I am writing data to Parquet files using Spark, reading data output from AWS Kinesis in an hourly fashion based upon AWS Kinesis hourly partitions.
When writing, I partition the data output by year/month/day/hour/eventType, and then append & save to S3:
fooDf
.withColumn("timestamp_new", (col("timestamp").cast("timestamp")))
.drop("timestamp")
.withColumnRenamed("timestamp_new", "timestamp")
.withColumn("year", year(col("timestamp")))
.withColumn("month", month(col("timestamp")))
.withColumn("day", dayofmonth(col("timestamp")))
.withColumn("hour", hour(col("timestamp")))
.write
.option("mode", "DROPMALFORMED")
.mode("overwrite")
.partitionBy("year", "month", "day", "hour", "eventType")
.parquet("s3://foo/bar/foobar")
, but the problem arises when reading, I get incompatible data types, even though Parquet should handle schema updates. The issue is:
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:2041)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2029)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2028)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2028)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:966)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2262)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2211)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2200)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:777)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3383)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2544)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2544)
at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3364)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3363)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2544)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2758)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:254)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:291)
at org.apache.spark.sql.Dataset.show(Dataset.scala:745)
at org.apache.spark.sql.Dataset.show(Dataset.scala:704)
... 85 elided
Caused by: org.apache.spark.sql.execution.QueryExecutionException: Encounter error while reading parquet files. One possible cause: Parquet column cannot be converted in the corresponding files. Details:
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:193)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
... 3 more
Caused by: org.apache.parquet.io.ParquetDecodingException: Can not read value at 0 in block -1 in file s3://foo/bar/foobar/year=2019/month=9/day=5/hour=22/eventType=barbarbar/part-rawr-c000.snappy.parquet
at org.apache.parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:251)
at org.apache.parquet.hadoop.ParquetRecordReader.nextKeyValue(ParquetRecordReader.java:207)
at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:181)
... 22 more
Caused by: java.lang.ClassCastException: Expected instance of group converter but got "org.apache.spark.sql.execution.datasources.parquet.ParquetRowConverter$ParquetStringConverter"
at org.apache.parquet.io.api.Converter.asGroupConverter(Converter.java:34)
at org.apache.parquet.io.RecordReaderImplementation.<init>(RecordReaderImplementation.java:267)
at org.apache.parquet.io.MessageColumnIO$1.visit(MessageColumnIO.java:147)
at org.apache.parquet.io.MessageColumnIO$1.visit(MessageColumnIO.java:109)
at org.apache.parquet.filter2.compat.FilterCompat$NoOpFilter.accept(FilterCompat.java:165)
at org.apache.parquet.io.MessageColumnIO.getRecordReader(MessageColumnIO.java:109)
at org.apache.parquet.hadoop.InternalParquetRecordReader.checkRead(InternalParquetRecordReader.java:137)
at org.apache.parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:222)
... 26 more
This is a common problem as while reading Spark can not determine data type for eventType (e.g event=barbarbar)
in spark-submit or inside your code, set following before reading the file
spark.conf.set("spark.sql.sources.partitionColumnTypeInference.enabled", "false")
or read it with a Schema.
Related
Stack:
Spark 2.4.4
Hive 2.3.3
HBase 1.4.8
Issue: ClassCastException occurs when executing insertInto on HBase table
i've just read about that bug here https://issues.apache.org/jira/browse/SPARK-6628
but is there any solutions or fixes?
