Starting a new Spark codebase, Python or Scala / Java?
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
Encrypting Airflow Communications
>From what I see, Airflow communicates with a couple sources: 1) SQL Store 2) Celery Broker Does Airflow have any configurations which make it easy to encrypt all of its communications or do we need to build custom solutions into Airflow? -- This e-mail is private and confidential and is for the addressee only. If misdirected, please notify us by telephone, confirming that it has been deleted from your system and any hard copies destroyed. You are strictly prohibited from using, printing, distributing or disseminating it or any information contained in it save to the intended recipient.
Tracking metrics in a task
Hello! Airflow does a great job of tracking metrics at the task level and I am wondering if there is any support for tracking metrics within a task. Say I have a task which downloads data, processes it, then stores it. Are there any Airflow features which allow me to track how long these subtasks take? Brandon -- This e-mail is private and confidential and is for the addressee only. If misdirected, please notify us by telephone, confirming that it has been deleted from your system and any hard copies destroyed. You are strictly prohibited from using, printing, distributing or disseminating it or any information contained in it save to the intended recipient.
Re: Using spark package XGBoost
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
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
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
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
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?
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?
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
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
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
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?
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
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
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
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?
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
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 ?
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
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
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
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
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
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
What is the best way to make saveAsTextFile save as only a single file?
Re: Unsubscribe
YOU SHALL NOT PASS! On Aug 3, 2015 1:23 PM, Aries Kay ariesk1...@gmail.com wrote: On Mon, Aug 3, 2015 at 1:00 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) deepuj...@gmail.com wrote: Thanks a lot for all these documents. Appreciate your effort time. On Mon, Aug 3, 2015 at 10:15 AM, Christian Tzolov ctzo...@pivotal.io wrote: ÐΞ€ρ@Ҝ (๏̯͡๏), I've successfully run Zeppelin with Spark on YARN. I'm using Ambari and PivotalHD30. PHD30 is ODP compliant so you should be able to repeat the configuration for HDP (e.g. hortonworks). 1. Before you start with Zeppelin, make sure that your Spark/YARN env. works from the command line (e.g run Pi test). If it doesn't work make sure that the hdp.version is set correctly or you can hardcode the stack.name and stack.version properties as Ambari Custom yarn-site properties (that is what i did). 2. Your Zeppelin should be build with proper Spark and Hadoop versions and YARN support enabled. In my case used this build command: mvn clean package -Pspark-1.4 -Dspark.version=1.4.1 -Dhadoop.version=2.6.0 -Phadoop-2.6 -Pyarn -DskipTests -Pbuild-distr 3. Open the Spark interpreter configuration and set 'master' property to 'yarn-client' ( e.g. master=yarn-client). then press Save. 4. In (conf/zeppelin-env.sh) set HADOOP_CONF_DIR for PHD and HDP it will look like this: export HADOOP_CONF_DIR=/etc/hadoop/conf 5. (optional) i've restarted the zeppelin daemon but i don't think this is required. 6. Make sure that HDFS has /user/zeppelin user folder exists and has HDFS write permissions. Otherwise you can create it like this: sudo -u hdfs hdfs dfs -mkdir /user/zeppelin user sudo -u hdfs hdfs dfs -chown -R zeppelin usert:hdfs /user/zeppelin user Good to go! Cheers, Christian On 3 August 2015 at 17:50, Vadla, Karthik karthik.va...@intel.com wrote: Hi Deepak, I have documented everything here. Please check published document. https://software.intel.com/sites/default/files/managed/bb/bf/Apache-Zeppelin.pdf Thanks Karthik Vadla *From:* ÐΞ€ρ@Ҝ (๏̯͡๏) [mailto:deepuj...@gmail.com] *Sent:* Sunday, August 2, 2015 9:25 PM *To:* users@zeppelin.incubator.apache.org *Subject:* Yarn + Spark + Zepplin ? Hello, I would like to try out Zepplin and hence i got a 7 node Hadoop cluster with spark history server setup. I am able to run sample spark applications on my YARN cluster. I have no clue how to get zepplin to connect to this YARN cluster. Under https://zeppelin.incubator.apache.org/docs/install/install.html i see MASTER to point to spark master. I do not have a spark master running. How do i get Zepplin to be able to read data from YARN cluster ? Please share documentation. Regards, Deepak -- Christian Tzolov http://www.linkedin.com/in/tzolov | Solution Architect, EMEA Practice Team | Pivotal http://pivotal.io/ ctzo...@pivotal.io|+31610285517 -- Deepak
What happens when you create more DStreams then nodes in the cluster?
