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From Pat Ferrel <>
Subject Re: Using Dataframe API vs. RDD API?
Date Tue, 30 Jan 2018 18:33:42 GMT
What template are you using? If it is one of the templates in the Apache repos, you may want
to file a bug report. If PIO supports Spark 2.x, the Apache Templates should also IMHO.

From: Daniel O' Shaughnessy <>
Reply: <>
Date: January 30, 2018 at 9:09:49 AM
To: <>
Subject:  Re: Using Dataframe API vs. RDD API?  

Hi Shane,

You need to use PAlgorithm instead of P2Algorithm and save/load the spark context accordingly.
This way you can use spark context in the predict function.

There are examples of using PAlgorithm on the predictionio Site. It’s slightly more complicated
but not too bad!

On Tue, 30 Jan 2018 at 17:06, Shane Johnson <> wrote:
Thanks team! We are close to having our models working with the Dataframe API. One additional
roadblock we are hitting is the fundamental difference in the RDD based API vs the Dataframe
API. It seems that the old mllib API would allow a simple vector to get predictions where
in the new ml API a dataframe is required. This presents a challenge as the predict function
in PredictionIO does not have a spark context. 

Any ideas how to overcome this? Am I thinking through this correctly or are there other ways
to get predictions with the new ml Dataframe API without having a dataframe as input?



Shane Johnson | 801.360.3350

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2018-01-08 20:37 GMT-10:00 Donald Szeto <>:
We do have work-in-progress for DataFrame API tracked at

Chan, it would be nice if you could create a branch on your personal fork if you want to hand
it off to someone else. Thanks!

On Fri, Jan 5, 2018 at 2:02 PM, Pat Ferrel <> wrote:
Yes and I do not recommend that because the EventServer schema is not a developer contract.
It may change at any time. Use the conversion method and go through the PIO API to get the
RDD then convert to DF for now.

I’m not sure what PIO uses to get an RDD from Postgres but if they do not use something
like the lib you mention, a PR would be nice. Also if you have an interest in adding the DF
APIs to the EventServer contributions are encouraged. Committers will give some guidance I’m
sure—once that know more than me on the subject.

If you want to donate some DF code, create a Jira and we’ll easily find a mentor to make
suggestions. There are many benefits to this including not having to support a fork of PIO
through subsequent versions. Also others are interested in this too.


On Jan 5, 2018, at 7:39 AM, Daniel O' Shaughnessy <> wrote:

....Should have mentioned that I used  
org.apache.spark.rdd.JdbcRDD to read in the RDD from a postgres DB initially.

This was you don't need to use an EventServer!

On Fri, 5 Jan 2018 at 15:37 Daniel O' Shaughnessy <> wrote:
Hi Shane, 

I've successfully used : 


with pio. You can access feature importance through the RandomForestClassifier also.

Very simple to convert RDDs to DFs as Pat mentioned, something like:

val RDD_2_DF =
sqlContext.createDataFrame(yourRDD).toDF("col1", "col2")

On Thu, 4 Jan 2018 at 23:10 Pat Ferrel <> wrote:
Actually there are libs that will read DFs from HBase

This is out of band with PIO and should not be used IMO because the schema of the EventStore
is not guaranteed to remain as-is. The safest way is to translate or get DFs integrated to
PIO. I think there is an existing Jira that request Spark ML support, which assumes DFs. 

On Jan 4, 2018, at 12:25 PM, Pat Ferrel <> wrote:

Funny you should ask this. Yes, we are working on a DF based Universal Recommender but you
have to convert the RDD into a DF since PIO does not read out data in the form of a DF (yet).
This is a fairly simple step of maybe one line of code but would be better supported in PIO
itself. The issue is that the EventStore uses libs that may not read out DFs, but RDDs. This
is certainly the case with Elasticsearch, which provides an RDD lib. I haven’t seen one
from them that read out DFs though it would make a lot of sense for ES especially.

So TLDR; yes, just convert the RDD into a DF for now.

Also please add a feature request as a PIO Jira ticket to look into this. I for one would

On Jan 4, 2018, at 11:55 AM, Shane Johnson <> wrote:

Hello group, Happy new year! Does anyone have a working example or template using the DataFrame
API vs. the RDD based APIs. We are wanting to migrate to using the new DataFrame APIs to take
advantage of the Feature Importance function for our Regression Random Forest Models.

We are wanting to move from 

import org.apache.spark.mllib.tree.RandomForest
import org.apache.spark.mllib.tree.model.RandomForestModel
import org.apache.spark.mllib.util.MLUtils
import{RandomForestRegressionModel, RandomForestRegressor}

Is this something that should be fairly straightforward by adjusting parameters and calling
new classes within DASE or is it much more involved development.

Thank You!
Shane Johnson | 801.360.3350

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