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From steveloughran <...@git.apache.org>
Subject [GitHub] spark pull request #14731: [SPARK-17159] [streaming]: optimise check for new...
Date Fri, 24 Feb 2017 16:30:37 GMT
Github user steveloughran commented on a diff in the pull request:

    https://github.com/apache/spark/pull/14731#discussion_r102977480
  
    --- Diff: docs/streaming-programming-guide.md ---
    @@ -630,35 +630,106 @@ which creates a DStream from text
     data received over a TCP socket connection. Besides sockets, the StreamingContext API
provides
     methods for creating DStreams from files as input sources.
     
    -- **File Streams:** For reading data from files on any file system compatible with the
HDFS API (that is, HDFS, S3, NFS, etc.), a DStream can be created as:
    +#### File Streams
    +{:.no_toc}
    +
    +For reading data from files on any file system compatible with the HDFS API (that is,
HDFS, S3, NFS, etc.), a DStream can be created as
    +via `StreamingContext.fileStream[KeyClass, ValueClass, InputFormatClass]`.
    +
    +File streams do not require running a receiver, hence does not require allocating cores.
     
    -    <div class="codetabs">
    -    <div data-lang="scala" markdown="1">
    -        streamingContext.fileStream[KeyClass, ValueClass, InputFormatClass](dataDirectory)
    -    </div>
    -    <div data-lang="java" markdown="1">
    -		streamingContext.fileStream<KeyClass, ValueClass, InputFormatClass>(dataDirectory);
    -    </div>
    -    <div data-lang="python" markdown="1">
    -		streamingContext.textFileStream(dataDirectory)
    -    </div>
    -    </div>
    +For simple text files, the easiest method is `StreamingContext.textFileStream(dataDirectory)`.

     
    -	Spark Streaming will monitor the directory `dataDirectory` and process any files created
in that directory (files written in nested directories not supported). Note that
    +<div class="codetabs">
    +<div data-lang="scala" markdown="1">
     
    -     + The files must have the same data format.
    -     + The files must be created in the `dataDirectory` by atomically *moving* or *renaming*
them into
    -     the data directory.
    -     + Once moved, the files must not be changed. So if the files are being continuously
appended, the new data will not be read.
    +{% highlight scala %}
    +streamingContext.fileStream[KeyClass, ValueClass, InputFormatClass](dataDirectory)
    +{% endhighlight %}
    +For text files
    +
    +{% highlight scala %}
    +streamingContext.textFileStream(dataDirectory)
    +{% endhighlight %}
    +</div>
     
    -	For simple text files, there is an easier method `streamingContext.textFileStream(dataDirectory)`.
And file streams do not require running a receiver, hence does not require allocating cores.
    +<div data-lang="java" markdown="1">
    +{% highlight java %}
    +streamingContext.fileStream<KeyClass, ValueClass, InputFormatClass>(dataDirectory);
    +{% endhighlight %}
    +For text files
     
    -	<span class="badge" style="background-color: grey">Python API</span> `fileStream`
is not available in the Python API, only	`textFileStream` is	available.
    +{% highlight java %}
    +streamingContext.textFileStream(dataDirectory);
    +{% endhighlight %}
    +</div>
     
    -- **Streams based on Custom Receivers:** DStreams can be created with data streams received
through custom receivers. See the [Custom Receiver
    +<div data-lang="python" markdown="1">
    +`fileStream` is not available in the Python API; only `textFileStream` is available.
    +{% highlight python %}
    +streamingContext.textFileStream(dataDirectory)
    +{% endhighlight %}
    +</div>
    +
    +</div>
    +
    +##### How Directories are Monitored
    +{:.no_toc}
    +
    +Spark Streaming will monitor the directory `dataDirectory` and process any files created
in that directory.
    +
    +   * A simple directory can be monitored, such as `hdfs://namenode:8040/logs/`.
    +     All files directly under such a path will be processed as they are discovered.
    +   + A [POSIX glob pattern](http://pubs.opengroup.org/onlinepubs/009695399/utilities/xcu_chap02.html#tag_02_13_02)
can be supplied, such as
    +     `hdfs://namenode:8040/logs/2017/*`.
    +     Here, the DStream will consist of all files in the directories
    +     matching the pattern.
    +     That is: it is a pattern of directories, not of files in directories.
    +   + All files must be in the same data format.
    +   * A file is considered part of a time period based on its modification time,
    +     not its creation time.
    +   + Once processed, changes to a file within the current window will not cause the file
to be reread.
    +     That is: *updates are ignored*.
    +   + The more files under a directory, the longer it will take to
    +     scan for changes —even if no files have been modified.
    --- End diff --
    
    fixed


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