I have set up a spark cluster and all the nodes have access to network shared storage where they can access a file to read. I am running this in a python jupyter notebook. It was working a few days ago, and now it stopped working but I'm not sure why, or what I have changed.
I have tried restarting the nodes and master.
I have also tried copying the csv file to a new directory and pointing the spark.read there, but it still gives the same error.
When I delete the csv file, it gives a much shorter error saying 'File not found'
Any help would be greatly appreciated.
This is my code:
from pyspark.sql import SparkSession
from pyspark.conf import SparkConf
spark = SparkSession.builder \
.master("spark://IP:PORT") \
.appName("app_1") \
.config(conf=SparkConf()) \
.getOrCreate()
df = spark.read.csv("/nas/file123.csv")
string1 = df.rdd.map(lambda x: x.column1).collect()
However, I get this error:
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-2-12bd938122cd> in <module>()
29
30
---> 31 string1 = df.rdd.map(lambda x: x.column1).collect()
32
33
/home/hjk/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/rdd.pyc in collect(self)
807 """
808 with SCCallSiteSync(self.context) as css:
--> 809 port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
810 return list(_load_from_socket(port, self._jrdd_deserializer))
811
/usr/local/lib/python2.7/dist-packages/py4j/java_gateway.pyc in __call__(self, *args)
1131 answer = self.gateway_client.send_command(command)
1132 return_value = get_return_value(
-> 1133 answer, self.gateway_client, self.target_id, self.name)
1134
1135 for temp_arg in temp_args:
/home/hjk/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/sql/utils.pyc in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
/usr/local/lib/python2.7/dist-packages/py4j/protocol.pyc in get_return_value(answer, gateway_client, target_id, name)
317 raise Py4JJavaError(
318 "An error occurred while calling {0}{1}{2}.\n".
--> 319 format(target_id, ".", name), value)
320 else:
321 raise Py4JError(
Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 4 in stage 3.0 failed 4 times, most recent failure: Lost task 4.3 in stage 3.0 (TID 37, executor 2): java.io.FileNotFoundException: File file:/nas/file123.csv does not exist
It is possible the underlying files have been updated. You can explicitly invalidate the cache in Spark by running 'REFRESH TABLE tableName' command in SQL or by recreating the Dataset/DataFrame involved.
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:157)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:102)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:377)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.hasNext(SerDeUtil.scala:117)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.foreach(SerDeUtil.scala:112)
at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:504)
at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply(PythonRDD.scala:328)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1951)
at org.apache.spark.api.python.PythonRunner$WriterThread.run(PythonRDD.scala:269)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1944)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1958)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:935)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.collect(RDD.scala:934)
at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:453)
at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.io.FileNotFoundException: File file:/nas/file123.csv does not exist
It is possible the underlying files have been updated. You can explicitly invalidate the cache in Spark by running 'REFRESH TABLE tableName' command in SQL or by recreating the Dataset/DataFrame involved.
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:157)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:102)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:377)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.hasNext(SerDeUtil.scala:117)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.foreach(SerDeUtil.scala:112)
at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:504)
at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply(PythonRDD.scala:328)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1951)
at org.apache.spark.api.python.PythonRunner$WriterThread.run(PythonRDD.scala:269)
From the error it looks like it is checking the file on your local system. Just make sure that you have file present on specified Path. Also try below suggestions.
try with file URI : file:///nas/file123.csv
Upload the file on HDFS and try to read the file from HDFS URI like hdfs:///...
Hope this helps.
Regards,
Neeraj
If you are loading the data from local directory, remember to make sure file exists in all of your worker nodes.
Related
I'm using HDP 2.6.5 and when i want to save my data (get on a json file) to a mongoDB database i have this problem.
I'm using the sandbox.
