Github user steveloughran commented on a diff in the pull request:
    --- Diff: docs/ ---
    @@ -644,13 +644,44 @@ methods for creating DStreams from files as input 
    -   Spark Streaming will monitor the directory `dataDirectory` and process 
any files created in that directory (files written in nested directories not 
supported). Note that
    -     + 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.
    +   Spark Streaming will monitor the directory `dataDirectory` and process 
any files created in that directory.
    +     + A simple directory can be supplied, such as 
    +       All files directly such a path will be processed as they are 
    +     + A regular expression can be supplied instead, such as
    +       `hdfs://namenode:8040/logs/2016-*-31`.
    +       Here, the DStream will consist of all files directly under those 
    +       matching the regular expression.
    +       That is: it is a pattern of directories, not of files in 
    +     + 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.
    +     + Files must be created in/moved under the `dataDirectory` 
directory/directories by
    +       an atomic operation. In HDFS and similar filesystems, this can be 
done *renaming* them
    +       into the data directory from another part of the same filesystem.
    +     * If a wildcard is used to identify directories, such as 
    +       renaming an entire directory to match the path will add the 
directory to the list of
    +       monitored directories. However, unless the modification time of the 
directory's files
    +       are within that of the current window, they will not be recognized 
as new files.
    +     + Once processed, changes to a file will not cause the file to be 
    +       That is: Updates are ignored.
    +     + The more files under a directory/wildcard pattern, the longer it 
will take to
    +       scan for changes —even if no files have actually changed.
    +    Special points for object stores
    --- End diff --
    For object stores, direct writes to the directory, resulting in a PUT on 
close(), will guarantee that a file is picked up immediately. Things are 
actually a bit quirky for HDFS; even file length doesn't get updated reliably 
during a write-in-progress. I'll add a section there and then ask people who 
understand HDFS what is really happening

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