Starting a new Spark codebase, Python or Scala / Java?

[Brandon White]

Hello all,

I will be starting a new Spark codebase and I would like to get opinions on
using Python over Scala. Historically, the Scala API has always been the
strongest interface to Spark. Is this still true? Are there still many
benefits and additional features in the Scala API that are not available in
the Python API? Are there any performance concerns using the Python API
that do not exist when using the Scala API? Anything else I should know
about?

I appreciate any insight you have on using the Scala API over the Python
API.

Brandon

Re: Using spark package XGBoost

[Brandon White]

The XGBoost integration with Spark is currently only supported for RDDs,
there is a ticket for dataframe and folks calm to be working on it.

On Aug 14, 2016 8:15 PM, "Jacek Laskowski"  wrote:

> Hi,
>
> I've never worked with the library and speaking about sbt setup only.
>
> It appears that the project didn't release 2.11-compatible jars (only
> 2.10) [1] so you need to build the project yourself and uber-jar it
> (using sbt-assembly plugin).
>
> [1] https://spark-packages.org/package/rotationsymmetry/sparkxgboost
>
> Pozdrawiam,
> Jacek Laskowski
> 
> https://medium.com/@jaceklaskowski/
> Mastering Apache Spark 2.0 http://bit.ly/mastering-apache-spark
> Follow me at https://twitter.com/jaceklaskowski
>
>
> On Sun, Aug 14, 2016 at 7:13 AM, janardhan shetty
>  wrote:
> > Any leads how to do acheive this?
> >
> > On Aug 12, 2016 6:33 PM, "janardhan shetty" 
> wrote:
> >>
> >> I tried using  sparkxgboost package in build.sbt file but it failed.
> >> Spark 2.0
> >> Scala 2.11.8
> >>
> >> Error:
> >>  [warn]
> >> http://dl.bintray.com/spark-packages/maven/
> rotationsymmetry/sparkxgboost/0.2.1-s_2.10/sparkxgboost-0.2.
> 1-s_2.10-javadoc.jar
> >>[warn] ::
> >>[warn] ::  FAILED DOWNLOADS::
> >>[warn] :: ^ see resolution messages for details  ^ ::
> >>[warn] ::
> >>[warn] ::
> >> rotationsymmetry#sparkxgboost;0.2.1-s_2.10!sparkxgboost.jar(src)
> >>[warn] ::
> >> rotationsymmetry#sparkxgboost;0.2.1-s_2.10!sparkxgboost.jar(doc)
> >>
> >> build.sbt:
> >>
> >> scalaVersion := "2.11.8"
> >>
> >> libraryDependencies ++= {
> >>   val sparkVersion = "2.0.0-preview"
> >>   Seq(
> >> "org.apache.spark" %% "spark-core" % sparkVersion % "provided",
> >> "org.apache.spark" %% "spark-sql" % sparkVersion % "provided",
> >> "org.apache.spark" %% "spark-streaming" % sparkVersion % "provided",
> >> "org.apache.spark" %% "spark-mllib" % sparkVersion % "provided"
> >>   )
> >> }
> >>
> >> resolvers += "Spark Packages Repo" at
> >> "http://dl.bintray.com/spark-packages/maven;
> >>
> >> libraryDependencies += "rotationsymmetry" % "sparkxgboost" %
> >> "0.2.1-s_2.10"
> >>
> >> assemblyMergeStrategy in assembly := {
> >>   case PathList("META-INF", "MANIFEST.MF")   =>
> >> MergeStrategy.discard
> >>   case PathList("javax", "servlet", xs @ _*) =>
> >> MergeStrategy.first
> >>   case PathList(ps @ _*) if ps.last endsWith ".html" =>
> >> MergeStrategy.first
> >>   case "application.conf"=>
> >> MergeStrategy.concat
> >>   case "unwanted.txt"=>
> >> MergeStrategy.discard
> >>
> >>   case x => val oldStrategy = (assemblyMergeStrategy in assembly).value
> >> oldStrategy(x)
> >>
> >> }
> >>
> >>
> >>
> >>
> >> On Fri, Aug 12, 2016 at 3:35 PM, janardhan shetty <
> janardhan...@gmail.com>
> >> wrote:
> >>>
> >>> Is there a dataframe version of XGBoost in spark-ml ?.
> >>> Has anyone used sparkxgboost package ?
> >>
> >>
> >
>
> -
> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>
>

Setting spark.sql.shuffle.partitions Dynamically

[Brandon White]

Hello,

My platform runs hundreds of Spark jobs every day each with its own
datasize from 20mb to 20TB. This means that we need to set resources
dynamically. One major pain point for doing this is
spark.sql.shuffle.partitions, the number of partitions to use when
shuffling data for joins or aggregations. It is to be arbitrarily hard
coded to 200. The only way to set this config is in the spark submit
command or in the SparkConf before the executor is created.

This creates a lot of problems when I want to set this config dynamically
based on the in memory size of a dataframe. I only know the in memory size
of the dataframe halfway through the spark job. So I would need to stop the
context and recreate it in order to set this config.

Is there any better way to set this? How does  spark.sql.shuffle.partitions
work differently than .repartition?

Brandon

Optimal Amount of Tasks Per size of data in memory

[Brandon White]

What is the best heuristic for setting the number of partitions/task on an
RDD based on the size of the RDD in memory?

The Spark docs say that the number of partitions/tasks should be 2-3x the
number of CPU cores but this does not make sense for all data sizes.
Sometimes, this number is way to much and slows down the executor because
of overhead.

