S3 can be realized cheaper than HDFS on Amazon.
As you correctly describe it does not support data locality. The data is
distributed to the workers.
Depending on your use case it can make sense to have HDFS as a temporary
“cache” for S3 data.
> On 13. Dec 2017, at 09:39, Philip Lee <philjj...@gmail.com> wrote:
>
> Hi
>
> I have a few of questions about a structure of HDFS and S3 when Spark-like
> loads data from two storage.
>
> Generally, when Spark loads data from HDFS, HDFS supports data locality and
> already own distributed file on datanodes, right? Spark could just process
> data on workers.
>
> What about S3? many people in this field use S3 for storage or loading data
> remotely. When Spark loads data from S3 (sc.textFile('s3://...'), how all
> data will be spread on Workers? Master node's responsible for this task? It
> reads all data from S3, then spread the data to Worker? So it migt be a
> trade-off compared to HDFS? or I got a wrong point of this
> .
>
> What kind of points in S3 is better than that of HDFS?
> Thanks in Advanced
