Relative path in absolute URI: txt Spark mac - scala

I am running Spark on Mac (jupyter notebook) and not Windows. I am trying to read a txt file:
val text = sc.textFile("shakespeare.txt")
val relevant_lines = text.filter(l => l.contains("Music"))
val result = relevant_lines.count()
I get the following error:
java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: Module 3:%20Apache%20Spark
at org.apache.hadoop.fs.Path.initialize(Path.java:205)
at org.apache.hadoop.fs.Path.<init>(Path.java:171)
at org.apache.hadoop.fs.Path.<init>(Path.java:93)
at org.apache.hadoop.fs.Globber.glob(Globber.java:211)
at org.apache.hadoop.fs.FileSystem.globStatus(FileSystem.java:1676)
at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:259)
at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:229)
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:315)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:204)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:251)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:251)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:251)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2126)
at org.apache.spark.rdd.RDD.count(RDD.scala:1168)
... 37 elided
Caused by: java.net.URISyntaxException: Relative path in absolute URI: Module 3:%20Apache%20Spark
at java.base/java.net.URI.checkPath(URI.java:1941)
at java.base/java.net.URI.<init>(URI.java:757)
at org.apache.hadoop.fs.Path.initialize(Path.java:202)
... 61 more
Could you help me fix it?
Thank you

Give the complete path where the text file is located in your MAC.
eg -: "/user/name/shakespeare.txt"
For multiple text files
Syntax-: sc.textFile("/user/name/*")
val text = sc.textFile("/user/name/shakespeare.txt")
val relevant_lines = text.filter(l => l.contains("Music"))
val result = relevant_lines.count()

Related

Problems in the configuration between hadoop and spark

I have a problem in a program and I do not have this problem with spark-shell.
When I call:
FileSystem.get(spark.sparkContext.hadoopConfiguration)
In the spark-shell, everything works perfectly, but when I try to use it in the code, I can't read the core-site.xml. I still get it to work when I use:
val conf = new Configuration()
conf.addResource(new Path("path to conf/core-site.xml"))
FileSystem.get(conf)
This solution is not acceptable, since I need to use the Hadoop configuration without passing the configuration explicitly.
Both in (Spark-shell and in the program) the master is called with the parameters spark: //x.x.x.x: 7077
How can I configure spark to use the hadoop configuration?
Code:
val HdfsPrefix: String = "hdfs://"
val path: String = "/tmp/"
def getHdfs(spark: SparkSession): FileSystem = {
//val conf = new Configuration()
//conf.addResource(new Path("/path to/core-site.xml"))
//FileSystem.get(conf)
FileSystem.get(spark.sparkContext.hadoopConfiguration)
}
val dfs = getHdfs(session)
data.select("name", "value").collect().foreach{ x =>
val os = dfs.create(new Path(HdfsPrefix + path + x.getString(0)))
val content: String = x.getString(1)
os.write(content.getBytes)
os.hsync()
}
Error log:
Wrong FS: hdfs:/tmp, expected: file:///
java.lang.IllegalArgumentException: Wrong FS: hdfs:/tmp, expected: file:///
at org.apache.hadoop.fs.FileSystem.checkPath(FileSystem.java:645)
at org.apache.hadoop.fs.RawLocalFileSystem.pathToFile(RawLocalFileSystem.java:80)
at org.apache.hadoop.fs.RawLocalFileSystem.mkdirs(RawLocalFileSystem.java:428)
at org.apache.hadoop.fs.ChecksumFileSystem.mkdirs(ChecksumFileSystem.java:690)
at org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:446)
at org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:433)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:908)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:889)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:786)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:775)
at com.bbva.ebdm.ocelot.io.hdfs.HdfsIO$HdfsOutputFile$$anonfun$write$1.apply(HdfsIO.scala:116)
at com.bbva.ebdm.ocelot.io.hdfs.HdfsIO$HdfsOutputFile$$anonfun$write$1.apply(HdfsIO.scala:115)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at com.bbva.ebdm.ocelot.io.hdfs.HdfsIO$HdfsOutputFile.write(HdfsIO.scala:115)
at com.bbva.ebdm.ocelot.templates.spark_sql.SparkSqlBaseApp$$anonfun$exec$1.apply(SparkSqlBaseApp.scala:33)
at com.bbva.ebdm.ocelot.templates.spark_sql.SparkSqlBaseApp$$anonfun$exec$1.apply(SparkSqlBaseApp.scala:31)
at scala.collection.immutable.Map$Map3.foreach(Map.scala:161)
at com.bbva.ebdm.ocelot.templates.spark_sql.SparkSqlBaseApp$class.exec(SparkSqlBaseApp.scala:31)
at com.bbva.ebdm.ocelot.templates.spark_sql.SparkSqlBaseAppTest$$anonfun$1$$anonfun$apply$mcV$sp$1$$anonfun$apply$1$$anonfun$2$$anonfun$apply$2$$anon$1.exec(SparkSqlBaseAppTest.scala:47)