Size of cached dataframe

[Brandon White]

Is there any public API to get the size of a dataframe in cache? It's seen
through the Spark UI but I don't see the API to access this information. Do
I need to build it myself using a forked version of Spark?

Difference between Dataframe and RDD Persisting

[Brandon White]

What is the difference between persisting a dataframe and a rdd? When I
persist my RDD, the UI says it takes 50G or more of memory. When I persist
my dataframe, the UI says it takes 9G or less of memory.

Does the dataframe not persist the actual content? Is it better / faster to
persist a RDD when doing a lot of filter, mapping, and collecting
operations?

Re: What does it mean when a executor has negative active tasks?

[Brandon White]

1.6
On Jun 18, 2016 10:02 AM, "Mich Talebzadeh" <mich.talebza...@gmail.com>
wrote:

> could be a bug as they are no failed jobs. what version of Spark is this?
>
>
> HTH
>
> Dr Mich Talebzadeh
>
>
>
> LinkedIn * 
> https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
> <https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
>
>
>
> http://talebzadehmich.wordpress.com
>
>
>
> On 18 June 2016 at 17:50, Brandon White <bwwintheho...@gmail.com> wrote:
>
>> What does it mean when a executor has negative active tasks?
>>
>>
>>
>>
>> -
>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
>> For additional commands, e-mail: user-h...@spark.apache.org
>>
>
>

Spark ML - Is it safe to schedule two trainings job at the same time or will worker state be corrupted?

[Brandon White]

For example, say I want to train two Linear Regressions and two GBD Tree
Regressions.

Using different threads, Spark allows you to submit jobs at the same time
(see: http://spark.apache.org/docs/latest/job-scheduling.html). If I
schedule two or more training jobs and they are running at the same time:

1) Is there any risk that static worker variables or worker state could
become corrupted leading to incorrect calculations?
2) Is Spark ML designed for running two or more training jobs at the same
time? Is this something the architects consider during implementation?

Thanks,

Brandon

Re: BlockManager crashing applications

[Brandon White]

I'm not quite sure how this is a memory problem. There are no OOM
exceptions and the job only breaks when actions are ran in parallel,
submitted to the scheduler by different threads.

The issue is that the doGetRemote function does not retry when it is denied
access to a cache block.
On May 8, 2016 5:55 PM, "Ashish Dubey" <ashish@gmail.com> wrote:

Brandon,

how much memory are you giving to your executors - did you check if there
were dead executors in your application logs.. Most likely you require
higher memory for executors..

Ashish

On Sun, May 8, 2016 at 1:01 PM, Brandon White <bwwintheho...@gmail.com>
wrote:

> Hello all,
>
> I am running a Spark application which schedules multiple Spark jobs.
> Something like:
>
> val df  = sqlContext.read.parquet("/path/to/file")
>
> filterExpressions.par.foreach { expression =>
>   df.filter(expression).count()
> }
>
> When the block manager fails to fetch a block, it throws an exception
> which eventually kills the exception: http://pastebin.com/2ggwv68P
>
> This code works when I run it on one thread with:
>
> filterExpressions.foreach { expression =>
>   df.filter(expression).count()
> }
>
> But I really need the parallel execution of the jobs. Is there anyway
> around this? It seems like a bug in the BlockManagers doGetRemote function.
> I have tried the HTTP Block Manager as well.
>

BlockManager crashing applications

[Brandon White]

Hello all,

I am running a Spark application which schedules multiple Spark jobs.
Something like:

val df  = sqlContext.read.parquet("/path/to/file")

filterExpressions.par.foreach { expression =>
  df.filter(expression).count()
}

When the block manager fails to fetch a block, it throws an exception which
eventually kills the exception: http://pastebin.com/2ggwv68P

This code works when I run it on one thread with:

filterExpressions.foreach { expression =>
  df.filter(expression).count()
}

But I really need the parallel execution of the jobs. Is there anyway
around this? It seems like a bug in the BlockManagers doGetRemote function.
I have tried the HTTP Block Manager as well.

QueryExecution to String breaks with OOM

[Brandon White]

Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
at java.util.Arrays.copyOf(Arrays.java:2367)
at
java.lang.AbstractStringBuilder.expandCapacity(AbstractStringBuilder.java:130)
at
java.lang.AbstractStringBuilder.ensureCapacityInternal(AbstractStringBuilder.java:114)
at
java.lang.AbstractStringBuilder.append(AbstractStringBuilder.java:415)
at java.lang.StringBuilder.append(StringBuilder.java:132)
at scala.StringContext.standardInterpolator(StringContext.scala:123)
at scala.StringContext.s(StringContext.scala:90)
at
org.apache.spark.sql.SQLContext$QueryExecution.toString(SQLContext.scala:947)
at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:52)
at
org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation.run(InsertIntoHadoopFsRelation.scala:108)
at
org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:57)
at
org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:57)
at
org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:69)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:140)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:138)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
at
org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:138)
at
org.apache.spark.sql.SQLContext$QueryExecution.toRdd$lzycompute(SQLContext.scala:933)
at
org.apache.spark.sql.SQLContext$QueryExecution.toRdd(SQLContext.scala:933)
at
org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:197)
at
org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:146)
at
org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:137)
at
org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:304)

I have a dataframe which I am running a ton of filters on. When I try to
save it, my job runs out of memory.

Any idea how can I fix this?

Is DataFrame randomSplit Deterministic?

[Brandon White]

If I have the same data, the same ratios, and same sample seed, will I get
the same splits every time?