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?
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
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Does Spark Streaming need to list all the files in a directory?
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
NO! On Tue, Jul 28, 2015 at 5:03 PM, Harshvardhan Chauhan ha...@gumgum.com wrote: -- *Harshvardhan Chauhan* | Software Engineer *GumGum* http://www.gumgum.com/ | *Ads that stick* 310-260-9666 | ha...@gumgum.com
Re: Has anybody ever tried running Spark Streaming on 500 text streams?
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?
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
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
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
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
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
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?
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
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
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?
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
Are there any significant performance differences between reading text files from S3 and hdfs?
Re: Parallelizing multiple RDD / DataFrame creation in Spark
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
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 -- The information contained in this e-mail is confidential and/or proprietary to Capital One and/or its affiliates and may only be used solely in performance of work or services for Capital One. The information transmitted herewith is intended only for use by the individual or entity to which it is addressed. If the reader of this message is not the intended recipient, you are hereby notified that any review, retransmission, dissemination, distribution, copying or other use of, or taking of any action in reliance upon this information is strictly prohibited. If you have received this communication in error, please contact the sender and delete the material from your computer.
Re: Spark query
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
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?
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
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.
[jira] (MNG-4715) version expression constant
[ https://jira.codehaus.org/browse/MNG-4715?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=363737#comment-363737 ] Brandon White commented on MNG-4715: Prior comments on this bug report from Chris Price, Axel Fontaine, Don Brown, Nicolas Grobisa, Cameron Rochester, and Esteban Porcelli describe the same challenges that we experience in our own environment so I won't repeat them here. To mitigate the issues we encountered in our own continuous integration-based development landscape, we implemented a solution that is based on Axel Fontaine's suggestions from http://www.axelfontaine.com/2011/01/maven-releases-on-steroids-adios.html. There are a number of rough edges with our implementation, particularly for install/deploy, but it has enabled us to centralize our version management in a small number of POMs instead of replicating the version in every POM. Our implementation continues to work with maven versions 3.1.1 up to 3.2.5 but we remain concerned that a subsequent maven release will break some of the admittedly fragile hacks that we have introduced. Even if supporting expressions in version fields is the wrong approach according to the core Maven team, it would be really nice if Maven could provide some sort of official solution that allows version metadata to be injected into the POM from outside. Part of Nicolas Grobisa's comment from 2012 nails this point well: The essence of the problem comes from Maven wanting to do everything. In some ways, it is analog to storing the SCM URL inside the POM. For many reasons and in different scenarios, some of the POM information should be kept outside the POM, or at least provide the flexibility for users to