hive table created and mapped to hbase table with properties:
CREATE EXTERNAL TABLE hbase.h_table(
id string COMMENT '',
name string COMMENT '')
ROW FORMAT SERDE
'org.apache.hadoop.hive.hbase.HBaseSerDe'
STORED BY
'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
WITH SERDEPROPERTIES (
'hbase.columns.mapping'=':key,p:name',
'serialization.format'='1')
TBLPROPERTIES (
'hbase.mapred.output.outputtable'='h_table',
'hbase.table.name'='h_table',
'serialization.null.format'='NULL')
note that im unable to read table from hive atop hbase
val df = spark.sql("select * from hbase.h_table")) // that works
// hbase.h_table - hive database and table mapped to hbase table
df.write.mode("append").insertInto("hbase.h_table")
Stacktrace:
java.lang.ClassCastException: org.apache.hadoop.hive.hbase.HiveHBaseTableOutputFormat cannot be cast to org.apache.hadoop.hive.ql.io.HiveOutputFormat
at org.apache.spark.sql.hive.execution.HiveFileFormat$$anon$1.outputFormat$lzycompute(HiveFileFormat.scala:93)
at org.apache.spark.sql.hive.execution.HiveFileFormat$$anon$1.outputFormat(HiveFileFormat.scala:92)
at org.apache.spark.sql.hive.execution.HiveFileFormat$$anon$1.getFileExtension(HiveFileFormat.scala:96)
at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.newOutputWriter(FileFormatDataWriter.scala:114)
at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.<init>(FileFormatDataWriter.scala:108)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:236)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:170)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:169)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1889)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:167)
... 84 more
Caused by: java.lang.ClassCastException: org.apache.hadoop.hive.hbase.HiveHBaseTableOutputFormat cannot be cast to org.apache.hadoop.hive.ql.io.HiveOutputFormat
at org.apache.spark.sql.hive.execution.HiveFileFormat$$anon$1.outputFormat$lzycompute(HiveFileFormat.scala:93)
at org.apache.spark.sql.hive.execution.HiveFileFormat$$anon$1.outputFormat(HiveFileFormat.scala:92)
at org.apache.spark.sql.hive.execution.HiveFileFormat$$anon$1.getFileExtension(HiveFileFormat.scala:96)
at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.newOutputWriter(FileFormatDataWriter.scala:114)
at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.<init>(FileFormatDataWriter.scala:108)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:236)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:170)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:169)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
is there any solution without shc (HortonWorks spark-hbase connector)?
I'm trying to create a dataframe new_df and load the DataFrame to Kafka using pyspark. However, I'm getting few exception. Couldn't figure what exactly is the issue. Any help would be appreciated.
>>> dict = [{'name': 'Alice', 'age': 1},{'name': 'Again', 'age': 2}]
>>> df = spark.createDataFrame(dict)
>>> import time
>>> import datetime
>>> from pyspark.streaming.kafka import KafkaUtils
>>> timestamp = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S')
>>> type(timestamp)
<class 'str'>
>>> from pyspark.sql.functions import lit,unix_timestamp
>>> timestamp
'2017-08-02 16:16:14'
>>> new_df = df.withColumn('time',unix_timestamp(lit(timestamp),'yyyy-MM-dd HH:mm:ss').cast("timestamp"))
>>> new_df.show(truncate = False)
+---+-----+---------------------+
|age|name |time |
+---+-----+---------------------+
|1 |Alice|2017-08-02 16:16:14.0|
|2 |Again|2017-08-02 16:16:14.0|
+---+-----+---------------------+
Now I'm trying to wrtie the dataframe to a Kafka topic
def writeToKafka(outputDF):
outputDF.selectExpr("CAST(time AS STRING) AS key", "to_json(struct(*)) AS value") \
.write \
.format("kafka") \
.option("kafka.bootstrap.servers", "kafka-svc:9092") \
.option("topic", "test_topic") \
.save()
writeToKafka(new_df)
Exceptions(picked from error):
org.apache.spark.SparkException: Job aborted due to stage failure:
org.apache.kafka.common.KafkaException: Failed to construct kafka producer
org.apache.kafka.common.config.ConfigException: No resolvable bootstrap urls given in bootstrap.servers
Full Error:
Py4JJavaError: An error occurred while calling o1811.save. :
org.apache.spark.SparkException: Job aborted due to stage failure:
Task 8 in stage 76.0 failed 1 times, most recent failure: Lost task
