> (i.e. almost all productive uses of this dataframe) fails due to this
> exception.
>
> Is it a miss in the Literal implementation that it does not handle
> UserDefinedTypes or is it left out intentionally? Is there a way to get
> around this problem? This problem seems to be present
s in DataFrames, Python/R APIs for
>> DataFrames, dstream, and most recently Structured Streaming.
>>
>>
>>
>> Holden has been a long time Spark contributor and evangelist. She has
>> written a few books on Spark, as well as frequent contributions to the
>> Python A
>
> rdd.collect().foreach(println)
> throw new RuntimeException //exception
> }
> ssc.start()
> try {
> ssc.awaitTermination()
> } catch {
> case e: Exception => {
> System.out.println("end!")
> throw e
> }
> }
-
Liang-C
a number most close to that config.
> E.g. the config is 2000:
> if limit=1, 1 = 2000 * 5, we shuffle to 5 partitions
> if limit=, = * 9, we shuffle to 9 partitions
> if limit is a prime number, we just fall back to single partition
>
> best regards,
> -zhen
t; the target RDD, I use the input partition to get the corresponding parent
> partition, and get the half elements in the parent partitions as the
> output
> of the computing function.
>
> Thanks,
> Fei
>
> On Sun, Jan 15, 2017 at 11:01 PM, Liang-Chi Hsieh
> viirya@
hich can help get high data locality.
>>
>> Is there anyone who knows how to implement it or any hints for it?
>>
>> Thanks in advance,
>> Fei
>>
>>
>
>
> --
> -- Anastasios Zouzias
>
> azo@.ibm
>
-
Liang-Chi Hsieh | @v
0*10 = 100), once the first group
> finishes the tasks driver will check whether the accumulator value is been
> reached the limit value
> if its reached then no further task will be launched to executers and the
> result will be returned.
>
> Let me know for any furthur suggestion
> Shouldn't we add some check to Client?
>
>
> Thanks,
> Rostyslav
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= toCarryBd.value.get(i + 1).get
}
You may need to make sure the next partition has a value too. Holden has
pointed out before, you need to deal with the case that the previous/next
partition is empty too and go next until you find a non-empty partition.
geoHeil wrote
> Hi Liang-Chi Hsieh,
>
>
functions=[partial_aggregatefunction(key#0, nested#1, nestedArray#2,
>>> nestedObjectArray#3, value#4L, com.[...]uDf@108b121f, 0, 0)],
>>> output=[key#0,internal_col#37])
>>> +- *Sort [key#0 ASC], false, 0
>>>+- Scan Exist
owski/
> Mastering Apache Spark 2.0 https://bit.ly/mastering-apache-spark
> Follow me at https://twitter.com/jaceklaskowski
>
> ---------
> To unsubscribe e-mail:
> dev-unsubscribe@.apache
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Liang-Chi Hsieh | @viirya
Spark Technology Cen
do mapPartitions, it just gives you an empty
iterator as input. You can do what you need. You already return a None when
you find an empty iterator in preparing "toCarry". So I was wondering what
you want to ask in the previous reply.
geoHeil wrote
> Thanks a lot, Holden.
9984, 0.970241])
>>
>> This result has std=sqrt(2/3)
>>
>> Instead it should have resulted other 3 vectors that form std=1 for each
>> column.
>>
>> Adding another vector (4 total) results in 4 scaled vectors that form
>> std= sqrt(3/4) instead of std=1
&g
empty partitions?
> http://stackoverflow.com/questions/41474175/spark-mappartitionswithindex-handling-empty-partitions
>
> Kind regards,
> Georg
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alRow
> class TopKDataType extends UserDefinedType
>
> {
> private final ExpressionEncoder
>
> unboundedEncoder;
> private final List
>
> data;
>
> public Row[] rows() {
> val encoder = resolveAndBind(this.unboundedEncoder);
>
>
lease follow that thread on the Parquet list.
>
> Thanks!
>
> rb
>
> --
> Ryan Blue
> Software Engineer
> Netflix
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> collect(Collectors.toList());
>> val encoder =
>> RowEncoder.apply(schema).resolveAndBind(ScalaUtils.scalaSeq(attributes),
>> SimpleAnalyzer$.MODULE$);
>>
>>
>> ---
>> Regards,
>> Andy
>>
>> On Thu, Jan 5, 2017 at 2:53
, 10]
> at
> org.apache.parquet.hadoop.ParquetFileReader.readAllFootersInParallel(ParquetFileReader.java:248)
>
>
> Please let me know if I am missing anything.
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ntln("Round trip: " + roundTrip.size());
> }
>
> The code fails at the line encoder.fromRow() with the exception:
>> Caused by: java.lang.UnsupportedOperationException: Cannot evaluate
> expression: getcolumnbyordinal(0, IntegerType)
>
> ---
> Regards,
> A
ot.com/users/cloud-fan; 8
> jerryshao https://spark-prs.appspot.com/users/jerryshao; 8
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Forget to say, another option is we can replace readAllFootersInParallel
with our parallel reading logic, so we can ignore corrupt files.
Liang-Chi Hsieh wrote
> Hi,
>
> The method readAllFootersInParallel is implemented in Parquet's
> ParquetFileReader. So the
corruptblock.0 is not a
> Parquet file. expected magic number at tail [80, 65, 82, 49] but found
> [65, 82, 49, 10]
> at
> org.apache.parquet.hadoop.ParquetFileReader.readAllFootersInParallel(ParquetFileReader.java:248)
>
>
> Please let me know if I am missing anything.
