Greetings!
Thanks for the comment.
I have tried several variants of this, as indicated.
The code works on small sets, but fails on larger sets.However, I don't get
memory errors.I see "java.nio.channels.CancelledKeyException" and things about
"lost task"and then things like "Resubmitting state 1", and off it goes.
I've already upped the memory (I think the last experiment had
"--executor-memory 6G" and "--driver memory 6G".
I'm experimenting with recoding this with map-reduce and so far seem to be
having more success (with HADOOP_OPTS="-Xmx6g -Xmx5g")
Again, each grouping should have no more than 6E7 values, and the data is
(DataKey(Int,Int), Option[Float]), so that shouldn't need 5g?
Anyway, thanks for the info.
Best wishes,Mike
From: Sean Owen <so...@cloudera.com>
To: Michael Albert <m_albert...@yahoo.com>
Cc: user@spark.apache.org
Sent: Friday, December 26, 2014 3:23 PM
Subject: Re: unable to do group by with 1st column
Here is a sketch of what you need to do off the top of my head and based on a
guess of what your RDD is like:val in: RDD[(K,Seq[(C,V)])] = ...in.flatMap {
case (key, colVals) =>
colVals.map { case (col, val) =>
(col, (key, val))
}
}.groupByKeySo the problem with both input and output here is that all values
for each key exist in memory at once. When transposed, each element contains
50M key value pairs. You probably should try to do what you're trying to do a
slightly different way.Depends on what you mean by resubmitting but I imagine
you need a cache() on an RDD you are reusing.
On Dec 26, 2014 4:18 PM, "Michael Albert" <m_albert...@yahoo.com.invalid> wrote:
Greetings!
I'm trying to do something similar, and having a very bad time of it.
What I start with is
key1: (col1, val-1-1, col2: val-1-2, col3: val-1-3, col4: val-1-4...)key2:
(col1: val-2-1, col2: val-2-2, col3: val-2-3, col4: val 2-4, ...)....
What I want (what I have been asked to produce :-)) is:
col1: (key1: val-1-1, key2: val-2-1, key3, val-3-1, ...)col2: (key1: val-1-2,
key2: val2-2, key3: val-3-2,...)
So basically the transpose. The input is actually avro/parquet with each "key"
in one record. In the output, the final step is to convert each column into a
"matlab" file.Please don't ask me whether this is a good idea.
I can get this to work for smallish data sets (e.g, a few hundred keys and a
few hundred columns).However, if I crank up the number of keys to about 5e7,
then this fails, even if I turn the number of columns that are actually used
down to 10.
The system seems to spend lots of time resubmitting parts of the first phase in
which the data is read from the original records and shuffled and never quite
finishes.
I can't post the code, but I can give folks and idea of what I've tried.
Try #1: Mapper emits data as (DataKey(col-as-int,key-as-int),
value-as-Option[Any]), then create a ShuffledRDD using the col-as-int for
partitioning and then "SetKeyOrdering" on the key-as-int. This is then fed to
"mapPartitionWithIndex".
Try #2: Emit (col-as-int, (key-as-int, value)) and groupBy, and have a final
"map()" on each "col".
Try #3: Emit (col-as-t, Collection[(key-as-int, value)]), then have a
reduceByKey which takes the "union" of the collection (union for set, ++ for
list) then havea final map() which attempts the final conversion.
No matter what I do, it works for for "small" numbers of keys (hundreds), but
when I crank it up, it seems to sit there resubmitting the shuffle phase.
Happy holidays, all!-Mike
From: Amit Behera <amit.bd...@gmail.com>
To: u...@spark.incubator.apache.org
Sent: Thursday, December 25, 2014 3:22 PM
Subject: unable to do group by with 1st column
Hi Users,
I am reading a csv file and my data format is like :
key1,value1key1,value2
key1,value1
key1,value3
key2,value1
key2,value5
key2,value5
key2,value4key1,value4key1,value4
key3,value1
key3,value1
key3,value2
required output :
key1:[value1,value2,value1,value3,value4,value4]key2:[value1,value5,value5,value4]key3:[value1,value1,value2]
How can I do it? Please help me to do.
ThanksAmit