8.0 in stage 76.0 (TID 110, localhost, executor driver): org.apache.kafka.common.KafkaException: Failed to construct kafka
producer at
org.apache.kafka.clients.producer.KafkaProducer.(KafkaProducer.java:432)
at
org.apache.kafka.clients.producer.KafkaProducer.(KafkaProducer.java:270)
at
org.apache.spark.sql.kafka010.CachedKafkaProducer$.org$apache$spark$sql$kafka010$CachedKafkaProducer$$createKafkaProducer(CachedKafkaProducer.scala:67)
at
org.apache.spark.sql.kafka010.CachedKafkaProducer$$anon$1.load(CachedKafkaProducer.scala:46)
at
org.apache.spark.sql.kafka010.CachedKafkaProducer$$anon$1.load(CachedKafkaProducer.scala:43)
at
org.spark_project.guava.cache.LocalCache$LoadingValueReference.loadFuture(LocalCache.java:3599)
at
org.spark_project.guava.cache.LocalCache$Segment.loadSync(LocalCache.java:2379) at
org.spark_project.guava.cache.LocalCache$Segment.lockedGetOrLoad(LocalCache.java:2342)
at
org.spark_project.guava.cache.LocalCache$Segment.get(LocalCache.java:2257)
at org.spark_project.guava.cache.LocalCache.get(LocalCache.java:4000)
at
org.spark_project.guava.cache.LocalCache.getOrLoad(LocalCache.java:4004)
at
org.spark_project.guava.cache.LocalCache$LocalLoadingCache.get(LocalCache.java:4874)
at
org.apache.spark.sql.kafka010.CachedKafkaProducer$.getOrCreate(CachedKafkaProducer.scala:80)
at
org.apache.spark.sql.kafka010.KafkaWriteTask.execute(KafkaWriteTask.scala:44)
at
org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1$$anonfun$apply$1.apply$mcV$sp(KafkaWriter.scala:89)
at
org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1$$anonfun$apply$1.apply(KafkaWriter.scala:89)
at
org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1$$anonfun$apply$1.apply(KafkaWriter.scala:89)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at
org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1.apply(KafkaWriter.scala:89)
at
org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1.apply(KafkaWriter.scala:87)
at
org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$28.apply(RDD.scala:980)
at
org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$28.apply(RDD.scala:980)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123) at
org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748) Caused by:
org.apache.kafka.common.config.ConfigException: No resolvable
bootstrap urls given in bootstrap.servers at
org.apache.kafka.clients.ClientUtils.parseAndValidateAddresses(ClientUtils.java:88)
at
org.apache.kafka.clients.ClientUtils.parseAndValidateAddresses(ClientUtils.java:47)
at
org.apache.kafka.clients.producer.KafkaProducer.(KafkaProducer.java:407)
... 31 more
Driver stacktrace: at
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1891)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1879)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1878)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at
org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1878)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:927)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:927)
at scala.Option.foreach(Option.scala:257) at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:927)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2112)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2061)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2050)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at
org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:738)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061) at
org.apache.spark.SparkContext.runJob(SparkContext.scala:2082) at
org.apache.spark.SparkContext.runJob(SparkContext.scala:2101) at
org.apache.spark.SparkContext.runJob(SparkContext.scala:2126) at
org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:980)
at
org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:978)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:385) at
org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:978) at
org.apache.spark.sql.kafka010.KafkaWriter$.write(KafkaWriter.scala:87)
at
org.apache.spark.sql.kafka010.KafkaSourceProvider.createRelation(KafkaSourceProvider.scala:254)
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:676)
at
org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
at
org.apache.spark.sql.execution.SQLExecution$$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$.withNewExecutionId(SQLExecution.scala:75)
at
org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:676)
at
org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:285)