My code :
#HDFS-Mongo Used to write the json file from HDFS to Mongo DB
from pyspark.sql import SparkSession
my_spark = SparkSession \
.builder \
.appName("testdb") \
.config("spark.mongodb.input.uri", "mongodb://127.0.0.1/testdb.test1") \
.config("spark.mongodb.output.uri", "mongodb://127.0.0.1/testdb.test1") \
.getOrCreate()
df = my_spark.read.option("multiline", "true").json("hdfs://sandbox-hdp.hortonworks.com:8020/user/root/output2.json")
df.count()
df.printSchema()
df.write.format("com.mongodb.spark.sql.DefaultSource").mode("append").option("database","testdb").option("collection", "test1").save()
The errors :
Traceback (most recent call last): File "hdfs_mongo.py", line 19, in <module> df.write.format("com.mongodb.spark.sql.DefaultSource").mode("append").option("database","testdb").option("collection", "test1").save() File "/usr/local/lib/python3.6/site-packages/pyspark/sql/readwriter.py", line 738, in save self._jwrite.save() File "/usr/local/lib/python3.6/site-packages/py4j/java_gateway.py", line 1322, in __call__ answer, self.gateway_client, self.target_id, self.name) File "/usr/local/lib/python3.6/site-packages/pyspark/sql/utils.py", line 111, in deco return f(*a, **kw) File "/usr/local/lib/python3.6/site-packages/py4j/protocol.py", line 328, in get_return_value format(target_id, ".", name), value) py4j.protocol.Py4JJavaError: An error occurred while calling o44.save. : java.lang.ClassNotFoundException: Failed to find data source: com.mongodb.spark.sql.DefaultSource. Please find packages at http://spark.apache.org/third-party-projects.html at org.apache.spark.sql.errors.QueryExecutionErrors$.failedToFindDataSourceError(QueryExecutionErrors.scala:443) at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSource(DataSource.scala:670) at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSourceV2(DataSource.scala:720) at org.apache.spark.sql.DataFrameWriter.lookupV2Provider(DataFrameWriter.scala:852) at org.apache.spark.sql.DataFrameWriter.saveInternal(DataFrameWriter.scala:256) at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:247) 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 py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:282) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182) at py4j.ClientServerConnection.run(ClientServerConnection.java:106) at java.lang.Thread.run(Thread.java:748) Caused by: java.lang.ClassNotFoundException: com.mongodb.spark.sql.DefaultSource.DefaultSource at java.net.URLClassLoader.findClass(URLClassLoader.java:381) at java.lang.ClassLoader.loadClass(ClassLoader.java:424) at java.lang.ClassLoader.loadClass(ClassLoader.java:357) at org.apache.spark.sql.execution.datasources.DataSource$.$anonfun$lookupDataSource$5(DataSource.scala:656) at scala.util.Try$.apply(Try.scala:213) at org.apache.spark.sql.execution.datasources.DataSource$.$anonfun$lookupDataSource$4(DataSource.scala:656) at scala.util.Failure.orElse(Try.scala:224) at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSource(DataSource.scala:656) ... 16 more
I already try to uninstall python and install again (python3.6), i use this code and run it but it don't works :
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("myApp") \
.config("spark.mongodb.input.uri", "mongodb://xxx.xxx.xxx.xxx:27017/sample1.zips") \
.config("spark.mongodb.output.uri", "mongodb://xxx.xxx.xxx.xxx:27017/sample1.zips") \
.config('spark.jars.packages', 'org.mongodb.spark:mongo-spark-connector_2.11:2.3.2') \
.getOrCreate()
df = spark.read.format("com.mongodb.spark.sql.DefaultSource").load()
df.printSchema()
I am trying to write a datafram into Mongodb sink using foreachbach in Pyspark but getting an error. I am using spark version 2.4.7 and python 3.7. the same code work fine when I tried to write the datafram as batch. I used mongo-spark-connector_2.11:2.4.1
this is the code and the error message:
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
spark = SparkSession.builder \
.master('local[3]') \
.config('spark.jars.packages', 'org.mongodb.spark:mongo-spark-connector_2.11:2.4.1') \
.config('spark.jars.packages', 'org.apache.spark:spark-sql-kafka-0-10_2.11:2.4.7') \
.getOrCreate()
def mongoSink(df, batch_id):
df.write \
.format('mongo') \
.mode('append') \
.option('spark.mongodb.output.uri', 'mongodb://127.0.0.1:27017/db.cl') \
.save()
schema = StructType([....])
