ODIn/spark-instrumented-optimizer
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity

[SPARK-16112][SPARKR] Programming guide for gapply/gapplyCollect

Browse source 
## What changes were proposed in this pull request?

Updates programming guide for spark.gapply/spark.gapplyCollect.

Similar to other examples I used `faithful` dataset to demonstrate gapply's functionality.
Please, let me know if you prefer another example.

## How was this patch tested?
Existing test cases in R

Author: Narine Kokhlikyan <narine@slice.com>

Closes #14090 from NarineK/gapplyProgGuide.
This commit is contained in:
Narine Kokhlikyan 2016-07-16 16:56:16 -07:00 committed by Shivaram Venkataraman
parent 5ec0d692b0
commit 4167304836
1 changed files with 134 additions and 4 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

138
docs/sparkr.md
View file

@ -272,11 +272,11 @@ In SparkR, we support several kinds of User-Defined Functions:
##### dapply
Apply a function to each partition of a `SparkDataFrame`. The function to be applied to each partition of the `SparkDataFrame`
and should have only one parameter, to which a `data.frame` corresponds to each partition will be passed. The output of function
should be a `data.frame`. Schema specifies the row format of the resulting a `SparkDataFrame`. It must match the R function's output.
and should have only one parameter, to which a `data.frame` corresponds to each partition will be passed. The output of function should be a `data.frame`. Schema specifies the row format of the resulting a `SparkDataFrame`. It must match to [data types](#data-type-mapping-between-r-and-spark) of returned value.
<div data-lang="r" markdown="1">
{% highlight r %}
# Convert waiting time from hours to seconds.
# Note that we can apply UDF to DataFrame.
schema <- structType(structField("eruptions", "double"), structField("waiting", "double"),
@ -295,8 +295,8 @@ head(collect(df1))
##### dapplyCollect
Like `dapply`, apply a function to each partition of a `SparkDataFrame` and collect the result back. The output of function
should be a `data.frame`. But, Schema is not required to be passed. Note that `dapplyCollect` only can be used if the
output of UDF run on all the partitions can fit in driver memory.
should be a `data.frame`. But, Schema is not required to be passed. Note that `dapplyCollect` can fail if the output of UDF run on all the partition cannot be pulled to the driver and fit in driver memory.
<div data-lang="r" markdown="1">
{% highlight r %}
@ -316,6 +316,136 @@ head(ldf, 3)
{% endhighlight %}
</div>
#### Run a given function on a large dataset grouping by input column(s) and using `gapply` or `gapplyCollect`
##### gapply
Apply a function to each group of a `SparkDataFrame`. The function is to be applied to each group of the `SparkDataFrame` and should have only two parameters: grouping key and R `data.frame` corresponding to
that key. The groups are chosen from `SparkDataFrame`s column(s).
The output of function should be a `data.frame`. Schema specifies the row format of the resulting
`SparkDataFrame`. It must represent R function's output schema on the basis of Spark data types. The column names of the returned `data.frame` are set by user. Below is the data type mapping between R
and Spark.
#### Data type mapping between R and Spark
<table class="table">
<tr><th>R</th><th>Spark</th></tr>
<tr>
<td>byte</td>
<td>byte</td>
</tr>
<tr>
<td>integer</td>
<td>integer</td>
</tr>
<tr>
<td>float</td>
<td>float</td>
</tr>
<tr>
<td>double</td>
<td>double</td>
</tr>
<tr>
<td>numeric</td>
<td>double</td>
</tr>
<tr>
<td>character</td>
<td>string</td>
</tr>
<tr>
<td>string</td>
<td>string</td>
</tr>
<tr>
<td>binary</td>
<td>binary</td>
</tr>
<tr>
<td>raw</td>
<td>binary</td>
</tr>
<tr>
<td>logical</td>
<td>boolean</td>
</tr>
<tr>
<td><a href="https://stat.ethz.ch/R-manual/R-devel/library/base/html/DateTimeClasses.html">POSIXct</a></td>
<td>timestamp</td>
</tr>
<tr>
<td><a href="https://stat.ethz.ch/R-manual/R-devel/library/base/html/DateTimeClasses.html">POSIXlt</a></td>
<td>timestamp</td>
</tr>
<tr>
<td><a href="https://stat.ethz.ch/R-manual/R-devel/library/base/html/Dates.html">Date</a></td>
<td>date</td>
</tr>
<tr>
<td>array</td>
<td>array</td>
</tr>
<tr>
<td>list</td>
<td>array</td>
</tr>
<tr>
<td>env</td>
<td>map</td>
</tr>
</table>
<div data-lang="r" markdown="1">
{% highlight r %}
# Determine six waiting times with the largest eruption time in minutes.
schema <- structType(structField("waiting", "double"), structField("max_eruption", "double"))
result <- gapply(
df,
"waiting",
function(key, x) {
y <- data.frame(key, max(x$eruptions))
},
schema)
head(collect(arrange(result, "max_eruption", decreasing = TRUE)))
## waiting max_eruption
##1 64 5.100
##2 69 5.067
##3 71 5.033
##4 87 5.000
##5 63 4.933
##6 89 4.900
{% endhighlight %}
</div>
##### gapplyCollect
Like `gapply`, applies a function to each partition of a `SparkDataFrame` and collect the result back to R data.frame. The output of the function should be a `data.frame`. But, the schema is not required to be passed. Note that `gapplyCollect` can fail if the output of UDF run on all the partition cannot be pulled to the driver and fit in driver memory.
<div data-lang="r" markdown="1">
{% highlight r %}
# Determine six waiting times with the largest eruption time in minutes.
result <- gapplyCollect(
df,
"waiting",
function(key, x) {
y <- data.frame(key, max(x$eruptions))
colnames(y) <- c("waiting", "max_eruption")
y
})
head(result[order(result$max_eruption, decreasing = TRUE), ])
## waiting max_eruption
##1 64 5.100
##2 69 5.067
##3 71 5.033
##4 87 5.000
##5 63 4.933
##6 89 4.900
{% endhighlight %}
</div>
#### Run local R functions distributed using `spark.lapply`
##### spark.lapply