Cheat sheet: Converting Spark SQL to AWS Athena SQL |
add Data Collection and Data Pipeline Related Tutorials
| Published Jul 24 '24. Last edited Dec 19 '24 |
For Data Engineers trying to debug #spark data pipelines using #AWS #Athena, the little check list below will very likely save 30 minutes to one hour of your effort.
For Data Engineers working with Spark data pipelines on AWS, it's a quite common task for convert Spark SQL to AWS Athena SQL because a common scenario is Spark SQL errors out while running Spark job on a cluster, at that point, next step developers take is to test run the Spark SQL statement in AWS Athena to debug it. AWS Athena is AWS wrapper of open-source Presto SQL execution engine. However, converting Spark SQL to Athena SQL is not straight-forward, as there are a number of syntaxes that are specific to AWS Athena SQL and Spark SQL respectively, a successful conversion will require developers to
Here is a list of these syntax differences (this list is work in progress, welcome to comment to add or contribute to it)
| Intent | AWS Athena SQL | Databricks Spark SQL |
|---|---|---|
| string replacement | replace |
regexp_replace |
| string trimming spaces | LTRIM and RTRIM |
trim |
| get substring | substr |
substring |
| value of a certain date | date_parse('yyyy-mm-dd', '%Y-%m-%d') |
'yyyy-mm-dd' |
| value of today's date | current_date |
current_date() |
| map function | map_agg (need GROUP BY at end of SQL query, reference1 and reference2 on StackOverflow) |
map (no GROUP BY is needed at end of query, function reference on Databricks) |
escaping column name with . (dot) |
using double quotes, e.g. "col.name" |
using backtick symbol, e.g. `col.name` (reference on StackOverflow) |
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