Re: Dataframe saves for a large set but throws OOM for a small dataset

[Brandon White]

randomSplit instead of randomSample
On Apr 30, 2016 1:51 PM, "Brandon White" <bwwintheho...@gmail.com> wrote:

> val df = globalDf
> val filteredDfs= filterExpressions.map { expr =>
>   val filteredDf = df.filter(expr)
>   val samples = filteredDf.randomSample([.7, .3])
>(samples(0), samples(1)
> }
>
> val largeDfs = filteredDfs.(_._1)
> val smallDfs = filteredDfs(_._2)
>
> val unionedLargeDfs = tailRecursiveUnionAll(largeDfs.tail, largeDfs.head)
> val unionedSmallDfs = tailRecursiveUnionAll(smallDfs.tail, smallDfs.head)
>
> unionedLargeDfs.write.parquet(output) // works fine
> unionedSmallDfs.write.parquet(output)  // breaks with OOM stack trace in
> first thread
>
> There is no skew here. I am using Spark 1.5.1 with 80 executors with 7g
> memory.
> On Apr 30, 2016 1:22 PM, "Ted Yu" <yuzhih...@gmail.com> wrote:
>
>> Can you provide a bit more information:
>>
>> Does the smaller dataset have skew ?
>>
>> Which release of Spark are you using ?
>>
>> How much memory did you specify ?
>>
>> Thanks
>>
>> On Sat, Apr 30, 2016 at 1:17 PM, Brandon White <bwwintheho...@gmail.com>
>> wrote:
>>
>>> Hello,
>>>
>>> I am writing to datasets. One dataset is x2 larger than the other. Both
>>> datasets are written to parquet the exact same way using
>>>
>>> df.write.mode("Overwrite").parquet(outputFolder)
>>>
>>> The smaller dataset OOMs while the larger dataset writes perfectly fine.
>>> Here is the stack trace: Any ideas what is going on here and how I can fix
>>> it?
>>>
>>> Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
>>> at java.util.Arrays.copyOf(Arrays.java:2367)
>>> at
>>> java.lang.AbstractStringBuilder.expandCapacity(AbstractStringBuilder.java:130)
>>> at
>>> java.lang.AbstractStringBuilder.ensureCapacityInternal(AbstractStringBuilder.java:114)
>>> at java.lang.AbstractStringBuilder.append(AbstractStringBuilder.java:415)
>>> at java.lang.StringBuilder.append(StringBuilder.java:132)
>>> at scala.StringContext.standardInterpolator(StringContext.scala:123)
>>> at scala.StringContext.s(StringContext.scala:90)
>>> at
>>> org.apache.spark.sql.SQLContext$QueryExecution.toString(SQLContext.scala:947)
>>> at
>>> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:52)
>>> at
>>> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation.run(InsertIntoHadoopFsRelation.scala:108)
>>> at
>>> org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:57)
>>> at
>>> org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:57)
>>> at
>>> org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:69)
>>> at
>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:140)
>>> at
>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:138)
>>> at
>>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
>>> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:138)
>>> at
>>> org.apache.spark.sql.SQLContext$QueryExecution.toRdd$lzycompute(SQLContext.scala:933)
>>> at
>>> org.apache.spark.sql.SQLContext$QueryExecution.toRdd(SQLContext.scala:933)
>>> at
>>> org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:197)
>>> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:146)
>>> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:137)
>>> at
>>> org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:304)
>>>
>>
>>

Dataframe saves for a large set but throws OOM for a small dataset

[Brandon White]

Hello,

I am writing to datasets. One dataset is x2 larger than the other. Both
datasets are written to parquet the exact same way using

df.write.mode("Overwrite").parquet(outputFolder)

The smaller dataset OOMs while the larger dataset writes perfectly fine.
Here is the stack trace: Any ideas what is going on here and how I can fix
it?

Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
at java.util.Arrays.copyOf(Arrays.java:2367)
at
java.lang.AbstractStringBuilder.expandCapacity(AbstractStringBuilder.java:130)
at
java.lang.AbstractStringBuilder.ensureCapacityInternal(AbstractStringBuilder.java:114)
at java.lang.AbstractStringBuilder.append(AbstractStringBuilder.java:415)
at java.lang.StringBuilder.append(StringBuilder.java:132)
at scala.StringContext.standardInterpolator(StringContext.scala:123)
at scala.StringContext.s(StringContext.scala:90)
at
org.apache.spark.sql.SQLContext$QueryExecution.toString(SQLContext.scala:947)
at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:52)
at
org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation.run(InsertIntoHadoopFsRelation.scala:108)
at
org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:57)
at
org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:57)
at
org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:69)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:140)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:138)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:138)
at
org.apache.spark.sql.SQLContext$QueryExecution.toRdd$lzycompute(SQLContext.scala:933)
at
org.apache.spark.sql.SQLContext$QueryExecution.toRdd(SQLContext.scala:933)
at
org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:197)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:146)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:137)
at org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:304)

DataFrame to DataSet without Predefined Class

[Brandon White]

I am reading parquet files into a dataframe. The schema varies depending on
the data so I have no way to write a predefined class.

Is there any way to go from DataFrame to DataSet without predefined a case
class? Can I build a class from my dataframe schema?

How can I bucketize / group a DataFrame from parquet files?