at com.bbva.ebdm.ocelot.templates.spark_sql.SparkSqlBaseAppTest$$anonfun$1$$anonfun$apply$mcV$sp$1$$anonfun$apply$3.apply(SparkSqlBaseAppTest.scala:49)
at com.bbva.ebdm.ocelot.templates.spark_sql.SparkSqlBaseAppTest$$anonfun$1$$anonfun$apply$mcV$sp$1$$anonfun$apply$3.apply(SparkSqlBaseAppTest.scala:47)
at com.bbva.ebdm.ocelot.templates.spark_sql.SparkSqlBaseAppTest$$anonfun$1$$anonfun$apply$mcV$sp$1$$anonfun$apply$1.apply(SparkSqlBaseAppTest.scala:47)
at com.bbva.ebdm.ocelot.templates.spark_sql.SparkSqlBaseAppTest$$anonfun$1$$anonfun$apply$mcV$sp$1$$anonfun$apply$1.apply(SparkSqlBaseAppTest.scala:47)
at wvlet.airframe.Design.runWithSession(Design.scala:169)
at wvlet.airframe.Design.withSession(Design.scala:182)
at com.bbva.ebdm.ocelot.templates.spark_sql.SparkSqlBaseAppTest$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(SparkSqlBaseAppTest.scala:47)
at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
at org.scalatest.FunSpecLike$$anon$1.apply(FunSpecLike.scala:454)
at org.scalatest.TestSuite$class.withFixture(TestSuite.scala:196)
at org.scalatest.FunSpec.withFixture(FunSpec.scala:1630)
at org.scalatest.FunSpecLike$class.invokeWithFixture$1(FunSpecLike.scala:451)
at org.scalatest.FunSpecLike$$anonfun$runTest$1.apply(FunSpecLike.scala:464)
at org.scalatest.FunSpecLike$$anonfun$runTest$1.apply(FunSpecLike.scala:464)
at org.scalatest.SuperEngine.runTestImpl(Engine.scala:289)
at org.scalatest.FunSpecLike$class.runTest(FunSpecLike.scala:464)
at org.scalatest.FunSpec.runTest(FunSpec.scala:1630)
at org.scalatest.FunSpecLike$$anonfun$runTests$1.apply(FunSpecLike.scala:497)
at org.scalatest.FunSpecLike$$anonfun$runTests$1.apply(FunSpecLike.scala:497)
at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:396)
at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:384)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:384)
at org.scalatest.SuperEngine.org$scalatest$SuperEngine$$runTestsInBranch(Engine.scala:373)
at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:410)
at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:384)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:384)
at org.scalatest.SuperEngine.org$scalatest$SuperEngine$$runTestsInBranch(Engine.scala:379)
at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:461)
at org.scalatest.FunSpecLike$class.runTests(FunSpecLike.scala:497)
at org.scalatest.FunSpec.runTests(FunSpec.scala:1630)
at org.scalatest.Suite$class.run(Suite.scala:1147)
at org.scalatest.FunSpec.org$scalatest$FunSpecLike$$super$run(FunSpec.scala:1630)
at org.scalatest.FunSpecLike$$anonfun$run$1.apply(FunSpecLike.scala:501)
at org.scalatest.FunSpecLike$$anonfun$run$1.apply(FunSpecLike.scala:501)
at org.scalatest.SuperEngine.runImpl(Engine.scala:521)
at org.scalatest.FunSpecLike$class.run(FunSpecLike.scala:501)
at com.bbva.ebdm.ocelot.templates.spark_sql.SparkSqlBaseAppTest.org$scalatest$BeforeAndAfterAll$$super$run(SparkSqlBaseAppTest.scala:31)
at org.scalatest.BeforeAndAfterAll$class.liftedTree1$1(BeforeAndAfterAll.scala:213)
at org.scalatest.BeforeAndAfterAll$class.run(BeforeAndAfterAll.scala:210)
at com.bbva.ebdm.ocelot.templates.spark_sql.SparkSqlBaseAppTest.run(SparkSqlBaseAppTest.scala:31)
at org.scalatest.tools.SuiteRunner.run(SuiteRunner.scala:45)
at org.scalatest.tools.Runner$$anonfun$doRunRunRunDaDoRunRun$1.apply(Runner.scala:1346)
at org.scalatest.tools.Runner$$anonfun$doRunRunRunDaDoRunRun$1.apply(Runner.scala:1340)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.scalatest.tools.Runner$.doRunRunRunDaDoRunRun(Runner.scala:1340)
at org.scalatest.tools.Runner$$anonfun$runOptionallyWithPassFailReporter$2.apply(Runner.scala:1011)
at org.scalatest.tools.Runner$$anonfun$runOptionallyWithPassFailReporter$2.apply(Runner.scala:1010)
at org.scalatest.tools.Runner$.withClassLoaderAndDispatchReporter(Runner.scala:1506)
at org.scalatest.tools.Runner$.runOptionallyWithPassFailReporter(Runner.scala:1010)
at org.scalatest.tools.Runner$.run(Runner.scala:850)
at org.scalatest.tools.Runner.run(Runner.scala)
at org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.runScalaTest2(ScalaTestRunner.java:131)
at org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.main(ScalaTestRunner.java:28)
You need to put the hdfs-site.xml, core-site.xml in spark class path i.e classpath of your program when you are running it
https://spark.apache.org/docs/latest/configuration.html#custom-hadoophive-configuration
According to doc's:
If you plan to read and write from HDFS using Spark, there are two Hadoop configuration files that should be included on Spark’s classpath:
hdfs-site.xml, which provides default behaviors for the HDFS client.
