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author | Reza Zadeh <rizlar@gmail.com> | 2014-01-09 22:45:32 -0800 |
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committer | Reza Zadeh <rizlar@gmail.com> | 2014-01-09 22:45:32 -0800 |
commit | 21c8a54c08354f8934fd8ec58b43879c1686ccad (patch) | |
tree | 51426328d9f0eafdeec7fb46ef99c86f27f86dd2 /docs/streaming-programming-guide.md | |
parent | cf5bd4ab2e9db72d3d9164053523e9e872d85b94 (diff) | |
parent | 300eaa994c399a0c991c1e39b4dd864a7aa4bdc6 (diff) | |
download | spark-21c8a54c08354f8934fd8ec58b43879c1686ccad.tar.gz spark-21c8a54c08354f8934fd8ec58b43879c1686ccad.tar.bz2 spark-21c8a54c08354f8934fd8ec58b43879c1686ccad.zip |
Merge remote-tracking branch 'upstream/master' into sparsesvd
Conflicts:
docs/mllib-guide.md
Diffstat (limited to 'docs/streaming-programming-guide.md')
-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 6f97db6a3e..1c9ece6270 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -245,7 +245,7 @@ $ nc -lk 9999 Then, in a different terminal, you can start NetworkWordCount by using {% highlight bash %} -$ ./run-example org.apache.spark.streaming.examples.NetworkWordCount local[2] localhost 9999 +$ ./bin/run-example org.apache.spark.streaming.examples.NetworkWordCount local[2] localhost 9999 {% endhighlight %} This will make NetworkWordCount connect to the netcat server. Any lines typed in the terminal running the netcat server will be counted and printed on screen. @@ -283,7 +283,7 @@ Time: 1357008430000 ms </td> </table> -You can find more examples in `<Spark repo>/streaming/src/main/scala/org/apache/spark/streaming/examples/`. They can be run in the similar manner using `./run-example org.apache.spark.streaming.examples....` . Executing without any parameter would give the required parameter list. Further explanation to run them can be found in comments in the files. +You can find more examples in `<Spark repo>/streaming/src/main/scala/org/apache/spark/streaming/examples/`. They can be run in the similar manner using `./bin/run-example org.apache.spark.streaming.examples....` . Executing without any parameter would give the required parameter list. Further explanation to run them can be found in comments in the files. # DStream Persistence Similar to RDDs, DStreams also allow developers to persist the stream's data in memory. That is, using `persist()` method on a DStream would automatically persist every RDD of that DStream in memory. This is useful if the data in the DStream will be computed multiple times (e.g., multiple operations on the same data). For window-based operations like `reduceByWindow` and `reduceByKeyAndWindow` and state-based operations like `updateStateByKey`, this is implicitly true. Hence, DStreams generated by window-based operations are automatically persisted in memory, without the developer calling `persist()`. |