# Reading from Kafka topic
kafka_df = spark.readStream \
.format('kafka') \
.option('kafka.bootstrap.servers', 'localhost:9092') \
.option('subscribe', 'kTopic') \
.option('startingOffsets', 'latest') \
.load()
# Processing code
.
.
.
# Writing to MongoDB
write_df_mongodb = f_df.writeStream \
.format('mongo') \
.foreachBatch(mongoSink) \
.option("checkpointLocation", "chk_dir") \
.outputMode('append') \
.start()
write_df_mongodb.awaitTermination()
Error message:
Py4JJavaError: An error occurred while calling o77.awaitTermination.
: org.apache.spark.sql.streaming.StreamingQueryException: An exception was raised by the Python Proxy. Return Message: Traceback (most recent call last):
File "D:\Spark2-4\python\lib\py4j-0.10.7-src.zip\py4j\java_gateway.py", line 2381, in _call_proxy
return_value = getattr(self.pool[obj_id], method)(*params)
File "D:\Spark2-4\python\pyspark\sql\utils.py", line 191, in call
raise e
File "D:\Spark2-4\python\pyspark\sql\utils.py", line 188, in call
self.func(DataFrame(jdf, self.sql_ctx), batch_id)
File "<ipython-input-5-e9393e49a072>", line 5, in mongoSink
.option('spark.mongodb.output.uri', 'mongodb://127.0.0.1:27017/db.cl') \
File "D:\Spark2-4\python\pyspark\sql\readwriter.py", line 737, in save
self._jwrite.save()
File "D:\Spark2-4\python\lib\py4j-0.10.7-src.zip\py4j\java_gateway.py", line 1257, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "D:\Spark2-4\python\pyspark\sql\utils.py", line 63, in deco
return f(*a, **kw)
File "D:\Spark2-4\python\lib\py4j-0.10.7-src.zip\py4j\protocol.py", line 328, in get_return_value
format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling o83.save.
: java.lang.NoClassDefFoundError: com/mongodb/ConnectionString
at com.mongodb.spark.config.MongoCompanionConfig$$anonfun$4.apply(MongoCompanionConfig.scala:278)
at com.mongodb.spark.config.MongoCompanionConfig$$anonfun$4.apply(MongoCompanionConfig.scala:278)
at scala.util.Try$.apply(Try.scala:192)
at com.mongodb.spark.config.MongoCompanionConfig$class.connectionString(MongoCompanionConfig.scala:278)
at com.mongodb.spark.config.WriteConfig$.connectionString(WriteConfig.scala:37)
at com.mongodb.spark.config.WriteConfig$.apply(WriteConfig.scala:239)
at com.mongodb.spark.config.WriteConfig$.apply(WriteConfig.scala:37)
at com.mongodb.spark.config.MongoCompanionConfig$class.apply(MongoCompanionConfig.scala:124)
at com.mongodb.spark.config.WriteConfig$.apply(WriteConfig.scala:37)
at com.mongodb.spark.config.MongoCompanionConfig$class.apply(MongoCompanionConfig.scala:113)
at com.mongodb.spark.config.WriteConfig$.apply(WriteConfig.scala:37)
at com.mongodb.spark.sql.DefaultSource.createRelation(DefaultSource.scala:64)
at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:45)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:86)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:83)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:81)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:696)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:696)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:80)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:696)
at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:305)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:291)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
at java.lang.reflect.Method.invoke(Unknown Source)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Unknown Source)
Caused by: java.lang.ClassNotFoundException: com.mongodb.ConnectionString
at java.net.URLClassLoader.findClass(Unknown Source)
at java.lang.ClassLoader.loadClass(Unknown Source)
at sun.misc.Launcher$AppClassLoader.loadClass(Unknown Source)
at java.lang.ClassLoader.loadClass(Unknown Source)
... 43 more
Pleas any help
Thank you,
It is the first time I am using Pyspark. I would like to create an ETL which extract from the API and put the data into a database in my local environment. But I have an error to call the API as shown below. Any help would be appreciated.