-
Lia
evelopers-list.1001551.n3.nabble.com/What-is-mainly-
>> different-from-a-UDT-and-a-spark-internal-type-that-
>> ExpressionEncoder-recognized-tp20370.html
>> Sent from the Apache Spark Developers List mailing list archive at
>> Nabble.com.
>>
>> -
>> To unsubscribe e-
no errors and no
> outputs.
>
> What is the reason for it?
>
> Thanks,
> Fei
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of 100 Range plans, there are 5049 Range(s) needed to go
through. For 200 Range plans, it becomes 20099.
You can see it is not linear relation.
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ing-apache-spark
> Follow me at https://twitter.com/jaceklaskowski
>
> ---------
> To unsubscribe e-mail:
> dev-unsubscribe@.apache
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ht
ools.nsc.interpreter.IMain.interpret(IMain.scala:565)
>> at scala.tools.nsc.interpreter.ILoop.interpretStartingWith(
>> ILoop.scala:807)
>> at scala.tools.nsc.interpreter.ILoop.command(ILoop.scala:681)
>> at scala.tools.nsc.interpreter.ILoop.processLine(ILoop.scala:395)
>> at scala.tools.nsc.i
}
println(s"Took $timeLen miliseconds")
totalTime += timeLen
}
val timeLen2 = time {
val grouped2 = allDF.groupBy("cat1").agg(sum($"v"), count($"cat2"))
grouped2.show()
}
totalTime += timeLen2
println(s"Overall time was $totalTime miliseconds
does the window internally. This was still 8 times lower
> throughput than batch and required a lot of coding and is not a general
> solution.
>
> I am looking for an approach to improve the performance even more
> (preferably to either be on par with batch or a relatively low factor
>
ch.
> Is there a correct way to do such an aggregation on streaming data (using
> dataframes rather than RDD operations).
> Assaf.
>
>
>
> From: Liang-Chi Hsieh [via Apache Spark Developers List] [mailto:
> ml-node+s1001551n20361h80@.nabble
> ]
> Sent: Monday,
n x2)))
...
Your first example just does two aggregation operations. But your second
example like above does this aggregation operations for each iteration. So
the time of second example grows as the iteration increases.
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Liang-Chi Hsieh | @viirya
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-18986.
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Sent from the Apache Spark Developers List mailing list
y1”-> fraction, “key2”-> fraction, …, “keyn”->
> fraction).
>
> I have a question is that why stratified sampling scales poorly with
> different sampling fractions in this context? meanwhile simple random
> sampling scales well with different sampling fractions (I ran experime
-sorted. see here:
> https://github.com/tresata/spark-sorted/blob/master/src/main/scala/com/tresata/spark/sorted/sql/GroupSortedDataset.scala
>
> On Wed, Dec 21, 2016 at 9:44 PM, Liang-Chi Hsieh
> viirya@
> wrote:
>
>>
>> I agreed that to make sure this work, yo
.
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_code").parquet(.....)
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Sent from the Apa
properly installed in all nodes in the
cluster, because those Spark jobs will be launched at the node which the
main driver is running on.
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sure the node running the driver has enough
resources to run them.
I am not sure if you can use `SparkLauncher` to submit them in different
mode, e.g., main driver in client mode, others in cluster mode. Worth
trying.
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Liang-Chi Hsieh | @viirya
Spark Technology Center
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on the
node which the driver runs. It looks weird.
Looks like you try to fetch some data first and do some jobs on the data.
Can't you just do those jobs in the main driver as Spark actions with its
API?
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+--++
|number|collect_list(letter)|
+--++
| 3| [a, b, c]|
| 1| [a, b, c]|
| 2| [a, b, c]|
+--++
I think it should let you do aggregate on sorted data per key.
-
Liang-Ch
Hi,
You can't invoke any RDD actions/transformations inside another
transformations. They must be invoked by the driver.
If I understand your purpose correctly, you can partition your data (i.e.,
`partitionBy`) when writing out to parquet files.
-
Liang-Chi Hsieh | @viirya
Spark
if it is the root cause and
fix it then.
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Sent
-defined JVM object as buffer to hold the input data into your
aggregate function. But you may need to write necessary encoder for the
buffer object.
If you really need this feature, you may open a Jira to ask others' opinion
about this feature.
-
Liang-Chi Hsieh | @viirya
Spark Technology
Hi,
I tried your example with latest Spark master branch and branch-2.0. It
works well.
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like it
might not work much better than brute-force even you set a higher threshold.
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sure the problem is exactly at
columnSimilarities?
E.g,
val exact = mat.columnSimilarities(0.5)
val exactCount = exact.entries.count
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Hi,
There is a plan to add this into Spark ML. Please check out
https://issues.apache.org/jira/browse/SPARK-18023. You can also follow this
jira to get the latest update.
-
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Hi Dongjoon,
I know some people only use Spark SQL with SQL syntax not Dataset API. So I
think it should be useful to provide a way to do this in SQL.
-
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also adjust the
threshold when using DIMSUM.
[1] Reza Bosagh Zadeh and Gunnar Carlsson, "Dimension Independent Matrix
Square using MapReduce (DIMSUM)"
[2] Reza Bosagh Zadeh and Ashish Goel, "Dimension Independent Similarity
Computation"
-
Liang-Chi Hsieh | @viirya
Spa
Hi Nick,
I think it is due to a bug in UnsafeKVExternalSorter. I created a Jira and a
PR for this bug:
https://issues.apache.org/jira/browse/SPARK-18800
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