[Brandon White]

I am creating a dataFrame from parquet files. The schema is based on the
parquet files, I do not know it before hand. What I want to do is group the
entire DF into buckets based on a column.

val df = sqlContext.read.parquet("/path/to/files")
val groupedBuckets: DataFrame[String, Array[Rows]] =
df.groupBy($"columnName")

I know this does not work because the DataFrame's groupBy is only used for
aggregate functions. I cannot convert my DataFrame to a DataSet because I
do not have a case class for the DataSet schema. The only thing I can do is
convert the df to an RDD[Rows] and try to deal with the types. This is ugly
and difficult.

Is there any better way? Can I convert a DataFrame to a DataSet without a
predefined case class?

Brandon

Re: subscribe

[Brandon White]

https://www.youtube.com/watch?v=umDr0mPuyQc

On Sat, Aug 22, 2015 at 8:01 AM, Ted Yu yuzhih...@gmail.com wrote:

 See http://spark.apache.org/community.html

 Cheers

 On Sat, Aug 22, 2015 at 2:51 AM, Lars Hermes 
 li...@hermes-it-consulting.de wrote:

 subscribe

 -
 To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
 For additional commands, e-mail: user-h...@spark.apache.org

Re: How to save a string to a text file ?

[Brandon White]

Convert it to a rdd then save the rdd to a file

val str = dank memes
sc.parallelize(List(str)).saveAsTextFile(str.txt)

On Fri, Aug 14, 2015 at 7:50 PM, go canal goca...@yahoo.com.invalid wrote:

 Hello again,
 online resources have sample code for writing RDD to a file, but I have a
 simple string, how to save to a text file ? (my data is a DenseMatrix
 actually)

 appreciate any help !

 thanks, canal

Re: subscribe

[Brandon White]

https://www.youtube.com/watch?v=H07zYvkNYL8

On Mon, Aug 10, 2015 at 10:55 AM, Ted Yu yuzhih...@gmail.com wrote:

 Please take a look at the first section of
 https://spark.apache.org/community

 Cheers

 On Mon, Aug 10, 2015 at 10:54 AM, Phil Kallos phil.kal...@gmail.com
 wrote:

 please

Spark SQL Hive - merge small files

[Brandon White]

Hello,

I would love to have hive merge the small files in my managed hive context
after every query. Right now, I am setting the hive configuration in my
Spark Job configuration but hive is not managing the files. Do I need to
set the hive fields in around place? How do you set Hive configurations in
Spark?

Here is what I'd like to set

hive.merge.mapfilestrue
hive.merge.mapredfilestrue
hive.merge.size.per.task25600
hive.merge.smallfiles.avgsize1600

Re: Spark SQL Hive - merge small files

[Brandon White]

So there is no good way to merge spark files in a manage hive table right
now?

On Wed, Aug 5, 2015 at 10:02 AM, Michael Armbrust mich...@databricks.com
wrote:

 This feature isn't currently supported.

 On Wed, Aug 5, 2015 at 8:43 AM, Brandon White bwwintheho...@gmail.com
 wrote:

 Hello,

 I would love to have hive merge the small files in my managed hive
 context after every query. Right now, I am setting the hive configuration
 in my Spark Job configuration but hive is not managing the files. Do I need
 to set the hive fields in around place? How do you set Hive configurations
 in Spark?

 Here is what I'd like to set

 hive.merge.mapfilestrue
 hive.merge.mapredfilestrue
 hive.merge.size.per.task25600
 hive.merge.smallfiles.avgsize1600

Re: Schema evolution in tables

[Brandon White]

Sim did you find anything? :)

On Sun, Jul 26, 2015 at 9:31 AM, sim s...@swoop.com wrote:

 The schema merging
 http://spark.apache.org/docs/latest/sql-programming-guide.html#schema-merging
 section of the Spark SQL documentation shows an example of schema evolution
 in a partitioned table.

 Is this functionality only available when creating a Spark SQL table?

 dataFrameWithEvolvedSchema.saveAsTable(my_table, SaveMode.Append) fails
 with

 java.lang.RuntimeException: Relation[ ... ] 
 org.apache.spark.sql.parquet.ParquetRelation2@83a73a05
  requires that the query in the SELECT clause of the INSERT INTO/OVERWRITE 
 statement generates the same number of columns as its schema.

 What is the Spark SQL idiom for appending data to a table while managing
 schema evolution?
 --
 View this message in context: Schema evolution in tables
 http://apache-spark-user-list.1001560.n3.nabble.com/Schema-evolution-in-tables-tp23999.html
 Sent from the Apache Spark User List mailing list archive
 http://apache-spark-user-list.1001560.n3.nabble.com/ at Nabble.com.

Turn Off Compression for Textfiles

[Brandon White]

How do you turn off gz compression for saving as textfiles? Right now, I am
reading ,gz files and it is saving them as .gz. I would love to not
compress them when I save.

1) DStream.saveAsTextFiles() //no compression

2) RDD.saveAsTextFile() //no compression

Any ideas?

Combining Spark Files with saveAsTextFile

[Brandon White]

What is the best way to make saveAsTextFile save as only a single file?

What happens when you create more DStreams then nodes in the cluster?

[Brandon White]

Since one input dstream creates one receiver and one receiver uses one
executor / node.

What happens if you create more Dstreams than nodes in the cluster?

Say I have 30 Dstreams on a 15 node cluster.

Will ~10 streams get assigned to ~10 executors / nodes then the other ~20
streams will be queued for resources or will the other streams just fail
and never run?

Re: Has anybody ever tried running Spark Streaming on 500 text streams?