do it one way or the other. version expression constant --- Key: MNG-4715 URL: https://jira.codehaus.org/browse/MNG-4715 Project: Maven Issue Type: Improvement Components: Dependencies, POM Affects Versions: 3.0-alpha-6, 3.0-alpha-7, 3.0-beta-1 Environment: eclipse linux xp Reporter: Faruk Assignee: Jason van Zyl Priority: Critical Fix For: 3.2.6 Attachments: untitled.JPG early versions, we define modules versions with expressions, and set them to the parent pom, simply; {code:xml} properties ibb-core-cache.version1.0.1/ibb-core-cache.version ibb-core-util.version1.0.1/ibb-core-util.version /properties {code} and then, we give this property to modules pom as expression , {code:xml} nameik-plug/name packagingjar/packaging version${ibb-core-util.versionn}/version {code} but know , it gives an error you know like this, {noformat} [WARNING] Some problems were encountered while building the effective model for ibb-parent:ibb-modules-parent:pom:1.0.0 [WARNING] 'version' contains an expression but should be a constant. @ ibb-parent:ibb-modules-parent:${ibb-core-jars.version}, C:\dev\ibb\workspace\core\ibb-modules-parent\pom.xml {noformat} but I think that, this enhancement is causes wrong result, think that , if we have i project already developing about 3 years, this project has a lot of modules, and this modules have sub modules , and this sub modules already bound to some other modules not define in your pom, but your updates must be affect them, at this situation, developer want to write the existing version numbers with properties to parent pom, and want to manage them like this. at the attach file below , the close projects are belongs to open projects, but they are the different team developing this. I cant force the other developers to cache their versions, I must use this versions as initial step -- This message was sent by Atlassian JIRA (v6.1.6#6162)
Re: [coreboot] Support for Google CR-48/Atom N455
Thanks Crisit for getting back to me. I have indeed flashed it with flashrom in Ubuntu. I will see what I can do about details of the northbridge. On Mon, Jan 3, 2011 at 12:07 PM, Cristi Magherusan cristi.magheru...@net.utcluj.ro wrote: În Dum, Ianuarie 2, 2011 4:31, Brandon White a scris: Hello all. Someone was accidentally sent a CR-48 that had Windows 7 pre-installed and an actual BIOS instead of Google's EFI. Anways, he uploaded the BIOS, I was able to flash it and after that I installed Ubuntu as a dual boot with Chrome OS. I was wondering if Coreboot was possible on this machine? I have attached everything asked for in the wiki. Thanks, Brandon -- coreboot mailing list: coreboot@coreboot.org http://www.coreboot.org/mailman/listinfo/coreboot Hello, Flashrom seems to support your flash, but writes weren't tested/confirmed to work yet on the flashrom wiki. Did you flash it using Frashrom? The ICH7 southbridge is supported, no idea about the northbridge, you might need doc from Intel, which is hard to get unless you have an NDA. If you are lucky, the SMSC SCH5317 SuperIO might be easy to support, by porting the code for SCH5307 which is already supported. I have no clue about the other superIO chip or what could it be used for. maybe we don't need it at all for system init. Also, thew board might have an Embedded Controller, which might make things even harder. Good luck! Cristi -- coreboot mailing list: coreboot@coreboot.org http://www.coreboot.org/mailman/listinfo/coreboot
[coreboot] Support for Google CR-48/Atom N455
