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author | zsxwing <zsxwing@gmail.com> | 2014-12-25 19:46:05 -0800 |
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committer | Tathagata Das <tathagata.das1565@gmail.com> | 2014-12-25 19:46:05 -0800 |
commit | f9ed2b6641b9df39cee4b98a33cd5a3ddda2d146 (patch) | |
tree | 3f07c19a7e12db441ff1eba906ac5b671f644fe3 /docs | |
parent | f205fe477c33a541053c198cd43a5811d6cf9fe2 (diff) | |
download | spark-f9ed2b6641b9df39cee4b98a33cd5a3ddda2d146.tar.gz spark-f9ed2b6641b9df39cee4b98a33cd5a3ddda2d146.tar.bz2 spark-f9ed2b6641b9df39cee4b98a33cd5a3ddda2d146.zip |
[SPARK-4608][Streaming] Reorganize StreamingContext implicit to improve API convenience
There is only one implicit function `toPairDStreamFunctions` in `StreamingContext`. This PR did similar reorganization like [SPARK-4397](https://issues.apache.org/jira/browse/SPARK-4397).
Compiled the following codes with Spark Streaming 1.1.0 and ran it with this PR. Everything is fine.
```Scala
import org.apache.spark._
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._
object StreamingApp {
def main(args: Array[String]) {
val conf = new SparkConf().setMaster("local[2]").setAppName("FileWordCount")
val ssc = new StreamingContext(conf, Seconds(10))
val lines = ssc.textFileStream("/some/path")
val words = lines.flatMap(_.split(" "))
val pairs = words.map(word => (word, 1))
val wordCounts = pairs.reduceByKey(_ + _)
wordCounts.print()
ssc.start()
ssc.awaitTermination()
}
}
```
Author: zsxwing <zsxwing@gmail.com>
Closes #3464 from zsxwing/SPARK-4608 and squashes the following commits:
aa6d44a [zsxwing] Fix a copy-paste error
f74c190 [zsxwing] Merge branch 'master' into SPARK-4608
e6f9cc9 [zsxwing] Update the docs
27833bb [zsxwing] Remove `import StreamingContext._`
c15162c [zsxwing] Reorganize StreamingContext implicit to improve API convenience
Diffstat (limited to 'docs')
-rw-r--r-- | docs/streaming-programming-guide.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index 1ac5b9e863..01450efe35 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -75,7 +75,7 @@ main entry point for all streaming functionality. We create a local StreamingCon {% highlight scala %} import org.apache.spark._ import org.apache.spark.streaming._ -import org.apache.spark.streaming.StreamingContext._ +import org.apache.spark.streaming.StreamingContext._ // not necessary in Spark 1.3+ // Create a local StreamingContext with two working thread and batch interval of 1 second. // The master requires 2 cores to prevent from a starvation scenario. @@ -107,7 +107,7 @@ each line will be split into multiple words and the stream of words is represent `words` DStream. Next, we want to count these words. {% highlight scala %} -import org.apache.spark.streaming.StreamingContext._ +import org.apache.spark.streaming.StreamingContext._ // not necessary in Spark 1.3+ // Count each word in each batch val pairs = words.map(word => (word, 1)) val wordCounts = pairs.reduceByKey(_ + _) |