yes, the first code takes only 30mins.
but the second method, I wait for 5 hours, only finish 10%
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I meet a situation:
When I use
val a = rdd.pipe("./my_cpp_program").persist()
a.count() // just use it to persist a
val b = a.map(s => (s, 1)).reduceByKey().count()
it 's so fast
but when I use
val b = rdd.pipe("./my_cpp_program").map(s => (s, 1)).reduceByKey().count()
it is so slow
and
hi Daniel,
Do you solve your problem?
I met the same problem when running massive data using reduceByKey on yarn.
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I'm running reduceByKey in spark. My program is the simplest example of
spark:
val counts = textFile.flatMap(line = line.split( )).repartition(2).
.map(word = (word, 1))
.reduceByKey(_ + _, 1)
counts.saveAsTextFile(hdfs://...)
but it always run out of
in spark streaming, I want to use fileStream to monitor a directory. But the
files in that directory are compressed using lz4. So the new lz4 files are
not detected by the following code. How to detect these new files?
val list_join_action_stream = ssc.fileStream[LongWritable, Text,
I 'm using streaming integrated with streaming-kafka.
My kafka topic has 80 partitions, while my machines have 40 cores. I found
that when the job is running, the kafka consumer processes are only deploy
to 2 machines, the bandwidth of the 2 machines will be very very high.
I wonder is there
I want to start a child-thread in foreachRDD.
My situation is:
the job is reading from a hdfs dir continuously, and every 100 batches, I
want to launch a model training task (I will make a snapshot of the rdds at
that time and start the training task. the training task takes a very long
time(2