Hello all. Someone was accidentally sent a CR-48 that had Windows 7 pre-installed and an actual BIOS instead of Google's EFI. Anways, he uploaded the BIOS, I was able to flash it and after that I installed Ubuntu as a dual boot with Chrome OS. I was wondering if Coreboot was possible on this machine? I have attached everything asked for in the wiki. Thanks, Brandon flashrom v0.9.2-r1028 on Linux 2.6.35-22-generic (i686), built with libpci 3.0.0, GCC 4.4.4, little endian flashrom is free software, get the source code at http://www.flashrom.org Calibrating delay loop... OS timer resolution is 3 usecs, 811M loops per second, 10 myus = 12 us, 100 myus = 99 us, 1000 myus = 986 us, 1 myus = 10192 us, 12 myus = 22 us, OK. Initializing internal programmer No coreboot table found. DMI string system-manufacturer: IEC DMI string system-product-name: PineTrail DMI string system-version: 0.08 DMI string baseboard-manufacturer: IEC DMI string baseboard-product-name: Base Board Product Name DMI string baseboard-version: Base Board Version DMI string chassis-type: Other Found chipset Intel NM10, enabling flash write... chipset PCI ID is 8086:27bc, 0xfff8/0xffb8 FWH IDSEL: 0x0 0xfff0/0xffb0 FWH IDSEL: 0x0 0xffe8/0xffa8 FWH IDSEL: 0x1 0xffe0/0xffa0 FWH IDSEL: 0x1 0xffd8/0xff98 FWH IDSEL: 0x2 0xffd0/0xff90 FWH IDSEL: 0x2 0xffc8/0xff88 FWH IDSEL: 0x3 0xffc0/0xff80 FWH IDSEL: 0x3 0xff70/0xff30 FWH IDSEL: 0x4 0xff60/0xff20 FWH IDSEL: 0x5 0xff50/0xff10 FWH IDSEL: 0x6 0xff40/0xff00 FWH IDSEL: 0x7 0xfff8/0xffb8 FWH decode enabled 0xfff0/0xffb0 FWH decode enabled 0xffe8/0xffa8 FWH decode enabled 0xffe0/0xffa0 FWH decode enabled 0xffd8/0xff98 FWH decode enabled 0xffd0/0xff90 FWH decode enabled 0xffc8/0xff88 FWH decode enabled 0xffc0/0xff80 FWH decode enabled 0xff70/0xff30 FWH decode enabled 0xff60/0xff20 FWH decode enabled 0xff50/0xff10 FWH decode enabled 0xff40/0xff00 FWH decode enabled Maximum FWH chip size: 0x10 bytes BIOS Lock Enable: disabled, BIOS Write Enable: enabled, BIOS_CNTL is 0x9 Root Complex Register Block address = 0xfed1c000 GCS = 0x40460: BIOS Interface Lock-Down: disabled, BOOT BIOS Straps: 0x1 (SPI) Top Swap : not enabled SPIBAR = 0xfed1c000 + 0x3020 0x00: 0x0004 (SPIS) 0x02: 0x4140 (SPIC) 0x04: 0x (SPIA) 0x08: 0x001615ef (SPID0) 0x0c: 0x (SPID0+4) 0x10: 0x (SPID1) 0x14: 0x (SPID1+4) 0x18: 0x (SPID2) 0x1c: 0x (SPID2+4) 0x20: 0x (SPID3) 0x24: 0x (SPID3+4) 0x28: 0x (SPID4) 0x2c: 0x (SPID4+4) 0x30: 0x (SPID5) 0x34: 0x (SPID5+4) 0x38: 0x (SPID6) 0x3c: 0x (SPID6+4) 0x40: 0x (SPID7) 0x44: 0x (SPID7+4) 0x50: 0x (BBAR) 0x54: 0x5006 (PREOP) 0x56: 0x463b (OPTYPE) 0x58: 0x05d80302 (OPMENU) 0x5c: 0xc79f0190 (OPMENU+4) 0x60: 0x (PBR0) 0x64: 0x (PBR1) 0x68: 0x (PBR2) 0x6c: 0x (PBR3) Programming OPCODES... program_opcodes: preop=5006 optype=463b opmenu=05d80302c79f0190 done SPI Read Configuration: prefetching enabled, caching enabled, OK. This chipset supports the following protocols: SPI. Probing for AMD Am29F010A/B, 128 KB: skipped. Probing for AMD Am29F002(N)BB, 256 KB: skipped. Probing for AMD Am29F002(N)BT, 256 KB: skipped. Probing for AMD Am29F016D, 2048 KB: skipped. Probing for AMD Am29F040B, 512 KB: skipped. Probing for AMD Am29F080B, 1024 KB: skipped. Probing for AMD Am29LV040B, 512 KB: skipped. Probing for AMD Am29LV081B, 1024 KB: skipped. Probing for ASD AE49F2008, 256 KB: skipped. Probing for Atmel AT25DF021, 256 KB: probe_spi_rdid_generic: id1 0xef, id2 0x4016 Probing for Atmel AT25DF041A, 512 KB: probe_spi_rdid_generic: id1 0xef, id2 0x4016 Probing for Atmel AT25DF081, 1024 KB: probe_spi_rdid_generic: id1 0xef, id2 0x4016 Probing for Atmel AT25DF161, 2048 KB: probe_spi_rdid_generic: id1 0xef, id2 0x4016 Probing for Atmel AT25DF321, 4096 KB: probe_spi_rdid_generic: id1 0xef, id2 0x4016 Probing for Atmel AT25DF321A, 4096 KB: probe_spi_rdid_generic: id1 0xef, id2 0x4016 Probing for Atmel AT25DF641, 8192 KB: probe_spi_rdid_generic: id1 0xef, id2 0x4016 Probing for Atmel AT25F512B, 64 KB: probe_spi_rdid_generic: id1 0xef, id2 0x4016 Probing for Atmel AT25FS010, 128 KB: probe_spi_rdid_generic: id1 0xef, id2 0x4016 Probing for Atmel AT25FS040, 512 KB: probe_spi_rdid_generic: id1 0xef, id2 0x4016 Probing for Atmel AT26DF041, 512 KB: probe_spi_rdid_generic: id1 0xef, id2 0x4016 Probing for Atmel AT26DF081A, 1024 KB: probe_spi_rdid_generic: id1 0xef, id2 0x4016 Probing for Atmel AT26DF161, 2048 KB: probe_spi_rdid_generic: id1 0xef, id2 0x4016 Probing for Atmel AT26DF161A, 2048 KB: probe_spi_rdid_generic: id1 0xef, id2 0x4016 Probing for Atmel AT26F004, 512 KB: probe_spi_rdid_generic: id1 0xef, id2 0x4016 Probing for Atmel
[Bug 593152] [NEW] Remote Desktop Cannot Run W/O being plugged into a monitor.