t
t
The error:
Traceback (most recent call last):
File "etl.py", line 9, in <module>
df = spark.read.format("org.apache.dsext.spark.datasource.rest.RestDataSource").options(**options).load()
File "/home/ubuntu/.local/lib/python3.6/site-packages/pyspark/sql/readwriter.py", line 184, in load
return self._df(self._jreader.load())
File "/home/ubuntu/.local/lib/python3.6/site-packages/py4j/java_gateway.py", line 1305, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "/home/ubuntu/.local/lib/python3.6/site-packages/pyspark/sql/utils.py", line 128, in deco
return f(*a, **kw)
File "/home/ubuntu/.local/lib/python3.6/site-packages/py4j/protocol.py", line 328, in get_return_value
format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling o32.load.
: java.lang.ClassNotFoundException: Failed to find data source: org.apache.dsext.spark.datasource.rest.RestDataSource. Please find packages at http://spark.apache.org/third-party-projects.html
at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSource(DataSource.scala:679)
at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSourceV2(DataSource.scala:733)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:248)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:221)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:566)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.base/java.lang.Thread.run(Thread.java:834)
Caused by: java.lang.ClassNotFoundException: org.apache.dsext.spark.datasource.rest.RestDataSource.DefaultSource
at java.base/java.net.URLClassLoader.findClass(URLClassLoader.java:471)
at java.base/java.lang.ClassLoader.loadClass(ClassLoader.java:589)
at java.base/java.lang.ClassLoader.loadClass(ClassLoader.java:522)
at org.apache.spark.sql.execution.datasources.DataSource$.$anonfun$lookupDataSource$5(DataSource.scala:653)
at scala.util.Try$.apply(Try.scala:213)
at org.apache.spark.sql.execution.datasources.DataSource$.$anonfun$lookupDataSource$4(DataSource.scala:653)
at scala.util.Failure.orElse(Try.scala:224)
at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSource(DataSource.scala:653)
... 14 more
My code:
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("{your app name here}").getOrCreate()
uri = "https://min-api.cryptocompare.com/data/histoday?fsym=BTC&tsym=JPY&limit=30&aggregate=1&e=CCCAGG"
options = { 'url' : uri, 'method' : 'GET', 'readTimeout' : '10000', 'connectionTimeout' : '2000', 'partitions' : '10'}
df = spark.read.format("org.apache.dsext.spark.datasource.rest.RestDataSource").options(**options).load()
df.printSchema()
JAVA version:
openjdk 11.0.9.1 2020-11-04
OpenJDK Runtime Environment (build 11.0.9.1+1-Ubuntu-0ubuntu1.18.04)
OpenJDK 64-Bit Server VM (build 11.0.9.1+1-Ubuntu-0ubuntu1.18.04, mixed mode, sharing)
I believe that this issue was raised due to a missing dependency.
In the code, you mentioned org.apache.dsext.spark.datasource.rest.RestDataSource as your format, this particular functionality is not inbuild in spark but depends on third party package called REST Data Source
you need to create a jar file by building the codebase and add it to your spark as follows:
$SPARK_HOME/bin/spark-shell --jars spark-datasource-rest_2.11-2.1.0-SNAPSHOT.jar --packages org.scalaj:scalaj-http_2.10:2.3.0
I used Python and boto3 to process some S3 files on the spark, and when I downloaded the files, it was unusual: The 's3'resource does not exist.