core-site.xml, which sets the default filesystem name.
The location of these configuration files varies across Hadoop versions, but a common location is inside of /etc/hadoop/conf. Some tools create configurations on-the-fly, but offer a mechanism to download copies of them.
To make these files visible to Spark, set HADOOP_CONF_DIR in $SPARK_HOME/conf/spark-env.sh to a location containing the configuration files.
The problem was 'ScalaTest', it doesn't read the core-site.xml when Maven is compiling the proyect, but spark-submit reads it correctly when the proyect is compiled.

Spark unable to read parquet file from partitioned S3 bucket

I have my S3 bucket partitioned like this:
bucket
|--2018
|--2019
|--01
|--02
|--01
|--files.parquet
...
It works fine when I read using this command (Spark 2.1.1):
val dfo = sqlContext.read.parquet("s3://bucket/2019/04/03/*")
but it hits an error when I try to add a partition variable to the path:
val dfo = sqlContext.read.parquet("s3://bucket/2019/04/day=03/*")
or
val dfo = sqlContext.read.parquet("s3://bucket/y=2019/m=04/day=03")
Error:
Name: org.apache.spark.sql.AnalysisException
Message: Path does not exist: s3://bucket/2019/04/day=03/*;
StackTrace: at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:377)
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:370)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at scala.collection.immutable.List.flatMap(List.scala:344)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:370)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:152)
at org.apache.spark.sql.DataFrameReader.parquet(DataFrameReader.scala:441)
at org.apache.spark.sql.DataFrameReader.parquet(DataFrameReader.scala:425)

spark dealing with carbondata

Below is the code snippet I'm trying to use to create a carbondata table in S3. However, inspite of setting the aws credentials in hadoopconfiguration, it still complains about secret key and access key not being set. What is the issue here?
import org.apache.spark.sql.CarbonSession._
import org.apache.spark.sql.CarbonSession._
val carbon = SparkSession.builder().config(sc.getConf).getOrCreateCarbonSession("s3n://url")
carbon.sparkContext.hadoopConfiguration.set("fs.s3n.awsAccessKeyId","<accesskey>")
carbon.sparkContext.hadoopConfiguration.set("fs.s3n.awsSecretAccessKey","<secretaccesskey>")
carbon.sql("CREATE TABLE IF NOT EXISTS test_table(id string,name string,city string,age Int) STORED BY 'carbondata'")
Last command yields error:
java.lang.IllegalArgumentException: AWS Access Key ID and Secret
Access Key must be specified as the username or password
(respectively) of a s3n URL, or by setting the fs.s3n.awsAccessKeyId
or fs.s3n.awsSecretAccessKey properties (respectively)
Spark Version : 2.2.1
Command used to start spark-shell:
$SPARK_PATH/bin/spark-shell --jars /localpath/jar/apache-carbondata-1.3.1-bin-spark2.2.1-hadoop2.7.2/apache-carbondata-1.3.1-bin-spark2.2.1-hadoop2.7.2.jar,/localpath/jar/spark-avro_2.11-4.0.0.jar --packages com.amazonaws:aws-java-sdk-pom:1.9.22,org.apache.hadoop:hadoop-aws:2.7.2,org.slf4j:slf4j-simple:1.7.21,asm:asm:3.2,org.xerial.snappy:snappy-java:1.1.7.1,com.databricks:spark-avro_2.11:4.0.0
UPDATE:
Found that S3 support is only available in 1.4.0 RC1. So I built RC1 and tested the below code against the same. But still I seem to be running into issues. Any help appreciated.