[Brandon White]

Tathagata,

Could the bottleneck possibility be the number of executor nodes in our
cluster? Since we are creating 500 Dstreams based off 500 textfile
directories, do we need at least 500 executors / nodes to be receivers for
each one of the streams?

On Tue, Jul 28, 2015 at 6:09 PM, Tathagata Das t...@databricks.com wrote:

 @Ashwin: You could append the topic in the data.

 val kafkaStreams = topics.map { topic =
 KafkaUtils.createDirectStream(topic...).map { x = (x, topic) }
 }
 val unionedStream = context.union(kafkaStreams)


 @Brandon:
 I dont recommend it, but you could do something crazy like use the
 foreachRDD to farm out the jobs to a threadpool, but the final foreachRDD
 waits for all the jobs to complete.

 manyDStreams.foreach { dstream =
dstream1.foreachRDD { rdd =
 // Add runnable that runs the job on RDD to threadpool
 // This does not wait for the job to finish
   }
 }

 anyOfTheManyDStreams.foreachRDD { _ =
 // wait for all the current batch's jobs in the threadpool to
 complete.

 }


 This would run all the Spark jobs in the batch in parallel in thread pool,
 but it would also make sure all the jobs finish before the batch is marked
 as completed.

 On Tue, Jul 28, 2015 at 4:05 PM, Brandon White bwwintheho...@gmail.com
 wrote:

 Thank you Tathagata. My main use case for the 500 streams is to append
 new elements into their corresponding Spark SQL tables. Every stream is
 mapped to a table so I'd like to use the streams to appended the new rdds
 to the table. If I union all the streams, appending new elements becomes a
 nightmare. So there is no other way to parallelize something like the
 following? Will this still run sequence or timeout?

 //500 streams
 streams.foreach { stream =
   stream.foreachRDD { rdd =
 val df = sqlContext.jsonRDD(rdd)
 df.saveAsTable(streamTuple._1, SaveMode.Append)

   }
 }

 On Tue, Jul 28, 2015 at 3:42 PM, Tathagata Das t...@databricks.com
 wrote:

 I dont think any one has really run 500 text streams.
 And parSequences do nothing out there, you are only parallelizing the
 setup code which does not really compute anything. Also it setsup 500
 foreachRDD operations that will get executed in each batch sequentially, so
 does not make sense. The write way to parallelize this is union all the
 streams.

 val streams = streamPaths.map { path =
   ssc.textFileStream(path)
 }
 val unionedStream = streamingContext.union(streams)
 unionedStream.foreachRDD { rdd =
   // do something
 }

 Then there is only one foreachRDD executed in every batch that will
 process in parallel all the new files in each batch interval.
 TD


 On Tue, Jul 28, 2015 at 3:06 PM, Brandon White bwwintheho...@gmail.com
 wrote:

 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 scale? What would be the limiting factor to
 performance? What is the best way to parallelize this? Any other ideas on
 design?

Re: unsubscribe

[Brandon White]

https://www.youtube.com/watch?v=JncgoPKklVE

On Thu, Jul 30, 2015 at 1:30 PM, ziqiu...@accenture.com wrote:



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Does Spark Streaming need to list all the files in a directory?

[Brandon White]

Is this a known bottle neck for Spark Streaming textFileStream? Does it
need to list all the current files in a directory before he gets the new
files? Say I have 500k files in a directory, does it list them all in order
to get the new files?

Re: unsubscribe

[Brandon White]

NO!

On Tue, Jul 28, 2015 at 5:03 PM, Harshvardhan Chauhan ha...@gumgum.com
wrote:



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Re: Has anybody ever tried running Spark Streaming on 500 text streams?

[Brandon White]

Thank you Tathagata. My main use case for the 500 streams is to append new
elements into their corresponding Spark SQL tables. Every stream is mapped
to a table so I'd like to use the streams to appended the new rdds to the
table. If I union all the streams, appending new elements becomes a
nightmare. So there is no other way to parallelize something like the
following? Will this still run sequence or timeout?

//500 streams
streams.foreach { stream =
  stream.foreachRDD { rdd =
val df = sqlContext.jsonRDD(rdd)
df.saveAsTable(streamTuple._1, SaveMode.Append)

  }
}

On Tue, Jul 28, 2015 at 3:42 PM, Tathagata Das t...@databricks.com wrote:

 I dont think any one has really run 500 text streams.
 And parSequences do nothing out there, you are only parallelizing the
 setup code which does not really compute anything. Also it setsup 500
 foreachRDD operations that will get executed in each batch sequentially, so
 does not make sense. The write way to parallelize this is union all the
 streams.

 val streams = streamPaths.map { path =
   ssc.textFileStream(path)
 }
 val unionedStream = streamingContext.union(streams)
 unionedStream.foreachRDD { rdd =
   // do something
 }

 Then there is only one foreachRDD executed in every batch that will
 process in parallel all the new files in each batch interval.
 TD


 On Tue, Jul 28, 2015 at 3:06 PM, Brandon White bwwintheho...@gmail.com
 wrote:

 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 scale? What would be the limiting factor to
 performance? What is the best way to parallelize this? Any other ideas on
 design?

Has anybody ever tried running Spark Streaming on 500 text streams?

[Brandon White]

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 scale? What would be the limiting factor to
performance? What is the best way to parallelize this? Any other ideas on
design?

