title: Conversion incompatibility between timestamp type in Glue and in Spark?
body: <p>I want to run a simple sql select of timestamp fields from my data using <strong>spark sql (pyspark)</strong>.
  However, all the timestamp fields appear as 1970-01-19 10:45:37.009 .
  So looks like I have some <strong>conversion incompatibility between timestamp in Glue and in Spark</strong>.</p>
  <p>I'm running with pyspark, and I have the glue catalog configuration so I get my database schema from Glue. In both Glue and the spark sql dataframe these columns appear with timestamp type.</p>
  <p>However, it looks like when I read the parquet files from s3 path, the event_time column (for example) is of type long and when I get its data, I get a correct event_time as epoch in milliseconds = <strong>1593938489000</strong>. So I can convert it and get the actual datetime.</p>
  <p>But when I run spark.sql , the event_time column gets timestamp type but it isn’t useful and missing precision. So I get this = <strong>1970-01-19 10:45:37.009</strong> .
  When I run the same sql query in Athena, the timestamp field looks fine so my schema in Glue looks correct.</p>
  <p>Is there a way to overcome it?
  I didn't manage to find any spark.sql configurations that solved it.</p>