Because boto3 is not installed on each cluster node, I packaged the dependency packages used by boto3 as zip and used the spark cluster submitted by -- py-files, and this exception occurred.
Py4JJavaErrorTraceback (most recent call last)
<ipython-input-3-8147865bf49c> in <module>()
2
3
----> 4 extractor.extract(paths)
/usr/local/lib/python2.7/site-packages/extract-1.0-py2.7.egg/extract.pyc in extract(self, target_files_path)
52 try:
53 sc = self.get_spark_context()
---> 54 self._extract_file(sc, target_files_path)
55 finally:
56 if sc:
/usr/local/lib/python2.7/site-packages/extract-1.0-py2.7.egg/extract.pyc in _extract_file(self, sc, target_files_path)
109 def _extract_file(self, sc, target_files_path):
110 file_rdd = sc.parallelize(target_files_path, len(target_files_path))
--> 111 result_rdd = file_rdd.map(lambda file_path: self.process(file_path, self.func)).collect()
112 result_rdd.saveAsTextFile(self.result_path)
113
/usr/lib/spark/python/pyspark/rdd.py in collect(self)
769 """
770 with SCCallSiteSync(self.context) as css:
--> 771 port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
772 return list(_load_from_socket(port, self._jrdd_deserializer))
773
/usr/lib/spark/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py in __call__(self, *args)
811 answer = self.gateway_client.send_command(command)
812 return_value = get_return_value(
--> 813 answer, self.gateway_client, self.target_id, self.name)
814
815 for temp_arg in temp_args:
/usr/lib/spark/python/lib/py4j-0.9-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
306 raise Py4JJavaError(
307 "An error occurred while calling {0}{1}{2}.\n".
--> 308 format(target_id, ".", name), value)
309 else:
310 raise Py4JError(
Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 2 in stage 0.0 failed 4 times, most recent failure: Lost task 2.3 in stage 0.0 (TID 15, ip-172-20-219-210.corp.hpicloud.net): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/mnt/yarn/usercache/chqiang/appcache/application_1521024688288_67008/container_1521024688288_67008_01_000002/pyspark.zip/pyspark/worker.py", line 111, in main
process()
File "/mnt/yarn/usercache/chqiang/appcache/application_1521024688288_67008/container_1521024688288_67008_01_000002/pyspark.zip/pyspark/worker.py", line 106, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/mnt/yarn/usercache/chqiang/appcache/application_1521024688288_67008/container_1521024688288_67008_01_000002/pyspark.zip/pyspark/serializers.py", line 263, in dump_stream
vs = list(itertools.islice(iterator, batch))
File "/usr/local/lib/python2.7/site-packages/extract-1.0-py2.7.egg/extract.py", line 111, in <lambda>
File "./lib.zip/extract.py", line 115, in process
local_path = download_file_from_s3(self.app_name, file_path)
File "./lib.zip/extract.py", line 22, in download_file_from_s3
s3 = boto3.resource('s3')
File "./lib.zip/boto3/__init__.py", line 100, in resource
return _get_default_session().resource(*args, **kwargs)
File "./lib.zip/boto3/session.py", line 347, in resource
has_low_level_client)
ResourceNotExistsError: The 's3' resource does not exist.
