Understanding the Differences Between DataFrames Created with SparkR and Sparklyr

Описание: Explore the key distinctions between SparkR and Sparklyr DataFrames, and learn how to convert them when working with parquet files in Azure Databricks.
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Understanding the Differences Between DataFrames Created with SparkR and Sparklyr

When working with big data in Azure Databricks, you may encounter scenarios where you need to read parquet files and work with DataFrames. If you're using R, two popular tools at your disposal are SparkR and Sparklyr. However, you might notice that DataFrames created by these two libraries are quite different. In this guide, we'll break down the differences between these DataFrames and explore ways to convert them if needed.

The Problem at Hand

Let’s go through the situation. You're reading a parquet file in Azure Databricks using two different methods:

Using SparkR: read.parquet()

Using Sparklyr: spark_read_parquet()

In both cases, the DataFrames produced are incompatible, leading to confusion about how to work with them effectively. This brings up an important question: What are the differences between DataFrames created using SparkR and those created using Sparklyr?

Differences Between SparkR and Sparklyr DataFrames

Understanding the distinctions between these two approaches is crucial for effective data manipulation and analysis. Below, we outline the key differences:

1. DataFrame Type

SparkR DataFrame:

Created using SparkR, it produces a SparkDataFrame.

This is primarily a collection of data organized using a query plan.

Sparklyr DataFrame:

Generates a tbl_spark using Sparklyr.

Essentially, this is a lazy evaluation query translated into Spark SQL.

2. Functionality

SparkDataFrame:

More aligned with the traditional R data frame structures.

Allows various operations directly on the data it represents.

tbl_spark:

Doesn’t behave like a traditional data frame. Instead, it treats operations as queries that only execute upon explicitly asking for data.

3. Usability

You may find it challenging to apply general R functions directly on a tbl_spark, as it requires different handling.

Conversely, you can't directly treat a SparkDataFrame like a standard R data frame.

Converting Between DataFrames

If you find yourself needing to switch between these two types of DataFrames, there might be a solution. While there is no direct method for conversion, you can utilize the following workflow:

Suggested Workaround

Write to Data Lake/Data Warehouse:

Write your SparkR SparkDataFrame to your data lake or data warehouse.

After that, read it back into Sparklyr as a tbl_spark.

CSV Option:

Alternatively, you can export the DataFrame to a CSV format and then read it back into R using the other library.

Initial Loading in R:

You may consider loading the data into a standard R data frame first, and subsequently convert or manipulate it as needed.

Conclusion

Understanding how SparkR and Sparklyr differ in their DataFrame structures is essential for effective data handling in big data environments like Azure Databricks. While they serve similar purposes in enabling data analysis and manipulation in R, their foundational differences mean you should be aware of how to convert between them when needed. By following the provided workarounds, you can effectively switch between DataFrame types and continue your analysis without a hitch.

If you have any questions or need further clarification, feel free to reach out in the comments below!