Code:
import org.apache.spark.sql.CarbonSession._
import org.apache.hadoop.fs.s3a.Constants.{ACCESS_KEY, ENDPOINT, SECRET_KEY}
import org.apache.spark.sql.SparkSession
import org.apache.carbondata.core.constants.CarbonCommonConstants
object sample4 {
def main(args: Array[String]) {
val (accessKey, secretKey, endpoint) = getKeyOnPrefix("s3n://")
//val rootPath = new File(this.getClass.getResource("/").getPath
// + "../../../..").getCanonicalPath
val path = "/localpath/sample/data1.csv"
val spark = SparkSession
.builder()
.master("local")
.appName("S3UsingSDKExample")
.config("spark.driver.host", "localhost")
.config(accessKey, "<accesskey>")
.config(secretKey, "<secretkey>")
//.config(endpoint, "s3-us-east-1.amazonaws.com")
.getOrCreateCarbonSession()
spark.sql("Drop table if exists carbon_table")
spark.sql(
s"""
| CREATE TABLE if not exists carbon_table(
| shortField SHORT,
| intField INT,
| bigintField LONG,
| doubleField DOUBLE,
| stringField STRING,
| timestampField TIMESTAMP,
| decimalField DECIMAL(18,2),
| dateField DATE,
| charField CHAR(5),
| floatField FLOAT
| )
| STORED BY 'carbondata'
| LOCATION 's3n://bucketName/table/carbon_table'
| TBLPROPERTIES('SORT_COLUMNS'='', 'DICTIONARY_INCLUDE'='dateField, charField')
""".stripMargin)
}
def getKeyOnPrefix(path: String): (String, String, String) = {
val endPoint = "spark.hadoop." + ENDPOINT
if (path.startsWith(CarbonCommonConstants.S3A_PREFIX)) {
("spark.hadoop." + ACCESS_KEY, "spark.hadoop." + SECRET_KEY, endPoint)
} else if (path.startsWith(CarbonCommonConstants.S3N_PREFIX)) {
("spark.hadoop." + CarbonCommonConstants.S3N_ACCESS_KEY,
"spark.hadoop." + CarbonCommonConstants.S3N_SECRET_KEY, endPoint)
} else if (path.startsWith(CarbonCommonConstants.S3_PREFIX)) {
("spark.hadoop." + CarbonCommonConstants.S3_ACCESS_KEY,
"spark.hadoop." + CarbonCommonConstants.S3_SECRET_KEY, endPoint)
} else {
throw new Exception("Incorrect Store Path")
}
}
def getSparkMaster(args: Array[String]): String = {
if (args.length == 6) args(5)
else if (args(3).contains("spark:") || args(3).contains("mesos:")) args(3)
else "local"
}
}
Error:
18/05/17 12:23:22 ERROR SegmentStatusManager: main Failed to read metadata of load
org.apache.hadoop.fs.s3.S3Exception: org.jets3t.service.ServiceException: Request Error: Empty key
I also tried against the sample code in (tried s3,s3n,s3a protocols as well):
https://github.com/apache/carbondata/blob/master/examples/spark2/src/main/scala/org/apache/carbondata/examples/S3Example.scala
Ran as:
S3Example.main(Array("accesskey","secretKey","s3://bucketName/path/carbon_table","https://bucketName.s3.amazonaws.com","local"))
Error stacktrace:
org.apache.hadoop.fs.s3.S3Exception:
org.jets3t.service.S3ServiceException: Request Error: Empty key at
org.apache.hadoop.fs.s3.Jets3tFileSystemStore.get(Jets3tFileSystemStore.java:175)
at
org.apache.hadoop.fs.s3.Jets3tFileSystemStore.retrieveINode(Jets3tFileSystemStore.java:221)
at sun.reflect.GeneratedMethodAccessor42.invoke(Unknown Source) at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498) at
org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:191)
at
org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)
at com.sun.proxy.$Proxy21.retrieveINode(Unknown Source) at
org.apache.hadoop.fs.s3.S3FileSystem.getFileStatus(S3FileSystem.java:340)
at org.apache.hadoop.fs.FileSystem.exists(FileSystem.java:1426) at
org.apache.carbondata.core.datastore.filesystem.AbstractDFSCarbonFile.isFileExist(AbstractDFSCarbonFile.java:426)
at
org.apache.carbondata.core.datastore.impl.FileFactory.isFileExist(FileFactory.java:201)
at
org.apache.carbondata.core.statusmanager.SegmentStatusManager.readTableStatusFile(SegmentStatusManager.java:246)
at
org.apache.carbondata.core.statusmanager.SegmentStatusManager.readLoadMetadata(SegmentStatusManager.java:197)
at
org.apache.carbondata.core.cache.dictionary.ManageDictionaryAndBTree.clearBTreeAndDictionaryLRUCache(ManageDictionaryAndBTree.java:101)
at
org.apache.spark.sql.hive.CarbonFileMetastore.dropTable(CarbonFileMetastore.scala:460)
at
org.apache.spark.sql.execution.command.table.CarbonCreateTableCommand.processMetadata(CarbonCreateTableCommand.scala:148)
at
org.apache.spark.sql.execution.command.MetadataCommand.run(package.scala:68)
at
org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
at
org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
at
org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:67)
at org.apache.spark.sql.Dataset.(Dataset.scala:183) at
org.apache.spark.sql.CarbonSession$$anonfun$sql$1.apply(CarbonSession.scala:107)
at
org.apache.spark.sql.CarbonSession$$anonfun$sql$1.apply(CarbonSession.scala:96)
at
org.apache.spark.sql.CarbonSession.withProfiler(CarbonSession.scala:144)
