diff options
| author | Xiangrui Meng <meng@databricks.com> | 2014-11-03 22:29:48 -0800 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2014-11-03 22:31:43 -0800 |
| commit | 8395e8fbdf23bef286ec68a4bbadcc448b504c2c (patch) | |
| tree | 8bea8ca2bba38d861a8e428b9e295bb8782d8d85 /mllib/pom.xml | |
| parent | 42d02db86cd973cf31ceeede0c5a723238bbe746 (diff) | |
| download | spark-8395e8fbdf23bef286ec68a4bbadcc448b504c2c.tar.gz spark-8395e8fbdf23bef286ec68a4bbadcc448b504c2c.tar.bz2 spark-8395e8fbdf23bef286ec68a4bbadcc448b504c2c.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
(cherry picked from commit 1a9c6cddadebdc53d083ac3e0da276ce979b5d1f)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
Diffstat (limited to 'mllib/pom.xml')
| -rw-r--r-- | mllib/pom.xml | 5 |
1 files changed, 5 insertions, 0 deletions
diff --git a/mllib/pom.xml b/mllib/pom.xml index fb7239e779..87a7ddaba9 100644 --- a/mllib/pom.xml +++ b/mllib/pom.xml @@ -46,6 +46,11 @@ <version>${project.version}</version> </dependency> <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-sql_${scala.binary.version}</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> <groupId>org.eclipse.jetty</groupId> <artifactId>jetty-server</artifactId> </dependency> |