How well does R work in Synapse Analytics Notebooks?

Hello,

Thank you for your question regarding the compatibility of existing base R scripts with the Spark environment in Synapse Notebooks. This is a common point of clarification for those transitioning R code to a big data platform.

Executive Summary

While Synapse Notebooks with the %%sparkR magic command do provide an R runtime, existing base R scripts are not designed to run as-is on large, distributed datasets within this environment. Base R functions like read.csv() and dplyr are optimized for single-node, in-memory processing. For scripts to scale and leverage Spark's distributed computing power for large datasets, they must be adapted to use Spark-specific R packages like SparkR or sparklyr. 

https://free.blessedness.top/en-us/azure/synapse-analytics/spark/apache-spark-r-language

https://free.blessedness.top/en-us/fabric/data-science/r-overview

Detailed Explanation

To effectively use your R scripts in a Synapse Spark environment, you will need to consider the following:

1. Distributed vs. In-Memory Processing

• Base R: Functions like read.csv() load data into a single, local R data frame. This is efficient for small-to-medium sized datasets but will cause performance bottlenecks or memory errors when dealing with big data.
• SparkR: To process data in a distributed manner, you must use Spark-native functions. For example, use read.df() to load a CSV file from a distributed storage system (like ADLS Gen2) into a Spark data frame. Spark then automatically partitions and distributes this data across the cluster. 

2. Using Tidyverse Packages (dplyr, ggplot2)

• Small Data: You can use popular R packages like dplyr and ggplot2, which come pre-installed, but you should only apply them to smaller, local R data frames.
• Large Data: To use dplyr with large datasets, you must first connect to the Spark cluster using the sparklyr package. sparklyr translates your familiar dplyr commands into Spark SQL, which is then executed by the distributed engine. You can then use collect() to bring a smaller subset of the processed data into a local R data frame for visualization with ggplot2. 

3. Rewriting Scripts for Synapse Spark

For your existing scripts, the migration process will involve a few key steps:

• Convert Data Loading: Replace read.csv() with read.df() (for SparkR) or spark_read_csv() (for sparklyr).
• Adapt Transformations: Rewrite data manipulation logic (e.g., dplyr chains) to operate on Spark data frames using the sparklyr syntax.
• Final Data Collection: Only collect() the final, aggregated results from the Spark data frame into a local R data frame for any local processing or plotting. 

In summary, it is not possible to run existing base R scripts directly on big data within a Synapse Notebook's Spark context. The architecture requires adaptation to the Spark-specific R packages (SparkR or sparklyr) to handle large datasets effectively and to leverage the full power of the distributed compute engine.

Regards,

Raviteja M.