Public bug reported: I have been trying to figure out how to get a Ubuntu 10.04 Desktop Edition to work with remote desktop without using a monitor for the Ubuntu system. Unfortunately, I have not succeeded, can somebody PLEASE HELP?!? ** Affects: ubuntu Importance: Undecided Status: New -- Remote Desktop Cannot Run W/O being plugged into a monitor. https://bugs.launchpad.net/bugs/593152 You received this bug notification because you are a member of Ubuntu Bugs, which is subscribed to Ubuntu. -- ubuntu-bugs mailing list ubuntu-bugs@lists.ubuntu.com https://lists.ubuntu.com/mailman/listinfo/ubuntu-bugs
[laptop-discuss] problem with wificonfig
I am following the instructions to install ipw driver for intel 3945 here: http://opensolaris.org/os/community/laptop/wireless/ipw/ Everything went fine until I try to connect to created profile, I get this message: wificonfig: failed to open 'wpi0': No such file or directory However, wpi0 is indeed listed when I execute the ifconfig -a command. I also noticed that I have to plumb the driver each time I boot into Solaris. I also noticed that the wifi indicator on the laptop chassis is not on and from experience (linux) this has to be on for wifi to work. I am running Solaris 10. Thanks for your help. Be a better friend, newshound, and know-it-all with Yahoo! Mobile. Try it now. http://mobile.yahoo.com/;_ylt=Ahu06i62sR8HDtDypao8Wcj9tAcJ -- next part -- An HTML attachment was scrubbed... URL: http://mail.opensolaris.org/pipermail/laptop-discuss/attachments/20071230/a974646d/attachment.html
[osol-help] wpi driver install problem
I am following the instructions here: http://opensolaris.org/os/community/laptop/wireless/wpi/ However, when I try the command: pkgadd -d packages/i386/nightly/ SUNWwpi I get this error in the terminal: pkgadd: ERROR: attempt to process datastream failed - open of packages/i386/nightly/ failed, errno=2 pkgadd: ERROR: could not process datastream from packages/i386/nightly/ Is this referring to an online repository? I have no network devices setup on my solaris box yet, so this won't work if it is. Any work around? This message posted from opensolaris.org
[osol-help] wpi driver install problem
Thanks for your response. I am very new to Solaris, coming from Linux. I downloaded and installed Solaris 10 from sun.com. Is this the newest version? This message posted from opensolaris.org
Re: [osol-help] wpi driver install problem
Thanks for your response. I am very new to Solaris, coming from Linux. I downloaded and installed Solaris 10 from sun.com. Is this the newest version? This message posted from opensolaris.org ___ opensolaris-help mailing list opensolaris-help@opensolaris.org
Bug#420530: installation report
Package: installation-reports Boot method: CD Image version: JIGDO http://cdimage.debian.org/debian-cd/4.0_r0/i386/jigdo-cd/debian-40r0-i386-CD-1.jigdo Date: 22 APR 2007, 2200 Machine: Dell Inspiron E1505 Processor: Intel Centrio Core Duo, 1.8ghz Memory: 1GB Partitions: /dev/sda4 ext324675316 2180124 21241724 10% / tmpfstmpfs 517736 0517736 0% /lib/init/rw udev tmpfs 1024068 10172 1% /dev tmpfstmpfs 517736 0517736 0% /dev/shm /dev/scd0 iso9660 663506663506 0 100% /media/cdrom0 Output of lspci -nn and lspci -vnn: 00:00.0 Host bridge [0600]: Intel Corporation Mobile 945GM/PM/GMS/940GML and 945GT Express Memory Controller Hub [8086:27a0] (rev 03) 00:01.0 PCI bridge [0604]: Intel