at org.apache.spark.sql.CarbonSession.sql(CarbonSession.scala:94) at
$line19.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$S3Example$.main(:68) at $line26.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.(:31)
at $line26.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw.(:36) at
$line26.$read$$iw$$iw$$iw$$iw$$iw$$iw.(:38) at
$line26.$read$$iw$$iw$$iw$$iw$$iw.(:40) at
$line26.$read$$iw$$iw$$iw$$iw.(:42) at
$line26.$read$$iw$$iw$$iw.(:44) at
$line26.$read$$iw$$iw.(:46) at
$line26.$read$$iw.(:48) at
$line26.$read.(:50) at
$line26.$read$.(:54) at
$line26.$read$.() at
$line26.$eval$.$print$lzycompute(:7) at
$line26.$eval$.$print(:6) at $line26.$eval.$print()
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498) at
scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:786)
at
scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:1047)
at
scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:638)
at
scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:637)
at
scala.reflect.internal.util.ScalaClassLoader$class.asContext(ScalaClassLoader.scala:31)
at
scala.reflect.internal.util.AbstractFileClassLoader.asContext(AbstractFileClassLoader.scala:19)
at
scala.tools.nsc.interpreter.IMain$WrappedRequest.loadAndRunReq(IMain.scala:637)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:569) at
scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:565) at
scala.tools.nsc.interpreter.ILoop.interpretStartingWith(ILoop.scala:807)
at scala.tools.nsc.interpreter.ILoop.command(ILoop.scala:681) at
scala.tools.nsc.interpreter.ILoop.processLine(ILoop.scala:395) at
scala.tools.nsc.interpreter.ILoop.loop(ILoop.scala:415) at
scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply$mcZ$sp(ILoop.scala:923)
at
scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply(ILoop.scala:909)
at
scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply(ILoop.scala:909)
at
scala.reflect.internal.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:97)
at scala.tools.nsc.interpreter.ILoop.process(ILoop.scala:909) at
org.apache.spark.repl.Main$.doMain(Main.scala:74) at
org.apache.spark.repl.Main$.main(Main.scala:54) at
org.apache.spark.repl.Main.main(Main.scala) at
sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498) at
org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:775)
at
org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:119)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) Caused
by: org.jets3t.service.S3ServiceException: Request Error: Empty key
at org.jets3t.service.S3Service.getObject(S3Service.java:1470) at
org.apache.hadoop.fs.s3.Jets3tFileSystemStore.get(Jets3tFileSystemStore.java:163)
Is any of the arguments that I'm passing wrong.
I'm able to access the s3 path using aws cli:
aws s3 ls s3://bucketName/path
exists in S3.
You can try it using this example https://github.com/apache/carbondata/blob/master/examples/spark2/src/main/scala/org/apache/carbondata/examples/S3Example.scala
You have to provide aws credentials properties to spark first after that you will be creating carbonSession.
If you have already created sparkContext without aws properties being provided. Then it do not pick up those properties even after you give it to carbonContext.
hi vikas looking at your exception empty key simply means that your acesss key and secret key is not binded in carbon session because when we give the s3 implementation we write the logic that if any of key is not provide by user then it then their value should be taken as empty
so to make things easy
first build the carbon data jar using this command
mvn -Pspark-2.1 clean package
then execute spark submit with this command
./spark-submit --jars file:///home/anubhav/Downloads/softwares/spark-2.2.1-bin-hadoop2.7/carbonlib/apache-carbondata-1.4.0-SNAPSHOT-bin-spark2.2.1-hadoop2.7.2.jar --class org.apache.carbondata.examples.S3Example /home/anubhav/Documents/carbondata/carbondata/carbondata/examples/spark2/target/carbondata-examples-spark2-1.4.0-SNAPSHOT.jar local
replace my jar path with yours and see it should work,its working for me

Error while running spark on standalone cluster

I'm trying to run a simple Spark code on standalone cluster. Below is the code:
from pyspark import SparkConf,SparkContext
if __name__ == "__main__":
conf = SparkConf().setAppName("even-numbers").setMaster("spark://sumit-Inspiron-N5110:7077")
sc = SparkContext(conf)
inp = sc.parallelize([1,2,3,4,5])
even = inp.filter(lambda x: (x % 2 == 0)).collect()
for i in even:
print(i)
but, I'm getting error stating " Could not parse Master URL":
py4j.protocol.Py4JJavaError: An error occurred while calling None.org.apache.spark.api.java.JavaSparkContext.
