Have a look at the sparkContext.binaryFiles, it works like wholeTextFiles but returns a PortableDataStream per file. It might be a workable solution though you'll need to handle the binary to UTF-8 or equivalent conversion
Thanks, Ewan From: Shuai Zheng [mailto:szheng.c...@gmail.com] Sent: 03 September 2015 15:22 To: user@spark.apache.org Subject: How to Take the whole file as a partition Hi All, I have 1000 files, from 500M to 1-2GB at this moment. And I want my spark can read them as partition on the file level. Which means want the FileSplit turn off. I know there are some solutions, but not very good in my case: 1, I can't use WholeTextFiles method, because my file is too big, I don't want to risk the performance. 2, I try to use newAPIHadoopFile and turnoff the file split: lines = ctx.newAPIHadoopFile(inputPath, NonSplitableTextInputFormat.class, LongWritable.class, Text.class, hadoopConf).values() .map(new Function<Text, String>() { @Override public String call(Text arg0) throws Exception { return arg0.toString(); } }); This works for some cases, but it truncate some lines (I am not sure why, but it looks like there is a limit on this file reading). I have a feeling that the spark truncate this file on 2GB bytes. Anyway it happens (because same data has no issue when I use mapreduce to do the input), the spark sometimes do a trunc on very big file if try to read all of them. 3, I can do another way is distribute the file name as the input of the Spark and in function open stream to read the file directly. This is what I am planning to do but I think it is ugly. I want to know anyone have better solution for it? BTW: the file currently in text format, but it might be parquet format later, that is also reason I don't like my third option. Regards, Shuai