The available resources are:
-
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:207)
at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:70)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:927)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
at org.apache.spark.rdd.RDD.collect(RDD.scala:926)
at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:405)
at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
at py4j.Gateway.invoke(Gateway.java:259)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:209)
at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/mnt/yarn/usercache/chqiang/appcache/application_1521024688288_67008/container_1521024688288_67008_01_000002/pyspark.zip/pyspark/worker.py", line 111, in main
process()
File "/mnt/yarn/usercache/chqiang/appcache/application_1521024688288_67008/container_1521024688288_67008_01_000002/pyspark.zip/pyspark/worker.py", line 106, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/mnt/yarn/usercache/chqiang/appcache/application_1521024688288_67008/container_1521024688288_67008_01_000002/pyspark.zip/pyspark/serializers.py", line 263, in dump_stream
vs = list(itertools.islice(iterator, batch))
File "/usr/local/lib/python2.7/site-packages/extract-1.0-py2.7.egg/extract.py", line 111, in <lambda>
File "./lib.zip/extract.py", line 115, in process
local_path = download_file_from_s3(self.app_name, file_path)
File "./lib.zip/extract.py", line 22, in download_file_from_s3
s3 = boto3.resource('s3')
File "./lib.zip/boto3/__init__.py", line 100, in resource
return _get_default_session().resource(*args, **kwargs)
File "./lib.zip/boto3/session.py", line 347, in resource
has_low_level_client)
ResourceNotExistsError: The 's3' resource does not exist.
The available resources are:
-
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:207)
at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:70)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
... 1 more
Could you please help me? thank you!
I ran into the same error today. I tried fixing it using client instead of resource, however that gave out another error.
botocore.exceptions.DataNotFoundError: Unable to load data for: endpoints
I googled a bit and came to the conclusion that it is not possible to zip up the boto3 package since packaging it results in loss of a few .json files. The endpoints.json is one of them. It exists in your botocore/data directory however when you zip it, it doesn't have it.
The solution is to install boto3 during bootstrap. You can create a bootstrap file and provide it during the cluster build-up process (there is an option in the AWS EMR console for it). This will install boto3 on the master node and all of the slave nodes.
You can refer to the following answer as well :
boto3 cannot create client on pyspark worker?
I packaged the dependency packages as whl files instead of zip packages, then added them all to the --py-files parameter(eg. a.whl,b.whl,c.whl), changed s3=boto3.resource('s3') in the code to client=boto3.client('s3') and tried to succeed
I configured a new system, spark 2.3.0, python 3.6.0, dataframe read and other operations working as expected.
But, RDD collect is failing -
distFile = spark.sparkContext.textFile("/Users/aakash/Documents/Final_HOME_ORIGINAL/Downloads/PreloadedDataset/breast-cancer-wisconsin.csv")
distFile.collect()
Error:
py4j.protocol.Py4JJavaError: An error occurred while calling
z:org.apache.spark.api.python.PythonRDD.collectAndServe.
Traceback:
Traceback (most recent call last):
File "/Users/aakash/Documents/Final_HOME_ORIGINAL/PycharmProjects/AllMyRnD/BB_AutoML_Blocks/Test.py", line 15, in <module>
distFile.collect()
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pyspark/rdd.py", line 824, in collect
port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/py4j/java_gateway.py", line 1160, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/py4j/protocol.py", line 320, in get_return_value
format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: java.lang.IllegalArgumentException
at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
at org.apache.spark.util.ClosureCleaner$.getClassReader(ClosureCleaner.scala:46)
at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:449)
at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:432)
at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733)
at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103)
at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103)
at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:230)
at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
at scala.collection.mutable.HashMap$$anon$1.foreach(HashMap.scala:103)
at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732)
at org.apache.spark.util.FieldAccessFinder$$anon$3.visitMethodInsn(ClosureCleaner.scala:432)
at org.apache.xbean.asm5.ClassReader.a(Unknown Source)
at org.apache.xbean.asm5.ClassReader.b(Unknown Source)
at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:262)
at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:261)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:261)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:159)
at org.apache.spark.SparkContext.clean(SparkContext.scala:2292)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2066)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2092)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:939)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.collect(RDD.scala:938)
at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:153)
at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:564)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.base/java.lang.Thread.run(Thread.java:844)
I followed this solution for similar problem, (ERROR WHILE RUNNING collect() in PYSPARK) installed latest Java, but still of no use.
What to do?
spark has some compatibility issues with the current latest version of java. The best solution for the moment is
uninstall Java 10
install Java 8 instead