: org.apache.spark.SparkException: Could not parse Master URL: '<pyspark.conf.SparkConf object at 0x7fb27e864850>'
at org.apache.spark.SparkContext$.org$apache$spark$SparkContext$$createTaskScheduler(SparkContext.scala:2760)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:501)
at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:236)
at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748)
18/01/07 16:59:47 INFO ShutdownHookManager: Shutdown hook called
18/01/07 16:59:47 INFO ShutdownHookManager: Deleting directory /tmp/spark-0d71782f-617f-44b1-9593-b9cd9267757e
I also tried setting the master as 'local', but it didn't work. Can someone help?
And yes, the command to run the job is
./bin/spark-submit even.py
Replace your following line
sc = SparkContext(conf)
with
sc = SparkContext(conf=conf)
you should have it solved.

spark: SAXParseException while writing to parquet on s3

I'm trying to read in some json, infer a schema, and write it out again as parquet to s3 (s3a). For some reason, about a third of the way through the writing portion of the run, spark always errors out with the error included below. I can't find any obvious reasons for the issue: it isn't out of memory; there are no long GC pauses. There don't seem to be any additional error messages in the logs of the individual executors.
The script runs fine on another set of data that I have, which is of a very similar structure, but several orders of magnitude smaller.
I am running spark 2.0.1-hadoop-2.7 and am using the FileOutputCommitter. The algorithm version doesn't seem to matter.
Edit:
This does not appear to be a problem in badly formed json or corrupted files. I have unzipped and read in each file individually with no error.
Here's a simplified version of the script:
object Foo {
def parseJson(json: String): Option[Map[String, Any]] = {
if (json == null)
Some(Map())
else
parseOpt(json).map((j: JValue) => j.values.asInstanceOf[Map[String, Any]])
}
}
}
// read in as text and parse json using json4s
val jsonRDD: RDD[String] = sc.textFile(inputPath)
.map(row -> Foo.parseJson(row))
// infer a schema that will encapsulate the most rows in a sample of size sampleRowNum
val schema: StructType = Infer.getMostCommonSchema(sc, jsonRDD, sampleRowNum)
// get documents compatibility with schema
val jsonWithCompatibilityRDD: RDD[(String, Boolean)] = jsonRDD
.map(js => (js, Infer.getSchemaCompatibility(schema, Infer.inferSchema(js)).toBoolean))
.repartition(partitions)
val jsonCompatibleRDD: RDD[String] = jsonWithCompatibilityRDD
.filter { case (js: String, compatible: Boolean) => compatible }
.map { case (js: String, _: Boolean) => js }
// create a dataframe from documents with compatible schema
val dataFrame: DataFrame = spark.read.schema(schema).json(jsonCompatibleRDD)
It completes the earlier schema inferring steps successfully. The error itself occurs on the last line, but I suppose that could encompass at least the immediately preceding statemnt, if not earlier:
org.apache.spark.SparkException: Task failed while writing rows
at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:261)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(InsertIntoHadoopFsRelationCommand.scala:143)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(InsertIntoHadoopFsRelationCommand.scala:143)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.RuntimeException: Failed to commit task
at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.org$apache$spark$sql$execution$datasources$DefaultWriterContainer$$commitTask$1(WriterContainer.scala:275)
at org.apache.spark.sql.execution.datasources.DefaultWriterContainer$$anonfun$writeRows$1.apply$mcV$sp(WriterContainer.scala:257)
at org.apache.spark.sql.execution.datasources.DefaultWriterContainer$$anonfun$writeRows$1.apply(WriterContainer.scala:252)
at org.apache.spark.sql.execution.datasources.DefaultWriterContainer$$anonfun$writeRows$1.apply(WriterContainer.scala:252)
at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1345)
at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:258)
... 8 more
Suppressed: java.lang.NullPointerException
at org.apache.parquet.hadoop.InternalParquetRecordWriter.flushRowGroupToStore(InternalParquetRecordWriter.java:147)
at org.apache.parquet.hadoop.InternalParquetRecordWriter.close(InternalParquetRecordWriter.java:113)
at org.apache.parquet.hadoop.ParquetRecordWriter.close(ParquetRecordWriter.java:112)
at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.close(ParquetFileFormat.scala:569)
at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.org$apache$spark$sql$execution$datasources$DefaultWriterContainer$$abortTask$1(WriterContainer.scala:282)
at org.apache.spark.sql.execution.datasources.DefaultWriterContainer$$anonfun$writeRows$2.apply$mcV$sp(WriterContainer.scala:258)
at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1354)
... 9 more
Caused by: com.amazonaws.AmazonClientException: Unable to unmarshall response (Failed to parse XML document with handler class com.amazonaws.services.s3.model.transform.XmlResponsesSaxParser$ListBucketHandler). Response Code: 200, Response Text: OK
