You situation is special. It seems to me Spark may not fit well in your
case.
You want to process the individual files (500M~2G) as a whole, you want
good performance.
You may want to write our own Scala/Java programs and distribute it along
with those files across your cluster, and run them in parallel.
If you insist on using Spark, maybe option 3 is closer.
Cheers,
Tao
On Thu, Sep 3, 2015 at 10:22 AM, Shuai Zheng <szheng.c...@gmail.com> wrote:
> 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
>
--
------------------------------------------------
Thanks!
Tao
