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Posted to issues@spark.apache.org by "Hollin Wilkins (JIRA)" <ji...@apache.org> on 2015/07/16 01:12:04 UTC
[jira] [Updated] (SPARK-9084) Add in support for realtime data
predictions using ML PipelineModel
[ https://issues.apache.org/jira/browse/SPARK-9084?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hollin Wilkins updated SPARK-9084:
----------------------------------
Description:
Currently ML provides excellent support for feature manipulation, model selection, and prediction for large datasets. The models can all be easily serialized but currently it is not possible to use the fitted models without a DataFrame. This means that these models are only good for batch processing. In order to support realtime ML pipelines, I propose adding in three new methods to the Transformer class:
def transform(row: StructuredRow): StructuredRow
def transform(row: StructuredRow, paramMap: ParamMap): StructuredRow
def transform(row: StructuredRow, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): StructuredRow
Where a StructuredRow is a case class that is the combination of an org.apache.spark.sql.Row and an org.apache.spark.sql.types.StructType. An alternative would be to modify the transform method signature to take in two objects, a StructType and a Row.
This change necessitates the addition of the new transform method to each implementor of the Transformer class.
Following this change, it would be trivial to include the spark jars in an API server, deserialize an ML PipelineModel object, take incoming data from users, convert it into a StructuredRow and feed it into the PipelineModel to get a realtime result.
was:
Currently ML provides excellent support for feature manipulation, model selection, and prediction for large datasets. The models can all be easily serialized but currently it is not possible to use the fitted models without a DataFrame. This means that these models are only good for batch processing. In order to support realtime ML pipelines, I propose adding in three new methods to the Transformer class:
def transform(row: StructuredRow): StructuredRow
def transform(row: StructuredRow, paramMap: ParamMap): StructuredRow
def transform(row: StructuredRow, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): StructuredRow
Where a StructuredRow is a case class that is the combination of an org.apache.spark.sql.Row and an org.apache.spark.sql.types.StructType
This change necessitates the addition of the new transform method to each implementor of the Transformer class.
Following this change, it would be trivial to include the spark jars in an API server, deserialize an ML PipelineModel object, take incoming data from users, convert it into a StructuredRow and feed it into the PipelineModel to get a realtime result.
> Add in support for realtime data predictions using ML PipelineModel
> -------------------------------------------------------------------
>
> Key: SPARK-9084
> URL: https://issues.apache.org/jira/browse/SPARK-9084
> Project: Spark
> Issue Type: New Feature
> Components: Spark Core
> Reporter: Hollin Wilkins
> Priority: Critical
>
> Currently ML provides excellent support for feature manipulation, model selection, and prediction for large datasets. The models can all be easily serialized but currently it is not possible to use the fitted models without a DataFrame. This means that these models are only good for batch processing. In order to support realtime ML pipelines, I propose adding in three new methods to the Transformer class:
> def transform(row: StructuredRow): StructuredRow
> def transform(row: StructuredRow, paramMap: ParamMap): StructuredRow
> def transform(row: StructuredRow, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): StructuredRow
> Where a StructuredRow is a case class that is the combination of an org.apache.spark.sql.Row and an org.apache.spark.sql.types.StructType. An alternative would be to modify the transform method signature to take in two objects, a StructType and a Row.
> This change necessitates the addition of the new transform method to each implementor of the Transformer class.
> Following this change, it would be trivial to include the spark jars in an API server, deserialize an ML PipelineModel object, take incoming data from users, convert it into a StructuredRow and feed it into the PipelineModel to get a realtime result.
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