at com.amazonaws.http.AmazonHttpClient.handleResponse(AmazonHttpClient.java:738)
at com.amazonaws.http.AmazonHttpClient.executeHelper(AmazonHttpClient.java:399)
at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:232)
at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:3528)
at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:3480)
at com.amazonaws.services.s3.AmazonS3Client.listObjects(AmazonS3Client.java:604)
at org.apache.hadoop.fs.s3a.S3AFileSystem.getFileStatus(S3AFileSystem.java:962)
at org.apache.hadoop.fs.s3a.S3AFileSystem.deleteUnnecessaryFakeDirectories(S3AFileSystem.java:1147)
at org.apache.hadoop.fs.s3a.S3AFileSystem.finishedWrite(S3AFileSystem.java:1136)
at org.apache.hadoop.fs.s3a.S3AOutputStream.close(S3AOutputStream.java:142)
at org.apache.hadoop.fs.FSDataOutputStream$PositionCache.close(FSDataOutputStream.java:72)
at org.apache.hadoop.fs.FSDataOutputStream.close(FSDataOutputStream.java:106)
at org.apache.parquet.hadoop.ParquetFileWriter.end(ParquetFileWriter.java:400)
at org.apache.parquet.hadoop.InternalParquetRecordWriter.close(InternalParquetRecordWriter.java:117)
at org.apache.parquet.hadoop.ParquetRecordWriter.close(ParquetRecordWriter.java:112)
at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.close(ParquetFileFormat.scala:569)
at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.org$apache$spark$sql$execution$datasources$DefaultWriterContainer$$commitTask$1(WriterContainer.scala:267)
... 13 more
Caused by: com.amazonaws.AmazonClientException: Failed to parse XML document with handler class com.amazonaws.services.s3.model.transform.XmlResponsesSaxParser$ListBucketHandler
at com.amazonaws.services.s3.model.transform.XmlResponsesSaxParser.parseXmlInputStream(XmlResponsesSaxParser.java:150)
at com.amazonaws.services.s3.model.transform.XmlResponsesSaxParser.parseListBucketObjectsResponse(XmlResponsesSaxParser.java:279)
at com.amazonaws.services.s3.model.transform.Unmarshallers$ListObjectsUnmarshaller.unmarshall(Unmarshallers.java:75)
at com.amazonaws.services.s3.model.transform.Unmarshallers$ListObjectsUnmarshaller.unmarshall(Unmarshallers.java:72)
at com.amazonaws.services.s3.internal.S3XmlResponseHandler.handle(S3XmlResponseHandler.java:62)
at com.amazonaws.services.s3.internal.S3XmlResponseHandler.handle(S3XmlResponseHandler.java:31)
at com.amazonaws.http.AmazonHttpClient.handleResponse(AmazonHttpClient.java:712)
... 29 more
Caused by: org.xml.sax.SAXParseException; lineNumber: 1; columnNumber: 2; XML document structures must start and end within the same entity.
at org.apache.xerces.util.ErrorHandlerWrapper.createSAXParseException(Unknown Source)
at org.apache.xerces.util.ErrorHandlerWrapper.fatalError(Unknown Source)
at org.apache.xerces.impl.XMLErrorReporter.reportError(Unknown Source)
at org.apache.xerces.impl.XMLErrorReporter.reportError(Unknown Source)
at org.apache.xerces.impl.XMLErrorReporter.reportError(Unknown Source)
at org.apache.xerces.impl.XMLScanner.reportFatalError(Unknown Source)
at org.apache.xerces.impl.XMLDocumentFragmentScannerImpl.endEntity(Unknown Source)
at org.apache.xerces.impl.XMLDocumentScannerImpl.endEntity(Unknown Source)
at org.apache.xerces.impl.XMLEntityManager.endEntity(Unknown Source)
at org.apache.xerces.impl.XMLEntityScanner.load(Unknown Source)
at org.apache.xerces.impl.XMLEntityScanner.skipChar(Unknown Source)
at org.apache.xerces.impl.XMLDocumentScannerImpl$PrologDispatcher.dispatch(Unknown Source)
at org.apache.xerces.impl.XMLDocumentFragmentScannerImpl.scanDocument(Unknown Source)
at org.apache.xerces.parsers.XML11Configuration.parse(Unknown Source)
at org.apache.xerces.parsers.XML11Configuration.parse(Unknown Source)
at org.apache.xerces.parsers.XMLParser.parse(Unknown Source)
at org.apache.xerces.parsers.AbstractSAXParser.parse(Unknown Source)
at com.amazonaws.services.s3.model.transform.XmlResponsesSaxParser.parseXmlInputStream(XmlResponsesSaxParser.java:141)
... 35 more
Here's my conf:
spark.executor.extraJavaOptions -XX:+UseG1GC -XX:MaxPermSize=1G -XX:+HeapDumpOnOutOfMemoryError
spark.executor.memory 16G
spark.executor.uri https://s3.amazonaws.com/foo/spark-2.0.1-bin-hadoop2.7.tgz
spark.hadoop.fs.s3a.impl org.apache.hadoop.fs.s3a.S3AFileSystem
spark.hadoop.fs.s3a.buffer.dir /raid0/spark
spark.hadoop.fs.s3n.buffer.dir /raid0/spark
spark.hadoop.fs.s3a.connection.timeout 500000
spark.hadoop.fs.s3n.multipart.uploads.enabled true
spark.hadoop.parquet.block.size 2147483648
spark.hadoop.parquet.enable.summary-metadata false
spark.jars.packages com.databricks:spark-avro_2.11:3.0.1
spark.local.dir /raid0/spark
spark.mesos.coarse false
spark.mesos.constraints priority:1
spark.network.timeout 600
spark.rpc.message.maxSize 500
spark.speculation false
spark.sql.parquet.mergeSchema false
spark.sql.planner.externalSort true
spark.submit.deployMode client
spark.task.cpus 1
I can think for three possible reasons for this problem.