at
org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:268)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498) at
py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at
py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at
py4j.Gateway.invoke(Gateway.java:282) at
py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79) at
py4j.GatewayConnection.run(GatewayConnection.java:238) at
java.lang.Thread.run(Thread.java:748) Caused by:
org.apache.kafka.common.KafkaException: Failed to construct kafka
producer at
org.apache.kafka.clients.producer.KafkaProducer.(KafkaProducer.java:432)
at
org.apache.kafka.clients.producer.KafkaProducer.(KafkaProducer.java:270)
at
org.apache.spark.sql.kafka010.CachedKafkaProducer$.org$apache$spark$sql$kafka010$CachedKafkaProducer$$createKafkaProducer(CachedKafkaProducer.scala:67)
at
org.apache.spark.sql.kafka010.CachedKafkaProducer$$anon$1.load(CachedKafkaProducer.scala:46)
at
org.apache.spark.sql.kafka010.CachedKafkaProducer$$anon$1.load(CachedKafkaProducer.scala:43)
at
org.spark_project.guava.cache.LocalCache$LoadingValueReference.loadFuture(LocalCache.java:3599)
at
org.spark_project.guava.cache.LocalCache$Segment.loadSync(LocalCache.java:2379) at
org.spark_project.guava.cache.LocalCache$Segment.lockedGetOrLoad(LocalCache.java:2342)
at
org.spark_project.guava.cache.LocalCache$Segment.get(LocalCache.java:2257)
at org.spark_project.guava.cache.LocalCache.get(LocalCache.java:4000)
at
org.spark_project.guava.cache.LocalCache.getOrLoad(LocalCache.java:4004)
at
org.spark_project.guava.cache.LocalCache$LocalLoadingCache.get(LocalCache.java:4874)
at
org.apache.spark.sql.kafka010.CachedKafkaProducer$.getOrCreate(CachedKafkaProducer.scala:80)
at
org.apache.spark.sql.kafka010.KafkaWriteTask.execute(KafkaWriteTask.scala:44)
at
org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1$$anonfun$apply$1.apply$mcV$sp(KafkaWriter.scala:89)
at
org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1$$anonfun$apply$1.apply(KafkaWriter.scala:89)
at
org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1$$anonfun$apply$1.apply(KafkaWriter.scala:89)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at
org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1.apply(KafkaWriter.scala:89)
at
org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1.apply(KafkaWriter.scala:87)
at
org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$28.apply(RDD.scala:980)
at
org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$28.apply(RDD.scala:980)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123) at
org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more Caused by: org.apache.kafka.common.config.ConfigException:
No resolvable bootstrap urls given in bootstrap.servers at
org.apache.kafka.clients.ClientUtils.parseAndValidateAddresses(ClientUtils.java:88)
at
org.apache.kafka.clients.ClientUtils.parseAndValidateAddresses(ClientUtils.java:47)
at
org.apache.kafka.clients.producer.KafkaProducer.(KafkaProducer.java:407)
... 31 more
Note:
I have 3 kafka brokers, 3 kafka zookeepers which are hosted on Kubernetes Cluster.
New code:
def writeToKafka(outputDF):
outputDF.selectExpr("CAST(time AS STRING) AS key", "to_json(struct(*)) AS value") \
.write \
.format("kafka") \
.option("kafka.bootstrap.servers", "kafka-svc.test_namespace:9092") \
.option("topic", "test_topic") \
.save()
The kafka brokers are on another namespace in kubernetes cluster. And, my jupyter notebook was on another namespace.
Once I tried with "kafka_service.namespace:portno" for the kafka.bootstrap.servers (i.e.,kafka-svc.test_namespace:9092 ), it worked
kafka-svc - is the kafka service name.
test_namespace - is the name of the namespace where kafka brokers are hosted
I'm trying to read a table using spark.
spark.table("table_name")
sc.sequenceFile(path, classOf[Text], classOf[Text], 1000).
map(x => x._2.toString.split(delimiter, -1))
Both work if there are no empty files and both fail with java.io.EOFException: /path/to/file/1612735495084_12eed62a-b1ee-4cf5-8b71-a87149acd9c8.sf not a SequenceFile if the table contains empty files.
Setting spark.sql.files.ignoreCorruptFiles=true did not help. Seems like 0 byte file is not considered corrupted.
I can't modify the source table, only my own code. Is there a way to ignore empty files when reading this table?
Using spark 2.2, scala 2.11
diagnostics: User class threw exception: org.apache.spark.SparkException: Job aborted.
