Chris,
To use s3distcp in this case, are you suggesting saving the RDD to
local/ephemeral HDFS and then copying it up to S3 using this tool?
On Sat, May 3, 2014 at 7:14 PM, Chris Fregly ch...@fregly.com wrote:
not sure if this directly addresses your issue, peter, but it's worth
mentioned a
Thank you Chris, I am familiar with S3distcp, I'm trying to replicate some of
that functionality and combine it with my log post processing in one step
instead of yet another step.
On Saturday, May 3, 2014 4:15 PM, Chris Fregly ch...@fregly.com wrote:
not sure if this directly addresses your
Hi Patrick
I should probably explain my use case in a bit more detail. I have hundreds of
thousands to millions of clients uploading events to my pipeline, these are
batched periodically (every 60 seconds atm) into logs which are dumped into S3
(and uploaded into a data warehouse). I need to
not sure if this directly addresses your issue, peter, but it's worth
mentioned a handy AWS EMR utility called s3distcp that can upload a single
HDFS file - in parallel - to a single, concatenated S3 file once all the
partitions are uploaded. kinda cool.
here's some info:
Hi Peter,
If your dataset is large (3GB) then why not just chunk it into
multiple files? You'll need to do this anyways if you want to
read/write this from S3 quickly, because S3's throughput per file is
limited (as you've seen).
This is exactly why the Hadoop API lets you save datasets with
Thank you Patrick.
I took a quick stab at it:
val s3Client = new AmazonS3Client(...)
val copyObjectResult = s3Client.copyObject(upload, outputPrefix +
/part-0, rolled-up-logs, 2014-04-28.csv)
val objectListing = s3Client.listObjects(upload, outputPrefix)
The fastest way to save to S3 should be to leave the RDD with many
partitions, because all partitions will be written out in parallel.
Then, once the various parts are in S3, somehow concatenate the files
together into one file.
If this can be done within S3 (I don't know if this is possible),
Hi
Playing around with Spark S3, I'm opening multiple objects (CSV files) with:
val hfile = sc.textFile(s3n://bucket/2014-04-28/)
so hfile is a RDD representing 10 objects that were underneath 2014-04-28.
After I've sorted and otherwise transformed the content, I'm trying to write it
Ah, looks like RDD.coalesce(1) solves one part of the problem.
On Wednesday, April 30, 2014 11:15 AM, Peter thenephili...@yahoo.com wrote:
Hi
Playing around with Spark S3, I'm opening multiple objects (CSV files) with:
val hfile = sc.textFile(s3n://bucket/2014-04-28/)
so hfile is a RDD
Yes, saveAsTextFile() will give you 1 part per RDD partition. When you
coalesce(1), you move everything in the RDD to a single partition, which
then gives you 1 output file.
It will still be called part-0 or something like that because that’s
defined by the Hadoop API that Spark uses for
Thanks Nicholas, this is a bit of a shame, not very practical for log roll up
for example when every output needs to be in it's own directory.
On Wednesday, April 30, 2014 12:15 PM, Nicholas Chammas
nicholas.cham...@gmail.com wrote:
Yes, saveAsTextFile() will give you 1 part per RDD
This is a consequence of the way the Hadoop files API works. However,
you can (fairly easily) add code to just rename the file because it
will always produce the same filename.
(heavy use of pseudo code)
dir = /some/dir
rdd.coalesce(1).saveAsTextFile(dir)
f = new File(dir + part-0)
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