README.md
In jjallaire/sparklingwater: R Interface for H2O SparkingWater
Sparkling Water for R
This is a proof of concept extension package for sparkapi / sparklyr that demonstrates creating an R front-end for a Spark package (in this case Sparkling Water from H2O).
This package implements only the most basic functionality (creating an H2OContext, showing the H2O Flow interface, and converting a Spark DataFrame to an H2O Frame). Note that the package won't be developed further since it's just a demonstration.
Connecting to Spark
First we connect to Spark. The call to library(sparklingwater) will make the H2O functions available on the R search path and will also ensure that the dependencies required by the Sparkling Water package are included when we connect to Spark.
library(sparklyr)
library(sparklingwater)
sc <- spark_connect(master = "local")
H2O Context and Flow
The call to library(sparklingwater) automatically registered the Sparkling Water extension, which in turn specified that the Sparkling Water Spark package should be made available for Spark connections. Let's inspect the H2OContext for our Spark connection:
h2o_context(sc)
## <jobj[6]>
## class org.apache.spark.h2o.H2OContext
##
## Sparkling Water Context:
## * H2O name: sparkling-water-jjallaire_-1482215501
## * number of executors: 1
## * list of used executors:
## (executorId, host, port)
## ------------------------
## (driver,localhost,54323)
## ------------------------
##
## Open H2O Flow in browser: http://127.0.0.1:54323 (CMD + click in Mac OSX)
##
We can also view the H2O Flow web UI:
h2o_flow(sc)
H2O with Spark DataFrames
Let's copy the mtcars dataset to to Spark so we can access it from Sparkling Water:
library(dplyr)
mtcars_tbl <- copy_to(sc, mtcars, overwrite = TRUE)
mtcars_tbl
## Source: query [?? x 11]
## Database: spark connection master=local[8] app=sparklyr local=TRUE
##
## mpg cyl disp hp drat wt qsec vs am gear carb
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## # ... with more rows
The use case we'd like to enable is calling the H2O algorithms and feature transformers directly on Spark DataFrames that we've manipulated with dplyr. This is indeed supported by the Sparkling Water package. Here though we'll just convert the Spark DataFrame into an H2O Frame to prove that it's possible:
mtcars_hf <- h2o_frame(mtcars_tbl)
mtcars_hf
## <jobj[103]>
## class water.fvec.H2OFrame
## Frame frame_rdd_39 (32 rows and 11 cols):
## mpg cyl disp hp drat wt qsec vs am gear carb
## min 10.4 4 71.1 52 2.76 1.513 14.5 0 0 3 1
## mean 20.090625 6 230.721875 146 3.5965625 3.21725 17.848750000000003 0 0 3 2
## stddev 6.026948052089104 1 123.93869383138194 68 0.5346787360709715 0.9784574429896966 1.7869432360968436 0 0 0 1
## max 33.9 8 472.0 335 4.93 5.424 22.9 1 1 5 8
## missing 0.0 0 0.0 0 0.0 0.0 0.0 0 0 0 0
## 0 21.0 6 160.0 110 3.9 2.62 16.46 0 1 4 4
## 1 21.0 6 160.0 110 3.9 2.875 17.02 0 1 4 4
## 2 22.8 4 108.0 93 3.85 2.32 18.61 1 1 4 1
## 3 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 4 18.7 8 360.0 175 3.15 3.44 17.02 0 0 3 2
## 5 18.1 6 225.0 105 2.76 3.46 20.22 1 0 3 1
## 6 14.3 8 360.0 245 3.21 3.57 15.84 0 0 3 4
## 7 24.4 4 146.7 62 3.69 3.19 20.0 1 0 4 2
## 8 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2
## 9 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4
## 10 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4
## 11 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3
## 12 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3
## 13 15.2 8 275.8 180 3.07 3.78 18.0 0 0 3 3
## 14 10.4 8 472.0 205 2.93 5.25 17.98 0 0 3 4
## 15 10.4 8 460.0 215 3.0 5.424 17.82 0 0 3 4
## 16 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 17 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1
## 18 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 19 33.9 4 71.1 65 4.22 1.835 19.9 1 1 4 1
Now we disconnect from Spark, this will result in the H2OContext being stopped as well since it's owned by the spark shell process used by our Spark connection:
spark_disconnect(sc)
jjallaire/sparklingwater documentation built on May 19, 2019, 11:38 a.m.
