Hi.
Your code is like this right?
"/joined_dataset = show_channel.join(show_views) joined_dataset.take(4)/"
well /joined_dataset / is now an array (because you used /.take(4)/ ).
So it does not support any RDD operations..
Could that be the problem?
Otherwise more code is needed to
1) Start by looking at ML-lib or KeystoneML
2) If you can't find an impl., start by analyzing the access patterns and
data manipulations you will need to implement.
3) Then figure out if it fits Spark structures.. and when you realized it
doesn't you start speculating on how you can twist or
Hi.
Can't you do a filter, to get only the ABC shows, map that into a keyed
instance of the show,
and then do a reduceByKey to sum up the views?
Something like this in Scala code: /filter for the channel new pair
(show, view count) /
val myAnswer = joined_dataset.filter( _._2._1 == "ABC"
Look at KeystoneML, there is an image processing pipeline there
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Can't you just access it by element, like with [0] and [1] ?
http://www.tutorialspoint.com/python/python_tuples.htm
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"spark.storage.memoryFraction 0.05"
If you want to store a lot of memory I think this must be a higher fraction.
The default is 0.6 (not 0.0X).
To change the output directory you can set "spark.local.dir=/path/to/dir"
and you can even specify multiple directories (for example if you have
HDFS has a default replication factor of 3
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ckage.scala:107)
at org.apache.spark.util.AkkaUtils$.askWithReply(AkkaUtils.scala:195)
... 13 more"
I have already set set the akka.timeout to 300 etc.
Anyone have any ideas on what the problem could be ?
Regares,
Gylfi.
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Hi.
You may want to look into Indexed RDDs
https://github.com/amplab/spark-indexedrdd
Regards,
Gylfi.
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Hi.
What is slow exactly?
In code-base 1:
When you run the persist() + count() you stored the result in RAM.
Then the map + reducebykey is done on in-memory data.
In the latter case (all-in-oneline) you are doing both steps at the same
time.
So you are saying that if you sum-up the time to
By default Spark will actually not keep the data at all, it will just store
"how" to recreate the data.
The programmer can however choose to keep the data once instantiated by
calling "/.persist()/" or "/.cache()/" on the RDD.
/.cache/ will store the data in-memory only and fail if it will not
on how much RAM you have per node, you may want to re-block the
data on HDFS for optimal performance.
Hope this helps,
Gylfi.
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You may want to look into using the pipe command ..
http://blog.madhukaraphatak.com/pipe-in-spark/
http://spark.apache.org/docs/0.6.0/api/core/spark/rdd/PipedRDD.html
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structure as it is not synced between workers after it is broadcasted.
To broadcast, your data must be serializable.
If the data you are trying to broadcast is a distributed RDD (and thus I
assumably large), perhaps what you need is some form of join operation (or
cogroup)?
Regards,
Gylfi
Hi.
Assuming your have the data in an RDD you can save your RDD (regardless of
structure) with nameRDD.saveAsObjectFile(path) where path can be
hdfs:///myfolderonHDFS or the local file system.
Alternatively you can also use .saveAsTextFile()
Regards,
Gylfi.
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into more parts before line 52 by calling
rddname.repartition(10) for example and see if it runs faster..
Regards,
Gylfi.
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could would be something like this ..
val flattnedIntRDD : RDD[(Int)] = intArraysRDD.flatmap( array =
array.toList)
However, to understand exactly your problem you need to explain better what
the RDD you want to create should look like..
Regards,
Gylfi.
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both RDDs are destroyed again.
If you run the myrdd2.count again, both myrdd and myrdd2 are created again
..
If your transformation is expensive, you may want to keep the data around
and for that must use .persist() or .cache() etc.
Regards,
Gylfi.
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http
You could even try changing the block size of the input data on HDFS (can be
done on a per file basis) and that would get all workers going right from
the get-go in Spark.
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? Does this make any sense? :)
Regards,
Gylfi.
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;)
Regards and good luck,
Gylfi.
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Hi.
I am just wondering if the rdd was actually modified.
Did you test it by printing rdd.partitions.length before and after?
Regards,
Gylfi.
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actually correct?
Hope this helps..
Regards,
Gylfi.
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Hi.
Have you tried to repartition the finalRDD before saving?
This link might help.
http://databricks.gitbooks.io/databricks-spark-reference-applications/content/logs_analyzer/chapter3/save_the_rdd_to_files.html
Regards,
Gylfi.
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spark.speculation.multiplier
spark.speculation.quantile
See https://spark.apache.org/docs/latest/configuration.html under
Scheduling.
Regards,
Gylfi.
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