list
pathes = list(pathes.split())
After filtering out the relevant subdirectories, you can use the resulting list to read the dataframe.
code
df = spark.read.json(pathes)
This approach allows you to skip non-existent paths and focus only on the subdirectories that meet your criteria.
Option 2: Using Hadoop Library
Another approach involves using the Hadoop library to check the existence of a path. This method can be particularly useful when dealing with glob paths that contain wildcard characters.
Here’s an example of how you can leverage the Hadoop library to check if a specific path exists in Scala:
code
val exists = dbutils.fs.ls("/mnt").map(_.name).contains(s"$path/")
By utilizing the dbutils.fs.ls() function and checking for the presence of the desired path, you can determine whether it exists or not. This approach provides flexibility when dealing with complex path patterns.
Conclusion
In this blog post, we explored different approaches to check whether a path exists or not in Databricks. By filtering relevant subdirectories or utilizing the Hadoop library, you can avoid exceptions and handle non-existent paths gracefully. Implementing these techniques will help ensure a smooth execution of your code and improve the overall reliability of your data processing pipelines.
Remember to adapt these approaches based on your specific requirements and the structure of your data. By effectively handling path existence checks, you can streamline your workflows and enhance the efficiency of your Databricks projects.