Re: Programmatically launch several hundred Spark Streams in parallel

[Brandon White]

THanks. Sorry the last section was supposed be

streams.par.foreach { nameAndStream =
  nameAndStream._2.foreachRDD { rdd =
df = sqlContext.jsonRDD(rdd)

df.insertInto(stream._1)
  }
}

ssc.start()


On Fri, Jul 24, 2015 at 10:39 AM, Dean Wampler deanwamp...@gmail.com
wrote:

 You don't need the par (parallel) versions of the Scala collections,
 actually, Recall that you are building a pipeline in the driver, but it
 doesn't start running cluster tasks until ssc.start() is called, at which
 point Spark will figure out the task parallelism. In fact, you might as
 well do the foreachRDD call within the initial map. No need for the streams
 collection, unless you need it for something else. Test it out to make sure
 I'm not wrong ;)

 However, I'm a little confused by the per-stream logic. It looks like
 you're using foreachRDD to dump each input stream into the same output
 location stream._1. True? If it's a directory, you'll get an error that
 it already exists for the *second* stream in streams. If you're just
 funneling all 500 inputs into the same output location, how about using
 DStream.union to combine all the input streams into one, then have one
 foreachRDD to write output?

 Dean Wampler, Ph.D.
 Author: Programming Scala, 2nd Edition
 http://shop.oreilly.com/product/0636920033073.do (O'Reilly)
 Typesafe http://typesafe.com
 @deanwampler http://twitter.com/deanwampler
 http://polyglotprogramming.com

 On Fri, Jul 24, 2015 at 11:23 AM, Brandon White bwwintheho...@gmail.com
 wrote:

 Hello,

 So I have about 500 Spark Streams and I want to know the fastest and most
 reliable way to process each of them. Right now, I am creating and process
 them in a list:

 val ssc = new StreamingContext(sc, Minutes(10))


 val streams = paths.par.map { nameAndPath =
   (path._1, ssc.textFileStream(path._1))
 }

 streams.par.foreach { nameAndStream =
   streamTuple.foreachRDD { rdd =
 df = sqlContext.jsonRDD(rdd)

 df.insertInto(stream._1)
   }
 }

 ssc.start()



 Is this the best way to do this? Are there any better faster methods?

Spark SQL Table Caching

[Brandon White]

A few questions about caching a table in Spark SQL.

1) Is there any difference between caching the dataframe and the table?

df.cache() vs sqlContext.cacheTable(tableName)

2) Do you need to warm up the cache before seeing the performance
benefits? Is the cache LRU? Do you need to run some queries on the table
before it is cached in memory?

3) Is caching the table much faster than .saveAsTable? I am only seeing a
10 %- 20% performance increase.

DataFrame Union not passing optimizer assertion

[Brandon White]

Hello! So I am doing a union of two dataframes with the same schema but a
different number of rows. However, I am unable to pass an assertion. I
think it is this one here
https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
but I am not sure. Any ideas why this assertion isn't passing?

java.lang.AssertionError: assertion failed at
scala.Predef$.assert(Predef.scala:165) at
org.apache.spark.sql.catalyst.optimizer.UnionPushdown$.buildRewrites(Optimizer.scala:72)
at
org.apache.spark.sql.catalyst.optimizer.UnionPushdown$$anonfun$apply$1.applyOrElse(Optimizer.scala:102)
at
org.apache.spark.sql.catalyst.optimizer.UnionPushdown$$anonfun$apply$1.applyOrElse(Optimizer.scala:92)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:188)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:188)
at
org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:51)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:187)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:208)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) at
scala.collection.Iterator$class.foreach(Iterator.scala:727) at
scala.collection.AbstractIterator.foreach(Iterator.scala:1157) at
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273) at
scala.collection.AbstractIterator.to(Iterator.scala:1157) at
scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157) at
scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1157) at
org.apache.spark.sql.catalyst.trees.TreeNode.transformChildrenDown(TreeNode.scala:238)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:193)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:178)
at
org.apache.spark.sql.catalyst.optimizer.UnionPushdown$.apply(Optimizer.scala:92)
at
org.apache.spark.sql.catalyst.optimizer.UnionPushdown$.apply(Optimizer.scala:66)
at
org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1$$anonfun$apply$2.apply(RuleExecutor.scala:61)
at
org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1$$anonfun$apply$2.apply(RuleExecutor.scala:59)
at
scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:51)
at
scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:60)
at scala.collection.mutable.WrappedArray.foldLeft(WrappedArray.scala:34) at
org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1.apply(RuleExecutor.scala:59)
at
org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1.apply(RuleExecutor.scala:51)
at scala.collection.immutable.List.foreach(List.scala:318) at
org.apache.spark.sql.catalyst.rules.RuleExecutor.apply(RuleExecutor.scala:51)
at
org.apache.spark.sql.SQLContext$QueryExecution.optimizedPlan$lzycompute(SQLContext.scala:1087)
at
org.apache.spark.sql.SQLContext$QueryExecution.optimizedPlan(SQLContext.scala:1087)
at
org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan$lzycompute(SQLContext.scala:1092)
at
org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan(SQLContext.scala:1090)
at
org.apache.spark.sql.SQLContext$QueryExecution.executedPlan$lzycompute(SQLContext.scala:1096)
at
org.apache.spark.sql.SQLContext$QueryExecution.executedPlan(SQLContext.scala:1096)
at org.apache.spark.sql.DataFrame.collect(DataFrame.scala:887)

Nullpointer when saving as table with a timestamp column type

[Brandon White]

So I have a very simple dataframe that looks like

df: [name:String, Place:String, time: time:timestamp]

I build this java.sql.Timestamp from a string and it works really well
expect when I call saveAsTable(tableName) on this df. Without the
timestamp, it saves fine but with the timestamp, it throws

java.lang.NullPointerException Driver stacktrace: at
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1230)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1219)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1218)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) at
org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1218)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:719)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:719)
at scala.Option.foreach(Option.scala:236) at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:719)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1419)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1380)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)


Any ideas how I can get around this?

