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https://issues.apache.org/jira/browse/SPARK-23693?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-23693:
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Assignee: (was: Apache Spark)
> SQL function uuid()
> -------------------
>
> Key: SPARK-23693
> URL: https://issues.apache.org/jira/browse/SPARK-23693
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.2.1, 2.3.0
> Reporter: Arseniy Tashoyan
> Priority: Minor
>
> Add function uuid() to org.apache.spark.sql.functions that returns
> [Universally Unique
> ID|https://en.wikipedia.org/wiki/Universally_unique_identifier].
> Sometimes it is necessary to uniquely identify each row in a DataFrame.
> Currently the following ways are available:
> * monotonically_increasing_id() function
> * row_number() function over some window
> * convert the DataFrame to RDD and zipWithIndex()
> All these approaches do not work when appending this DataFrame to another
> DataFrame (union). Collisions may occur - two rows in different DataFrames
> may have the same ID. Re-generating IDs on the resulting DataFrame is not an
> option, because some data in some other system may already refer to old IDs.
> The proposed solution is to add new function:
> {code:scala}
> def uuid(): Column
> {code}
> that returns String representation of UUID.
> UUID is represented as a 128-bit number (two long numbers). Such numbers are
> not supported in Scala or Java. In addition, some storage systems do not
> support 128-bit numbers (Parquet's largest numeric type is INT96). This is
> the reason for the uuid() function to return String.
> I already have a simple implementation based on
> [java-uuid-generator|https://github.com/cowtowncoder/java-uuid-generator]. I
> can share it as a PR.
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