Corporation Mobile 945GM/PM/GMS/940GML and 945GT Express PCI Express Root Port [8086:27a1] (rev 03) 00:1b.0 Audio device [0403]: Intel Corporation 82801G (ICH7 Family) High Definition Audio Controller [8086:27d8] (rev 01) 00:1c.0 PCI bridge [0604]: Intel Corporation 82801G (ICH7 Family) PCI Express Port 1 [8086:27d0] (rev 01) 00:1c.3 PCI bridge [0604]: Intel Corporation 82801G (ICH7 Family) PCI Express Port 4 [8086:27d6] (rev 01) 00:1d.0 USB Controller [0c03]: Intel Corporation 82801G (ICH7 Family) USB UHCI #1 [8086:27c8] (rev 01) 00:1d.1 USB Controller [0c03]: Intel Corporation 82801G (ICH7 Family) USB UHCI #2 [8086:27c9] (rev 01) 00:1d.2 USB Controller [0c03]: Intel Corporation 82801G (ICH7 Family) USB UHCI #3 [8086:27ca] (rev 01) 00:1d.3 USB Controller [0c03]: Intel Corporation 82801G (ICH7 Family) USB UHCI #4 [8086:27cb] (rev 01) 00:1d.7 USB Controller [0c03]: Intel Corporation 82801G (ICH7 Family) USB2 EHCI Controller [8086:27cc] (rev 01) 00:1e.0 PCI bridge [0604]: Intel Corporation 82801 Mobile PCI Bridge [8086:2448] (rev e1) 00:1f.0 ISA bridge [0601]: Intel Corporation 82801GBM (ICH7-M) LPC Interface Bridge [8086:27b9] (rev 01) 00:1f.2 IDE interface [0101]: Intel Corporation 82801GBM/GHM (ICH7 Family) Serial ATA Storage Controller IDE [8086:27c4] (rev 01) 00:1f.3 SMBus [0c05]: Intel Corporation 82801G (ICH7 Family) SMBus Controller [8086:27da] (rev 01) 01:00.0 VGA compatible controller [0300]: ATI Technologies Inc Radeon Mobility X1400 [1002:7145] 03:00.0 Ethernet controller [0200]: Broadcom Corporation BCM4401-B0 100Base-TX [14e4:170c] (rev 02) 03:01.0 FireWire (IEEE 1394) [0c00]: Ricoh Co Ltd Unknown device [1180:0832] 03:01.1 Generic system peripheral [0805]: Ricoh Co Ltd R5C822 SD/SDIO/MMC/MS/MSPro Host Adapter [1180:0822] (rev 19) 03:01.2 System peripheral [0880]: Ricoh Co Ltd Unknown device [1180:0843] (rev 01) 03:01.3 System peripheral [0880]: Ricoh Co Ltd R5C592 Memory Stick Bus Host Adapter [1180:0592] (rev 0a) 03:01.4 System peripheral [0880]: Ricoh Co Ltd xD-Picture Card Controller [1180:0852] (rev 05) 0b:00.0 Network controller [0280]: Intel Corporation PRO/Wireless 3945ABG Network Connection [8086:4222] (rev 02) 00:00.0 Host bridge [0600]: Intel Corporation Mobile 945GM/PM/GMS/940GML and 945GT Express Memory Controller Hub [8086:27a0] (rev 03) Subsystem: Dell Unknown device [1028:01bd] Flags: bus master, fast devsel, latency 0 Capabilities: [e0] Vendor Specific Information 00:01.0 PCI bridge [0604]: Intel Corporation Mobile 945GM/PM/GMS/940GML and 945GT Express PCI Express Root Port [8086:27a1] (rev 03) (prog-if 00 [Normal decode]) Flags: bus master, fast devsel, latency 0 Bus: primary=00, secondary=01, subordinate=01, sec-latency=0 I/O behind bridge: e000-efff Memory behind bridge: efd0-efef Prefetchable memory behind bridge: d000-dfff Capabilities: [88] Subsystem: Dell Unknown device [1028:01bd] Capabilities: [80] Power Management version 2 Capabilities: [90] Message Signalled Interrupts: Mask- 64bit- Queue=0/0 Enable+ Capabilities: [a0] Express Root Port (Slot+) IRQ 0 Capabilities: [100] Virtual Channel Capabilities: [140] Unknown (5) 00:1b.0 Audio device [0403]: Intel Corporation 82801G (ICH7 Family) High Definition Audio Controller [8086:27d8] (rev 01) Subsystem: Dell Unknown device [1028:01bd] Flags: bus master, fast devsel, latency 0, IRQ 233 Memory at efffc000 (64-bit, non-prefetchable) [size=16K] Capabilities: [50] Power