Skip to content

Commit 9d8e838

Browse files
Stephen Hoppersrowen
Stephen Hopper
authored andcommittedSep 8, 2015
[DOC] Added R to the list of languages with "high-level API" support in the…
… main README. Author: Stephen Hopper <[email protected]> Closes apache#8646 from enragedginger/master.
1 parent 5ffe752 commit 9d8e838

File tree

2 files changed

+10
-10
lines changed

2 files changed

+10
-10
lines changed
 

‎README.md

+2-2
Original file line numberDiff line numberDiff line change
@@ -1,7 +1,7 @@
11
# Apache Spark
22

33
Spark is a fast and general cluster computing system for Big Data. It provides
4-
high-level APIs in Scala, Java, and Python, and an optimized engine that
4+
high-level APIs in Scala, Java, Python, and R, and an optimized engine that
55
supports general computation graphs for data analysis. It also supports a
66
rich set of higher-level tools including Spark SQL for SQL and DataFrames,
77
MLlib for machine learning, GraphX for graph processing,
@@ -94,5 +94,5 @@ distribution.
9494

9595
## Configuration
9696

97-
Please refer to the [Configuration guide](http://spark.apache.org/docs/latest/configuration.html)
97+
Please refer to the [Configuration Guide](http://spark.apache.org/docs/latest/configuration.html)
9898
in the online documentation for an overview on how to configure Spark.

‎docs/quick-start.md

+8-8
Original file line numberDiff line numberDiff line change
@@ -126,7 +126,7 @@ scala> val wordCounts = textFile.flatMap(line => line.split(" ")).map(word => (w
126126
wordCounts: spark.RDD[(String, Int)] = spark.ShuffledAggregatedRDD@71f027b8
127127
{% endhighlight %}
128128

129-
Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations) and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (String, Int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
129+
Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations), and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (String, Int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
130130

131131
{% highlight scala %}
132132
scala> wordCounts.collect()
@@ -163,7 +163,7 @@ One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can i
163163
>>> wordCounts = textFile.flatMap(lambda line: line.split()).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a+b)
164164
{% endhighlight %}
165165

166-
Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations) and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (string, int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
166+
Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations), and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (string, int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
167167

168168
{% highlight python %}
169169
>>> wordCounts.collect()
@@ -217,13 +217,13 @@ a cluster, as described in the [programming guide](programming-guide.html#initia
217217
</div>
218218

219219
# Self-Contained Applications
220-
Now say we wanted to write a self-contained application using the Spark API. We will walk through a
221-
simple application in both Scala (with SBT), Java (with Maven), and Python.
220+
Suppose we wish to write a self-contained application using the Spark API. We will walk through a
221+
simple application in Scala (with sbt), Java (with Maven), and Python.
222222

223223
<div class="codetabs">
224224
<div data-lang="scala" markdown="1">
225225

226-
We'll create a very simple Spark application in Scala. So simple, in fact, that it's
226+
We'll create a very simple Spark application in Scala--so simple, in fact, that it's
227227
named `SimpleApp.scala`:
228228

229229
{% highlight scala %}
@@ -259,7 +259,7 @@ object which contains information about our
259259
application.
260260

261261
Our application depends on the Spark API, so we'll also include an sbt configuration file,
262-
`simple.sbt` which explains that Spark is a dependency. This file also adds a repository that
262+
`simple.sbt`, which explains that Spark is a dependency. This file also adds a repository that
263263
Spark depends on:
264264

265265
{% highlight scala %}
@@ -302,7 +302,7 @@ Lines with a: 46, Lines with b: 23
302302

303303
</div>
304304
<div data-lang="java" markdown="1">
305-
This example will use Maven to compile an application jar, but any similar build system will work.
305+
This example will use Maven to compile an application JAR, but any similar build system will work.
306306

307307
We'll create a very simple Spark application, `SimpleApp.java`:
308308

@@ -374,7 +374,7 @@ $ find .
374374
Now, we can package the application using Maven and execute it with `./bin/spark-submit`.
375375

376376
{% highlight bash %}
377-
# Package a jar containing your application
377+
# Package a JAR containing your application
378378
$ mvn package
379379
...
380380
[INFO] Building jar: {..}/{..}/target/simple-project-1.0.jar

0 commit comments

Comments
 (0)
Please sign in to comment.