spark-reviews mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From mengxr <>
Subject [GitHub] spark pull request: [SPARK-3573][MLLIB] Make MLlib's Vector compat...
Date Mon, 03 Nov 2014 18:52:01 GMT
Github user mengxr commented on a diff in the pull request:
    --- Diff: mllib/pom.xml ---
    @@ -46,6 +46,11 @@
    +      <groupId>org.apache.spark</groupId>
    +      <artifactId>spark-sql_${scala.binary.version}</artifactId>
    --- End diff --
    @srowen Yes, it feels weird if we say ML depends on SQL, the "query language". Spark SQL
provides RDD with schema support and execution plan optimization, both of which are need by
MLlib. We need flexible table-like datasets and I/O support, and operations that "carry over"
additional columns during the training phrase. It is natural to say that ML depends on RDD
with schema support and execution plan optimization.
    I agree that we should factor the common part out or make SchemaRDD a first-class citizen
in Core, but that definitely takes time for both design and development. This dependence change
has no effect on the content we deliver to users, and UDTs are internal to Spark.

If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at or file a JIRA ticket
with INFRA.

To unsubscribe, e-mail:
For additional commands, e-mail:

View raw message