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Sparkling Water for R
This is a proof of concept extension package for sparkapi / sparklyr that demonstrates creating an R front-end for a Spark package (in this case Sparkling Water from H2O).
This package implements only the most basic functionality (creating an H2OContext, showing the H2O Flow interface, and converting a Spark DataFrame to an H2O Frame). Note that the package won't be developed further since it's just a demonstration.
Connecting to Spark
First we connect to Spark. The call to library(sparklingwater) will make the H2O functions available on the R search path and will also ensure that the dependencies required by the Sparkling Water package are included when we connect to Spark.
library(sparklyr)
library(sparklingwater)
sc <- spark_connect(master = "local")
H2O Context and Flow
The call to library(sparklingwater) automatically registered the Sparkling Water extension, which in turn specified that the Sparkling Water Spark package should be made available for Spark connections. Let's inspect the H2OContext for our Spark connection:
h2o_context(sc)
## <jobj[6]>
## class org.apache.spark.h2o.H2OContext
##
## Sparkling Water Context:
## * H2O name: sparkling-water-jjallaire_-1482215501
## * number of executors: 1
## * list of used executors:
## (executorId, host, port)
## ------------------------
## (driver,localhost,54323)
## ------------------------
##
## Open H2O Flow in browser: http://127.0.0.1:54323 (CMD + click in Mac OSX)
##
We can also view the H2O Flow web UI:
h2o_flow(sc)
H2O with Spark DataFrames
Let's copy the mtcars dataset to to Spark so we can access it from Sparkling Water:
library(dplyr)
mtcars_tbl <- copy_to(sc, mtcars, overwrite = TRUE)
mtcars_tbl
## Source: query [?? x 11]
## Database: spark connection master=local[8] app=sparklyr local=TRUE
##
## mpg cyl disp hp drat wt qsec vs am gear carb
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## # ... with more rows
The use case we'd like to enable is calling the H2O algorithms and feature transformers directly on Spark DataFrames that we've manipulated with dplyr. This is indeed supported by the Sparkling Water package. Here though we'll just convert the Spark DataFrame into an H2O Frame to prove that it's possible:
mtcars_hf <- h2o_frame(mtcars_tbl)
mtcars_hf
## <jobj[103]>
## class water.fvec.H2OFrame
## Frame frame_rdd_39 (32 rows and 11 cols):
## mpg cyl disp hp drat wt qsec vs am gear carb
## min 10.4 4 71.1 52 2.76 1.513 14.5 0 0 3 1
## mean 20.090625 6 230.721875 146 3.5965625 3.21725 17.848750000000003 0 0 3 2
## stddev 6.026948052089104 1 123.93869383138194 68 0.5346787360709715 0.9784574429896966 1.7869432360968436 0 0 0 1
## max 33.9 8 472.0 335 4.93 5.424 22.9 1 1 5 8
## missing 0.0 0 0.0 0 0.0 0.0 0.0 0 0 0 0
## 0 21.0 6 160.0 110 3.9 2.62 16.46 0 1 4 4
## 1 21.0 6 160.0 110 3.9 2.875 17.02 0 1 4 4
## 2 22.8 4 108.0 93 3.85 2.32 18.61 1 1 4 1
## 3 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 4 18.7 8 360.0 175 3.15 3.44 17.02 0 0 3 2
## 5 18.1 6 225.0 105 2.76 3.46 20.22 1 0 3 1
## 6 14.3 8 360.0 245 3.21 3.57 15.84 0 0 3 4
## 7 24.4 4 146.7 62 3.69 3.19 20.0 1 0 4 2
## 8 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2
## 9 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4
## 10 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4
## 11 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3
## 12 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3
## 13 15.2 8 275.8 180 3.07 3.78 18.0 0 0 3 3
## 14 10.4 8 472.0 205 2.93 5.25 17.98 0 0 3 4
## 15 10.4 8 460.0 215 3.0 5.424 17.82 0 0 3 4
## 16 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 17 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1
## 18 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 19 33.9 4 71.1 65 4.22 1.835 19.9 1 1 4 1
Now we disconnect from Spark, this will result in the H2OContext being stopped as well since it's owned by the spark shell process used by our Spark connection:
spark_disconnect(sc)
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Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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