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:224)
at org.apache.spark.sql.hive.execution.SaveAsHiveFile$class.saveAsHiveFile(SaveAsHiveFile.scala:86)
at org.apache.spark.sql.hive.execution.InsertIntoHiveTable.saveAsHiveFile(InsertIntoHiveTable.scala:66)
at org.apache.spark.sql.hive.execution.InsertIntoHiveTable.processInsert(InsertIntoHiveTable.scala:195)
at org.apache.spark.sql.hive.execution.InsertIntoHiveTable.run(InsertIntoHiveTable.scala:99)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)
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$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:656)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:656)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:656)
at org.apache.spark.sql.DataFrameWriter.insertInto(DataFrameWriter.scala:322)
at org.apache.spark.sql.DataFrameWriter.insertInto(DataFrameWriter.scala:308)
at ru.gjin.gjin.system.Main$.delayedEndpoint$ru$gjin$gjin$system$Main$1(Main.scala:30)
at ru.gjin.gjin.system.Main$delayedInit$body.apply(Main.scala:12)
at scala.Function0$class.apply$mcV$sp(Function0.scala:34)
at scala.runtime.AbstractFunction0.apply$mcV$sp(AbstractFunction0.scala:12)
at scala.App$$anonfun$main$1.apply(App.scala:76)
at scala.App$$anonfun$main$1.apply(App.scala:76)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.generic.TraversableForwarder$class.foreach(TraversableForwarder.scala:35)
at scala.App$class.main(App.scala:76)
at ru.gjin.gjin.system.Main$.main(Main.scala:12)
at ru.gjin.gjin.system.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$4.run(ApplicationMaster.scala:721)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 36305 in stage 9.0 failed 4 times, most recent failure: Lost task 36305.3 in stage 9.0 (TID 2694, hdp3-sp-024.dmp.vimpelcom.ru, executor 6): java.io.EOFException: host/path/to/file/1612735487120_c186faf5-72c0-4747-884b-58ce29433906.sf not a SequenceFile
at org.apache.hadoop.io.SequenceFile$Reader.init(SequenceFile.java:1964)
at org.apache.hadoop.io.SequenceFile$Reader.initialize(SequenceFile.java:1923)
at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1872)
at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1886)
at org.apache.hadoop.mapred.SequenceFileRecordReader.<init>(SequenceFileRecordReader.java:49)
at org.apache.hadoop.mapred.SequenceFileInputFormat.getRecordReader(SequenceFileInputFormat.java:64)
at org.apache.spark.rdd.HadoopRDD$$anon$1.liftedTree1$1(HadoopRDD.scala:257)
at org.apache.spark.rdd.HadoopRDD$$anon$1.<init>(HadoopRDD.scala:256)
at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:214)
at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:94)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.UnionRDD.compute(UnionRDD.scala:105)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1651)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1639)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1638)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1638)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1872)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1821)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1810)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:194)
... 37 more
Caused by: java.io.EOFException: host/path/to/file/1612735487120_c186faf5-72c0-4747-884b-58ce29433906.sf not a SequenceFile
at org.apache.hadoop.io.SequenceFile$Reader.init(SequenceFile.java:1964)
at org.apache.hadoop.io.SequenceFile$Reader.initialize(SequenceFile.java:1923)
at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1872)
at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1886)
at org.apache.hadoop.mapred.SequenceFileRecordReader.<init>(SequenceFileRecordReader.java:49)
at org.apache.hadoop.mapred.SequenceFileInputFormat.getRecordReader(SequenceFileInputFormat.java:64)
at org.apache.spark.rdd.HadoopRDD$$anon$1.liftedTree1$1(HadoopRDD.scala:257)
at org.apache.spark.rdd.HadoopRDD$$anon$1.<init>(HadoopRDD.scala:256)
at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:214)
at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:94)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.UnionRDD.compute(UnionRDD.scala:105)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
An "empty" SequenceFile will still contain some header info. For example, create a new empty SequenceFile configured to contain LongWritable and check what's inside:
SEQ^F!org.apache.hadoop.io.LongWritable!org.apache.hadoop.io.LongWritable^A^#*org.apache.hadoop.io.compress.DefaultCodec^#^#^#^#ï<9c>p<84>º74K=æÅ3!<92>^A^F
I think that empty SequenceFile (a file which size is 0) is indeed corrupted.