Management version 2 Capabilities: [60] Message Signalled Interrupts: Mask- 64bit+ Queue=0/0 Enable- Capabilities: [70] Express Unknown type IRQ 0 Capabilities: [100] Virtual Channel Capabilities: [130] Unknown (5) 00:1c.0 PCI bridge [0604]: Intel Corporation 82801G (ICH7 Family) PCI Express Port 1 [8086:27d0] (rev 01) (prog-if 00 [Normal decode]) Flags: bus master, fast devsel, latency 0 Bus: primary=00, secondary=0b, subordinate=0b, sec-latency=0 Memory behind bridge: efc0-efcf Capabilities: [40] Express Root Port
Bug#420530: installation report
Package: installation-reports Boot method: CD Image version: JIGDO http://cdimage.debian.org/debian-cd/4.0_r0/i386/jigdo-cd/debian-40r0-i386-CD-1.jigdo Date: 22 APR 2007, 2200 Machine: Dell Inspiron E1505 Processor: Intel Centrio Core Duo, 1.8ghz Memory: 1GB Partitions: /dev/sda4 ext324675316 2180124 21241724 10% / tmpfstmpfs 517736 0517736 0% /lib/init/rw udev tmpfs 1024068 10172 1% /dev tmpfstmpfs 517736 0517736 0% /dev/shm /dev/scd0 iso9660 663506663506 0 100% /media/cdrom0 Output of lspci -nn and lspci -vnn: 00:00.0 Host bridge [0600]: Intel Corporation Mobile 945GM/PM/GMS/940GML and 945GT Express Memory Controller Hub [8086:27a0] (rev 03) 00:01.0 PCI bridge [0604]: Intel Corporation Mobile 945GM/PM/GMS/940GML and 945GT Express PCI Express Root Port [8086:27a1] (rev 03) 00:1b.0 Audio device [0403]: Intel Corporation 82801G (ICH7 Family) High Definition Audio Controller [8086:27d8] (rev 01) 00:1c.0 PCI bridge [0604]: Intel Corporation 82801G (ICH7 Family) PCI Express Port 1 [8086:27d0] (rev 01) 00:1c.3 PCI bridge [0604]: Intel Corporation 82801G (ICH7 Family) PCI Express Port 4 [8086:27d6] (rev 01) 00:1d.0 USB Controller [0c03]: Intel Corporation 82801G (ICH7 Family) USB UHCI #1 [8086:27c8] (rev 01) 00:1d.1 USB Controller [0c03]: Intel Corporation 82801G (ICH7 Family) USB UHCI #2 [8086:27c9] (rev 01) 00:1d.2 USB Controller [0c03]: Intel Corporation 82801G (ICH7 Family) USB UHCI #3 [8086:27ca] (rev 01) 00:1d.3 USB Controller [0c03]: Intel Corporation 82801G (ICH7 Family) USB UHCI #4 [8086:27cb] (rev 01) 00:1d.7 USB Controller [0c03]: Intel Corporation 82801G (ICH7 Family) USB2 EHCI Controller [8086:27cc] (rev 01) 00:1e.0 PCI bridge [0604]: Intel Corporation 82801 Mobile PCI Bridge [8086:2448] (rev e1) 00:1f.0 ISA bridge [0601]: Intel Corporation 82801GBM (ICH7-M) LPC Interface Bridge [8086:27b9] (rev 01) 00:1f.2 IDE interface [0101]: Intel Corporation 82801GBM/GHM (ICH7 Family) Serial ATA Storage Controller IDE [8086:27c4] (rev 01) 00:1f.3 SMBus [0c05]: Intel Corporation 82801G (ICH7 Family) SMBus Controller [8086:27da] (rev 01) 01:00.0 VGA compatible controller [0300]: ATI Technologies Inc Radeon Mobility X1400 [1002:7145] 03:00.0 Ethernet controller [0200]: Broadcom Corporation BCM4401-B0 100Base-TX [14e4:170c] (rev 02) 03:01.0 FireWire (IEEE 1394) [0c00]: Ricoh Co Ltd Unknown device [1180:0832] 03:01.1 Generic system peripheral [0805]: Ricoh Co Ltd R5C822 SD/SDIO/MMC/MS/MSPro Host Adapter [1180:0822] (rev 19) 03:01.2 System peripheral [0880]: Ricoh Co Ltd Unknown device [1180:0843] (rev 01) 03:01.3 System peripheral [0880]: Ricoh Co Ltd R5C592 Memory Stick Bus Host Adapter [1180:0592] (rev 0a) 03:01.4 System peripheral [0880]: Ricoh Co Ltd xD-Picture Card Controller [1180:0852] (rev 05) 0b:00.0 Network controller [0280]: Intel Corporation PRO/Wireless 3945ABG Network Connection [8086:4222] (rev 