Running foreach on a list of rdds in parallel

[Brandon White]

Hello,

I have a list of rdds

List(rdd1, rdd2, rdd3,rdd4)

I would like to save these rdds in parallel. Right now, it is running each
operation sequentially. I tried using a rdd of rdd but that does not work.

list.foreach { rdd =
  rdd.saveAsTextFile(/tmp/cache/)
}

Any ideas?

How do you access a cached Spark SQL Table from a JBDC connection?

[Brandon White]

Hello there,

I have a JBDC connection setup to my Spark cluster but I cannot see the
tables that I cache in memory. The only tables I can see are those that are
in my Hive instance. I use a HiveContext to register a table and cache it
in memory. How can I enable my JBDC connection to query this in memory
table?

Brandon

Re: Spark Streaming - Inserting into Tables

[Brandon White]

Hi Yin,

Yes there were no new rows. I fixed it by doing a .remember on the context.
Obviously, this is not ideal.

On Sun, Jul 12, 2015 at 6:31 PM, Yin Huai yh...@databricks.com wrote:

 Hi Brandon,

 Can you explain what did you mean by It simply does not work? You did
 not see new data files?

 Thanks,

 Yin

 On Fri, Jul 10, 2015 at 11:55 AM, Brandon White bwwintheho...@gmail.com
 wrote:

 Why does this not work? Is insert into broken in 1.3.1? It does not throw
 any errors, fail, or throw exceptions. It simply does not work.


 val ssc = new StreamingContext(sc, Minutes(10))

 val currentStream = ssc.textFileStream(ss3://textFileDirectory/)
 val dayBefore = sqlContext.jsonFile(ss3://textFileDirectory/)

 dayBefore.saveAsParquetFile(/tmp/cache/dayBefore.parquet)
 val parquetFile = sqlContext.parquetFile(/tmp/cache/dayBefore.parquet)
 parquetFile.registerTempTable(rideaccepted)

 currentStream.foreachRDD { rdd =
   val df = sqlContext.jsonRDD(rdd)
   df.insertInto(rideaccepted)
 }

 ssc.start()


 Or this?

 val ssc = new StreamingContext(sc, Minutes(10))
 val currentStream = ssc.textFileStream(s3://textFileDirectory)
 val day = sqlContext.jsonFile(s3://textFileDirectory)
 day.registerTempTable(rideaccepted)


 currentStream.foreachRDD { rdd =
   val df = sqlContext.jsonRDD(rdd)
   df.registerTempTable(tmp_rideaccepted)
   sqlContext.sql(insert into table rideaccepted select * from 
 tmp_rideaccepted)
 }

 ssc.start()


 or this?

 val ssc = new StreamingContext(sc, Minutes(10))

 val currentStream = ssc.textFileStream(ss3://textFileDirectory/)
 val dayBefore = sqlContext.jsonFile(ss3://textFileDirectory/)

 dayBefore..registerTempTable(rideaccepted)

 currentStream.foreachRDD { rdd =
   val df = sqlContext.jsonRDD(rdd)
   df.insertInto(rideaccepted)
 }

 ssc.start()

Spark Streaming - Inserting into Tables

[Brandon White]

Why does this not work? Is insert into broken in 1.3.1? It does not throw
any errors, fail, or throw exceptions. It simply does not work.


val ssc = new StreamingContext(sc, Minutes(10))

val currentStream = ssc.textFileStream(ss3://textFileDirectory/)
val dayBefore = sqlContext.jsonFile(ss3://textFileDirectory/)

dayBefore.saveAsParquetFile(/tmp/cache/dayBefore.parquet)
val parquetFile = sqlContext.parquetFile(/tmp/cache/dayBefore.parquet)
parquetFile.registerTempTable(rideaccepted)

currentStream.foreachRDD { rdd =
  val df = sqlContext.jsonRDD(rdd)
  df.insertInto(rideaccepted)
}

ssc.start()


Or this?

val ssc = new StreamingContext(sc, Minutes(10))
val currentStream = ssc.textFileStream(s3://textFileDirectory)
val day = sqlContext.jsonFile(s3://textFileDirectory)
day.registerTempTable(rideaccepted)


currentStream.foreachRDD { rdd =
  val df = sqlContext.jsonRDD(rdd)
  df.registerTempTable(tmp_rideaccepted)
  sqlContext.sql(insert into table rideaccepted select * from
tmp_rideaccepted)
}

ssc.start()


or this?

val ssc = new StreamingContext(sc, Minutes(10))

val currentStream = ssc.textFileStream(ss3://textFileDirectory/)
val dayBefore = sqlContext.jsonFile(ss3://textFileDirectory/)

dayBefore..registerTempTable(rideaccepted)

currentStream.foreachRDD { rdd =
  val df = sqlContext.jsonRDD(rdd)
  df.insertInto(rideaccepted)
}

ssc.start()

What is faster for SQL table storage, On-Heap or off-heap?

[Brandon White]

Is the read / aggregate performance better when caching Spark SQL tables
on-heap with sqlContext.cacheTable() or off heap by saving it to Tachyon?

Has anybody tested this? Any theories?

