Hi Benedict, Did you set lambda to zero? -Xiangrui
On Mon, Jul 13, 2015 at 4:18 AM, Benedict Liang bli...@thecarousell.com wrote:
Hi Sean,
This user dataset is organic. What do you think is a good ratings threshold
then? I am only encountering this with the implicit type though. The
explicit
Hi all,
I have questions with regarding to the log file directory.
That say if I run spark-submit --master local[4], where is the log file?
Then how about if I run standalone spark-submit --master
spark://mymaster:7077?
Best regards,
Jack
Hi Aniruddh,
Increasing number of partitions doesn't always help in ALS due to
communication/computation trade-off. What rank did you set? If the
rank is not large, I'd recommend a small number of partitions. There
are some other numbers to watch. Do you have super popular items/users
in your
Hi Danny,
You might need to reduce the number of partitions (or set userBlocks
and productBlocks directly in ALS). Using a large number of partitions
increases shuffle size and memory requirement. If you have 16 x 16 =
256 cores. I would recommend 64 or 128 instead of 2048.
It seems that the error happens before ALS iterations. Could you try
`ratings.first()` right after `ratings = newrdd.map(lambda l:
Rating(int(l[1]),int(l[2]),l[4])).partitionBy(50)`? -Xiangrui
On Fri, Jun 26, 2015 at 2:28 PM, Ayman Farahat ayman.fara...@yahoo.com wrote:
I tried something similar
You have to partition that data on the Spark Streaming by the primary key,
and then make sure insert data into Cassandra atomically per key, or per
set of keys in the partition. You can use the combination of the (batch
time, and partition Id) of the RDD inside foreachRDD as the unique id for
the
Unfortunately, AFAIK custom transformers are not part of the public API so
you will have to continue with what you're doing.
On Tue, Jul 28, 2015 at 1:32 PM, Matt Narrell matt.narr...@gmail.com
wrote:
Hey,
Our ML ETL pipeline has several complex steps that I’d like to address
with custom
If you are using the same RDDs in the both the attempts to run the job, the
previous stage outputs generated in the previous job will indeed be reused.
This applies to core though. For dataframes, depending on what you do, the
physical plan may get generated again leading to new RDDs which may
I agree with Sean - using virtual box on windows and using linux vm is a lot
easier than trying to circumvent the cygwin oddities. a lot of functionality
might not work in cygwin and you will end up trying to do back patches. Unless
there is a compelling reason - cygwin support seems not
I agree, I found this book very useful for getting started with spark and
eclipse
On Tue, Jul 28, 2015 at 11:10 AM, Petar Zecevic petar.zece...@gmail.com
wrote:
Sorry about self-promotion, but there's a really nice tutorial for setting
up Eclipse for Spark in Spark in Action book:
On Tue, Jul 28, 2015 at 2:17 PM, Elkhan Dadashov elkhan8...@gmail.com
wrote:
Thanks Corey for your answer,
Do you mean that final status : SUCCEEDED in terminal logs means that
YARN RM could clean the resources after the application has finished
(application finishing does not necessarily
I run Spark in yarn-cluster mode. and yes , log aggregation enabled. In
Yarn aggregated logs i can the job status correctly.
The issue is Yarn Client logs (which is written to stdout in terminal)
states that job has succeeded even though the job has failed.
As user is not testing if Yarn RM
try looking at the causes and steps here
https://wiki.apache.org/hadoop/BindException
On 28 Jul 2015, at 09:22, Wayne Song
wayne.e.s...@gmail.commailto:wayne.e.s...@gmail.com wrote:
I made this message with the Nabble web interface; I included the stack trace
there, but I guess it didn't
That's for the Windows interpreter rather than bash-running Cygwin. I
don't know it's worth doing a lot of legwork for Cygwin, but, if it's
really just a few lines of classpath translation in one script, seems
reasonable.
On Tue, Jul 28, 2015 at 9:13 PM, Steve Loughran ste...@hortonworks.com
Hey,
Our ML ETL pipeline has several complex steps that I’d like to address with
custom Transformers in an ML Pipeline. Looking at the Tokenizer and HashingTF
transformers I see these handy traits (HasInputCol, HasLabelCol, HasOutputCol,
etc.) but they have strict access modifiers. How can I
I am building an analytics environment based on Spark and want to use HIVE in
multi-user mode i.e. not use the embedded derby database but use Postgres
and HDFS instead. I am using the included Spark Thrift Server to process
queries using Spark SQL.
The documentation gives me the impression that
val ssc = new StreamingContext(sc, Minutes(10))
//500 textFile streams watching S3 directories
val streams = streamPaths.par.map { path =
ssc.textFileStream(path)
}
streams.par.foreach { stream =
stream.foreachRDD { rdd =
//do something
}
}
ssc.start()
Would something like this
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