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Discover how to convert long epoch timestamps in milliseconds to TimestampType in PySpark using native Spark functions without relying on Python libraries.
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Converting Long Epoch to TimestampType in PySpark

When working with time data in PySpark, you may encounter timestamps represented as long epoch values in milliseconds. For instance, the timestamp 1541106106796 corresponds to Thursday, November 1, 2018 9:01:46.796 PM. If you want to convert these epoch milliseconds to a Spark-compatible TimestampType, you're in the right place. This guide will guide you through the process of achieving this solely using native Spark functions.

The Problem

You have a dataset with a date field in the format of long epoch milliseconds and need to convert this into a readable timestamp format. Traditionally, one might think of using Python's datetime library to achieve this conversion, but in this post, we'll focus solely on using native Spark functions, which allows for better performance and integration within the Spark ecosystem.

The Solution

To convert long epoch milliseconds to TimestampType, you can use the combination of the from_unixtime function along with string manipulation functions in Spark SQL. This method efficiently extracts the timestamp and handles the milliseconds correctly.

Step-by-Step Breakdown

Extract the Whole Second Portion: Use the substring function to remove the last three digits of the epoch millisecond timestamp, which represent the milliseconds themselves.

Convert to Timestamp: Pass this modified epoch value into the from_unixtime function to convert it to a standard timestamp format.

Add Milliseconds Back: Concatenate the milliseconds back to the timestamp string and cast it to a timestamp type.

Example Code

Here is how you can implement the conversion in PySpark:

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

Additional Example

If you need to work with a timestamp that is already in a readable string format, you can use the unix_timestamp along with regular expression extraction:

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

Conclusion

Using native Spark functions for converting long epoch milliseconds to TimestampType is not only an efficient way to process time data in your applications but also keeps your workflow within the Spark ecosystem. This approach avoids the need for additional Python libraries, enhancing performance when handling large datasets.

Next time you need to convert your epoch timestamps, try out this method and enjoy the seamless integration with your PySpark data processing tasks!