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| author | Xiangrui Meng <meng@databricks.com> | 2014-11-03 22:29:48 -0800 |
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| committer | Xiangrui Meng <meng@databricks.com> | 2014-11-03 22:29:48 -0800 |
| commit | 1a9c6cddadebdc53d083ac3e0da276ce979b5d1f (patch) | |
| tree | b485818ba52a9287ae7124e57ef55f1d974f3a1f /streaming | |
| parent | 04450d11548cfb25d4fb77d4a33e3a7cd4254183 (diff) | |
| download | spark-1a9c6cddadebdc53d083ac3e0da276ce979b5d1f.tar.gz spark-1a9c6cddadebdc53d083ac3e0da276ce979b5d1f.tar.bz2 spark-1a9c6cddadebdc53d083ac3e0da276ce979b5d1f.zip | |
[SPARK-3573][MLLIB] Make MLlib's Vector compatible with SQL's SchemaRDD
Register MLlib's Vector as a SQL user-defined type (UDT) in both Scala and Python. With this PR, we can easily map a RDD[LabeledPoint] to a SchemaRDD, and then select columns or save to a Parquet file. Examples in Scala/Python are attached. The Scala code was copied from jkbradley.
~~This PR contains the changes from #3068 . I will rebase after #3068 is merged.~~
marmbrus jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes #3070 from mengxr/SPARK-3573 and squashes the following commits:
3a0b6e5 [Xiangrui Meng] organize imports
236f0a0 [Xiangrui Meng] register vector as UDT and provide dataset examples
Diffstat (limited to 'streaming')
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