UPD:
You can enable malformed file filtering like this:
val conf = new SparkConf();
// ...
conf.set("spark.files.ignoreCorruptFiles", "true")
Based on the code in the link https://alexwlchan.net/2019/09/unpacking-compressed-archives-in-scala/ I have a UDF in scala to extract tar.gz files (of size 6GB) which contain upto 1000 json documents
val udfExtract = udf((data: Array[Byte]) => Unpacker(data) : Map[String,String])
The UDF returns a map of key (json file name) and value (actual json content) pairs i.e upto 1000 pairs for each tar.gz file
Binary File Schema
val binaryFileSchema = StructType( Array(
StructField( "path", StringType, true),
StructField( "modificationTime", TimestampType, true),
StructField( "length", LongType, true),
StructField( "content", BinaryType,true)))
Load Data where the input path contains 24 tar.gz files
val binaryDF = spark.read
.format("binaryFile").option("pathGlobFilter", "*.tar.gz")
.schema( binaryFileSchema)
.load(input_path)
Extract content using UDF and apply a pre defined schema to parse the extracted content
val parseDF = binaryDF.withColumn("extracted_content", udfExtract($"content"))
.withColumn("file_path", $"path")
.select($"file_path",explode($"extracted_content")).toDF("file_path", "key", "value")
.withColumn("my_json", from_json($"value", MySchema))
.persist()
Write to Table
parseDF.select($"my_json")
.write.format("delta").mode("append")
.saveAsTable("default.raw_data")
When executing the code I get OutOfMemoryError: Java heap space
I feel this OOM error maybe because of using the above UDF to extract the huge compressed files (50GB after uncomperssion) or is maybe related to garbage collection. How can I resolve this?
Below is the stacktrace
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 7 in stage 2.0 failed 4 times, most recent failure: Lost task 7.3 in stage 2.0 (TID 59, 10.24.48.68, executor 1): com.databricks.sql.io.FileReadException: Error while reading file dbfs:/mnt/raw_data_1583107200.tar.gz.
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1$$anon$2.logFileNameAndThrow(FileScanRDD.scala:331)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1$$anon$2.getNext(FileScanRDD.scala:310)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:397)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:250)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:640)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.execution.columnar.CachedRDDBuilder$$anonfun$3$$anon$1.hasNext(InMemoryRelation.scala:137)
at org.apache.spark.storage.memory.MemoryStore.putIterator(MemoryStore.scala:221)
at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:299)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1235)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1226)
at org.apache.spark.storage.BlockManager.org$apache$spark$storage$BlockManager$$doPut(BlockManager.scala:1161)
at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1226)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:1045)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:364)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:315)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:60)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:353)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:317)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:60)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:353)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:317)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.doRunTask(Task.scala:140)
at org.apache.spark.scheduler.Task.run(Task.scala:113)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$13.apply(Executor.scala:537)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1541)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:543)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.OutOfMemoryError: Java heap space
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:2362)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2350)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2349)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2349)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:1102)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:1102)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1102)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2582)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2529)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2517)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:897)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2282)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:170)
at com.databricks.sql.transaction.tahoe.files.TransactionalWriteEdge$$anonfun$writeFiles$1$$anonfun$apply$1.apply(TransactionalWriteEdge.scala:160)
at com.databricks.sql.transaction.tahoe.files.TransactionalWriteEdge$$anonfun$writeFiles$1$$anonfun$apply$1.apply(TransactionalWriteEdge.scala:133)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withCustomExecutionEnv$1.apply(SQLExecution.scala:113)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:243)
at org.apache.spark.sql.execution.SQLExecution$.withCustomExecutionEnv(SQLExecution.scala:99)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:173)
