Resolving the UNNEST and SPLIT Function Error in PySpark SQL

This guide delves into solving the `AnalysisException` error encountered while using the `UNNEST` and `SPLIT` functions in PySpark SQL within AWS Glue. Learn how to effectively modify your queries for seamless execution.
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Understanding the Problem

If you've worked with PySpark SQL in AWS Glue, you might have encountered unexpected errors while executing queries that work seamlessly in other systems like Presto or Athena. A common issue that many users face is the AnalysisException indicating that a column does not exist when it clearly does.

The Challenge

In this specific case, a user was trying to utilize a combination of the UNNEST and SPLIT functions while processing a dataframe, but encountered the error:

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

This error occurred when employing a cross join operation. It was functional in Athena, but failed when executed in AWS Glue.

The Original Query

Here's a brief look at the original SQL query that led to the confusion:

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

The task at hand involved splitting and processing a metric value represented as an array, but the cross join was throwing off the execution.

The Solution: Switching from UNNEST to EXPLODE

To resolve the issue, there was a necessary shift in strategy. Instead of using a cross join with UNNEST, we replaced it with the EXPLODE function directly, enabling a more straightforward manipulation of the data.

Revised Code Structure

Step 1: Modify the Initial Dataframe

Replace the entire query that caused the error with the following:

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

Step 2: Creating Temporary Views

You still retain the ability to create a temporary view for your dataframe, which allows you to manipulate it in SQL queries:

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

Step 3: Revised SQL Query for Aggregation

The subsequent aggregation query can remain similar but should reference raw_value instead of split_part:

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

Expected Outcomes

After making these adjustments, your query should run smoothly without errors, yielding results akin to the following:

Load_dateLoad_hrtimestampne_nameobjectmetric_valueMin_UTILMAX_UTILAVG_UTIL2023-08-10152023-08-10T09:45AP1AP1.12.5{14311, 242342134, 13132}3.429.115.8Conclusion

By transitioning from UNNEST to EXPLODE, you can effectively sidestep the AnalysisException encountered in AWS Glue, permitting your SQL queries to execute as expected. This innovative approach not only resolves the error but also enhances the performance and simplicity of data manipulation in PySpark SQL.

If you continue to encounter challenges, experimenting with these functions in breakpoints might illuminate other potential adjustments for future data processing tasks.