Databricks File System (DBFS) is a distributed file system that is used to store data for Databricks clusters. DBFS is accessible from all nodes in a cluster, and it provides a consistent view of the data, regardless of the location of the nodes. This makes it easy to share data between different jobs and users.

DBFS is also integrated with the Databricks platform, which makes it easy to use DBFS data in Databricks notebooks and jobs. For example, you can use DBFS to load data into a DataFrame, or you can use DBFS to store the results of a job.

Using DBFS in Azure Databricks

In Azure Databricks, DBFS is mounted at the /dbfs directory. This means that you can access DBFS files by using the dbutils.fs module. For example, the following code loads a file from DBFS into a DataFrame:

df = spark.read.format("csv").load("/dbfs/my_data.csv")

You can also use DBFS to store the results of a job. For example, the following code saves a DataFrame to DBFS:

df.write.format("csv").save("/dbfs/my_data.csv")

Using DBFS in Databricks Runtime for ML

In Databricks Runtime for ML, DBFS is mounted at the /mnt/dbfs directory. This means that you can access DBFS files by using the file: URI scheme. For example, the following code loads a file from DBFS into a DataFrame:

df = spark.read.format("csv").load("file:/mnt/dbfs/my_data.csv")

You can also use DBFS to store the results of a job. For example, the following code saves a DataFrame to DBFS:

df.write.format("csv").save("file:/mnt/dbfs/my_data.csv")

Benefits of Using DBFS

There are many benefits to using DBFS, including:

• Centralized storage: DBFS provides a centralized location for storing data for Databricks clusters. This makes it easy to share data between different jobs and users.
• Consistent view of data: DBFS provides a consistent view of the data, regardless of the location of the nodes in a cluster. This makes it easy to access data from different nodes in a cluster.
• Integration with the Databricks platform: DBFS is integrated with the Databricks platform, which makes it easy to use DBFS data in Databricks notebooks and jobs.

Conclusion

DBFS is a powerful tool that can be used to improve the performance and usability of Databricks clusters. By using DBFS, you can centralize your data storage, provide a consistent view of your data, and easily integrate your data with the Databricks platform.

Frequently Asked Questions

• What is DBFS?

DBFS is a distributed file system that is used to store data for Databricks clusters.

• Where is DBFS mounted in Azure Databricks?

In Azure Databricks, DBFS is mounted at the /dbfs directory.

• Where is DBFS mounted in Databricks Runtime for ML?

In Databricks Runtime for ML, DBFS is mounted at the /mnt/dbfs directory.

• What are the benefits of using DBFS?

There are many benefits to using DBFS, including centralized storage, consistent view of data, and integration with the Databricks platform.

• How can I use DBFS in Databricks notebooks and jobs?

You can use the dbutils.fs module to access DBFS files in Databricks notebooks and jobs.