S3 vs HDFS

[Brandon White]

Are there any significant performance differences between reading text
files from S3 and hdfs?

Re: Parallelizing multiple RDD / DataFrame creation in Spark

[Brandon White]

The point of running them in parallel would be faster creation of the
tables. Has anybody been able to efficiently parallelize something like
this in Spark?
On Jul 8, 2015 12:29 AM, Akhil Das ak...@sigmoidanalytics.com wrote:

 Whats the point of creating them in parallel? You can multi-thread it run
 it in parallel though.

 Thanks
 Best Regards

 On Wed, Jul 8, 2015 at 5:34 AM, Brandon White bwwintheho...@gmail.com
 wrote:

 Say I have a spark job that looks like following:

 def loadTable1() {
   val table1 = sqlContext.jsonFile(ss3://textfiledirectory/)
   table1.cache().registerTempTable(table1)}
 def loadTable2() {
   val table2 = sqlContext.jsonFile(ss3://testfiledirectory2/)
   table2.cache().registerTempTable(table2)}

 def loadAllTables() {
   loadTable1()
   loadTable2()}

 loadAllTables()

 How do I parallelize this Spark job so that both tables are created at
 the same time or in parallel?

Re: Real-time data visualization with Zeppelin

[Brandon White]

Can you use a con job to update it every X minutes?

On Wed, Jul 8, 2015 at 2:23 PM, Ganelin, Ilya ilya.gane...@capitalone.com
wrote:

 Hi all – I’m just wondering if anyone has had success integrating Spark
 Streaming with Zeppelin and actually dynamically updating the data in near
 real-time. From my investigation, it seems that Zeppelin will only allow
 you to display a snapshot of data, not a continuously updating table. Has
 anyone figured out if there’s a way to loop a display command or how to
 provide a mechanism to continuously update visualizations?

 Thank you,
 Ilya Ganelin


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 transmitted herewith is intended only for use by the individual or entity
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Re: Spark query

[Brandon White]

Convert the column to a column of java Timestamps. Then you can do the
following

import java.sql.Timestamp
import java.util.Calendar
def date_trunc(timestamp:Timestamp, timeField:String) = {
  timeField match {
case hour =
  val cal = Calendar.getInstance()
  cal.setTimeInMillis(timestamp.getTime())
  cal.get(Calendar.HOUR_OF_DAY)

case day =
  val cal = Calendar.getInstance()
  cal.setTimeInMillis(timestamp.getTime())
  cal.get(Calendar.DAY)
  }
}

sqlContext.udf.register(date_trunc, date_trunc _)

On Wed, Jul 8, 2015 at 9:23 PM, Harish Butani rhbutani.sp...@gmail.com
wrote:

 try the spark-datetime package:
 https://github.com/SparklineData/spark-datetime
 Follow this example
 https://github.com/SparklineData/spark-datetime#a-basic-example to get
 the different attributes of a DateTime.

 On Wed, Jul 8, 2015 at 9:11 PM, prosp4300 prosp4...@163.com wrote:

 As mentioned in Spark sQL programming guide, Spark SQL support Hive UDFs,
 please take a look below builtin UDFs of Hive, get day of year should be as
 simply as existing RDBMS

 https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF#LanguageManualUDF-DateFunctions


 At 2015-07-09 12:02:44, Ravisankar Mani rrav...@gmail.com wrote:

 Hi everyone,

 I can't get 'day of year'  when using spark query. Can you help any way
 to achieve day of year?

 Regards,
 Ravi

Parallelizing multiple RDD / DataFrame creation in Spark

[Brandon White]

Say I have a spark job that looks like following:

def loadTable1() {
  val table1 = sqlContext.jsonFile(ss3://textfiledirectory/)
  table1.cache().registerTempTable(table1)}
def loadTable2() {
  val table2 = sqlContext.jsonFile(ss3://testfiledirectory2/)
  table2.cache().registerTempTable(table2)}

def loadAllTables() {
  loadTable1()
  loadTable2()}

loadAllTables()

How do I parallelize this Spark job so that both tables are created at the
same time or in parallel?

Why can I not insert into TempTables in Spark SQL?

[Brandon White]

Why does this not work? Is insert into broken in 1.3.1?


val ssc = new StreamingContext(sc, Minutes(10))

val currentStream = ssc.textFileStream(ss3://textFileDirectory/)
val dayBefore = sqlContext.jsonFile(ss3://textFileDirectory/)

dayBefore.saveAsParquetFile(/tmp/cache/dayBefore.parquet)
val parquetFile = sqlContext.parquetFile(/tmp/cache/dayBefore.parquet)
parquetFile.registerTempTable(rideaccepted)

currentStream.foreachRDD { rdd =
  val df = sqlContext.jsonRDD(rdd)
  df.insertInto(rideaccepted)
}

ssc.start()


Or this?

val ssc = new StreamingContext(sc, Minutes(10))

val currentStream = ssc.textFileStream(ss3://textFileDirectory/)
val dayBefore = sqlContext.jsonFile(ss3://textFileDirectory/)

dayBefore..registerTempTable(rideaccepted)

currentStream.foreachRDD { rdd =
  val df = sqlContext.jsonRDD(rdd)
  df.insertInto(rideaccepted)
}

ssc.start()

Grouping elements in a RDD

[Brandon White]

How would you do a .grouped(10) on a RDD, is it possible? Here is an
example for a Scala list

scala List(1,2,3,4).grouped(2).toList
res1: List[List[Int]] = List(List(1, 2), List(3, 4))

Would like to group n elements.