02) 00:00.0 Host bridge [0600]: Intel Corporation Mobile 945GM/PM/GMS/940GML and 945GT Express Memory Controller Hub [8086:27a0] (rev 03) Subsystem: Dell Unknown device [1028:01bd] Flags: bus master, fast devsel, latency 0 Capabilities: [e0] Vendor Specific Information 00:01.0 PCI bridge [0604]: Intel Corporation Mobile 945GM/PM/GMS/940GML and 945GT Express PCI Express Root Port [8086:27a1] (rev 03) (prog-if 00 [Normal decode]) Flags: bus master, fast devsel, latency 0 Bus: primary=00, secondary=01, subordinate=01, sec-latency=0 I/O behind bridge: e000-efff Memory behind bridge: efd0-efef Prefetchable memory behind bridge: d000-dfff Capabilities: [88] Subsystem: Dell Unknown device [1028:01bd] Capabilities: [80] Power Management version 2 Capabilities: [90] Message Signalled Interrupts: Mask- 64bit- Queue=0/0 Enable+ Capabilities: [a0] Express Root Port (Slot+) IRQ 0 Capabilities: [100] Virtual Channel Capabilities: [140] Unknown (5) 00:1b.0 Audio device [0403]: Intel Corporation 82801G (ICH7 Family) High Definition Audio Controller [8086:27d8] (rev 01) Subsystem: Dell Unknown device [1028:01bd] Flags: bus master, fast devsel, latency 0, IRQ 233 Memory at efffc000 (64-bit, non-prefetchable) [size=16K] Capabilities: [50] Power Management version 2 Capabilities: [60] Message Signalled Interrupts: Mask- 64bit+ Queue=0/0 Enable- Capabilities: [70] Express Unknown type IRQ 0 Capabilities: [100] Virtual Channel Capabilities: [130] Unknown (5) 00:1c.0 PCI bridge [0604]: Intel Corporation 82801G (ICH7 Family) PCI Express Port 1 [8086:27d0] (rev 01) (prog-if 00 [Normal decode]) Flags: bus master, fast devsel, latency 0 Bus: primary=00, secondary=0b, subordinate=0b, sec-latency=0 Memory behind bridge: efc0-efcf Capabilities: [40] Express Root Port
Bug#24950: Please respond before August 11
THIS IS GOING TO BE OUR ABSOLUTE ATTEMPT We have endevored to speak to you on many periods and we await your response now! Your current finanncial loann situation meets the requirements for you for up to a 3.1 % lower rate. However, based on the fact that our previous attempts to speak to you didn't work, this will be our final attempt to finalize the lower ratee. Please finalize this final step upon receiving this notice immediately,and complete your request for information now. Submission Here. http://wrpvm.rates-lowered.com/4/index/apb/jrwab spectroscope at underclassmen or even silage as in emirate. Brandon was at automate when that happened towhead. -- To UNSUBSCRIBE, email to [EMAIL PROTECTED] with a subject of unsubscribe. Trouble? Contact [EMAIL PROTECTED]
Bug#24950: Please respond before August 11
THIS IS GOING TO BE OUR ABSOLUTE ATTEMPT We have endevored to speak to you on many periods and we await your response now! Your current finanncial loann situation meets the requirements for you for up to a 3.1 % lower rate. However, based on the fact that our previous attempts to speak to you didn't work, this will be our final attempt to finalize the lower ratee. Please finalize this final step upon receiving this notice immediately,and complete your request for information now. Submission Here. http://wrpvm.rates-lowered.com/4/index/apb/jrwab spectroscope at underclassmen or even silage as in emirate. Brandon was at automate when that happened towhead. -- To UNSUBSCRIBE, email to [EMAIL PROTECTED] with a subject of unsubscribe. Trouble? Contact [EMAIL PROTECTED]