JVM version. AWS SDK checks for the following ones. "1.6.0_06",
"1.6.0_13", "1.6.0_17", "1.6.0_65", "1.7.0_45". If you are using one
of them, try upgrading.
Old AWS SDK. Refer to
https://github.com/aws/aws-sdk-java/issues/460 for a workaround.
If you lots of files in the directory where you are writing these files, you might be hitting https://issues.apache.org/jira/browse/HADOOP-13164. Consider increasing the timeout to larger values.
A SAXParseException may indicate a badly formatted XML file. Since the job fails roughly a third of the way through consistently, this means it's probably failing in the same place every time (a file whose partition is roughly a third of the way through the partition list).
Can you paste your script? It may be possible to wrap the Spark step in a try/catch loop that will print out the file if this error occurs, which will let you easily zoom in on the problem.
From the logs:
Caused by: org.xml.sax.SAXParseException; lineNumber: 1; columnNumber: 2; XML document structures must start and end within the same entity.
and
Caused by: com.amazonaws.AmazonClientException: Failed to parse XML document with handler class com.amazonaws.services.s3.model.transform.XmlResponsesSaxParser$ListBucketHandler
It looks like you have a corrupted/incorrectly formatted file, and your error is actually occurring during the read portion of the task. You could confirm this by trying another operation that will force the read such as count().
If confirmed, the goal would then be to find the corrupted file. You could do this by listing the s3 files, sc.parallelize() that list, and then trying to read the files in a custom function using map().
import boto3
from pyspark.sql import Row
def scanKeys(startKey, endKey):
bucket = boto3.resource('s3').Bucket('bucketName')
for obj in bucket.objects.filter(Prefix='prefix', Marker=startKey):
if obj.key < endKey:
yield obj.key
else:
return
def testFile(s3Path):
s3obj = boto3.resource('s3').Object(bucket_name='bucketName', key=key)
body = s3obj.get()['Body']
...
logic to test file format, or use a try/except and attempt to parse it
...
if fileFormatedCorrectly == True:
return Row(status='Good', key = s3Path)
else:
return Row(status='Fail', key = s3Path)
keys = list(scanKeys(startKey, endKey))
keyListRdd = sc.parallelize(keys, 1000)
keyListRdd.map(testFile).filter(lambda x: x.asDict.get('status') == 'Fail').collect()
This will return the s3 paths for the incorrectly formatted files
For Googlers:
If you:
have a versioned bucket
use s3a://
see ListBucketHandler and listObjects in your error message
Quick solution:
use s3:// instead of s3a://, which will use S3 driver provided by EMR
You may see this error because s3a:// in older versions uses S3::ListObjects (v1) API instead of S3::ListObjectsV2. The former would return extra info like owner, and is not robust against large number of deletion markers. Newer versions of the s3a:// driver solved this problem, but you could always use the s3:// driver instead.
Quote:
the V1 list API experience always returns 5000 entries (as set in fs.s3a.paging.maximum
except for the final entry
if you have versioning turned on in your bucket, deleted entries retain tombstone markers with references to their versions
which will surface in the S3-side of list calls, but get stripped out from the response
so...for a very large tree, you may end up S3 having to keep a channel open while is skips of thousands to millions of deleted
objects before it can find actual ones to return.
which can time out connections.
Quote:
Introducing a new version of the ListObjects (ListObjectsV2) API that allows listing objects with a large number of delete markers.
Quote:
If there are thousands of delete markers, the list operation might timeout。