at com.databricks.sql.transaction.tahoe.files.TransactionalWriteEdge$$anonfun$writeFiles$1.apply(TransactionalWriteEdge.scala:133)
at com.databricks.sql.transaction.tahoe.files.TransactionalWriteEdge$$anonfun$writeFiles$1.apply(TransactionalWriteEdge.scala:90)
at com.databricks.logging.UsageLogging$$anonfun$recordOperation$1.apply(UsageLogging.scala:428)
at com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:238)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:233)
at com.databricks.spark.util.PublicDBLogging.withAttributionContext(DatabricksSparkUsageLogger.scala:18)
at com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:275)
at com.databricks.spark.util.PublicDBLogging.withAttributionTags(DatabricksSparkUsageLogger.scala:18)
at com.databricks.logging.UsageLogging$class.recordOperation(UsageLogging.scala:409)
at com.databricks.spark.util.PublicDBLogging.recordOperation(DatabricksSparkUsageLogger.scala:18)
at com.databricks.spark.util.PublicDBLogging.recordOperation0(DatabricksSparkUsageLogger.scala:55)
at com.databricks.spark.util.DatabricksSparkUsageLogger.recordOperation(DatabricksSparkUsageLogger.scala:98)
at com.databricks.spark.util.UsageLogger$class.recordOperation(UsageLogger.scala:69)
at com.databricks.spark.util.DatabricksSparkUsageLogger.recordOperation(DatabricksSparkUsageLogger.scala:67)
at com.databricks.spark.util.UsageLogging$class.recordOperation(UsageLogger.scala:344)
at com.databricks.sql.transaction.tahoe.OptimisticTransaction.recordOperation(OptimisticTransaction.scala:82)
at com.databricks.sql.transaction.tahoe.metering.DeltaLogging$class.recordDeltaOperation(DeltaLogging.scala:108)
at com.databricks.sql.transaction.tahoe.OptimisticTransaction.recordDeltaOperation(OptimisticTransaction.scala:82)
at com.databricks.sql.transaction.tahoe.files.TransactionalWriteEdge$class.writeFiles(TransactionalWriteEdge.scala:90)
at com.databricks.sql.transaction.tahoe.OptimisticTransaction.writeFiles(OptimisticTransaction.scala:82)
at com.databricks.sql.transaction.tahoe.files.TransactionalWrite$class.writeFiles(TransactionalWrite.scala:110)
at com.databricks.sql.transaction.tahoe.OptimisticTransaction.writeFiles(OptimisticTransaction.scala:82)
at com.databricks.sql.transaction.tahoe.commands.WriteIntoDelta.write(WriteIntoDelta.scala:111)
at com.databricks.sql.transaction.tahoe.commands.CreateDeltaTableCommand$$anonfun$run$2.apply(CreateDeltaTableCommand.scala:119)
at com.databricks.sql.transaction.tahoe.commands.CreateDeltaTableCommand$$anonfun$run$2.apply(CreateDeltaTableCommand.scala:93)
at com.databricks.logging.UsageLogging$$anonfun$recordOperation$1.apply(UsageLogging.scala:428)
at com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:238)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:233)
at com.databricks.spark.util.PublicDBLogging.withAttributionContext(DatabricksSparkUsageLogger.scala:18)
at com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:275)
at com.databricks.spark.util.PublicDBLogging.withAttributionTags(DatabricksSparkUsageLogger.scala:18)
at com.databricks.logging.UsageLogging$class.recordOperation(UsageLogging.scala:409)
at com.databricks.spark.util.PublicDBLogging.recordOperation(DatabricksSparkUsageLogger.scala:18)
at com.databricks.spark.util.PublicDBLogging.recordOperation0(DatabricksSparkUsageLogger.scala:55)
at com.databricks.spark.util.DatabricksSparkUsageLogger.recordOperation(DatabricksSparkUsageLogger.scala:98)
at com.databricks.spark.util.UsageLogger$class.recordOperation(UsageLogger.scala:69)
at com.databricks.spark.util.DatabricksSparkUsageLogger.recordOperation(DatabricksSparkUsageLogger.scala:67)
at com.databricks.spark.util.UsageLogging$class.recordOperation(UsageLogger.scala:344)
at com.databricks.sql.transaction.tahoe.commands.CreateDeltaTableCommand.recordOperation(CreateDeltaTableCommand.scala:45)
at com.databricks.sql.transaction.tahoe.metering.DeltaLogging$class.recordDeltaOperation(DeltaLogging.scala:108)
at com.databricks.sql.transaction.tahoe.commands.CreateDeltaTableCommand.recordDeltaOperation(CreateDeltaTableCommand.scala:45)
at com.databricks.sql.transaction.tahoe.commands.CreateDeltaTableCommand.run(CreateDeltaTableCommand.scala:93)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$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$$anonfun$execute$1.apply(SparkPlan.scala:152)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:140)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$5.apply(SparkPlan.scala:193)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:189)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:140)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:117)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:115)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:711)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:711)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withCustomExecutionEnv$1.apply(SQLExecution.scala:113)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:243)
at org.apache.spark.sql.execution.SQLExecution$.withCustomExecutionEnv(SQLExecution.scala:99)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:173)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:711)
at org.apache.spark.sql.DataFrameWriter.createTable(DataFrameWriter.scala:509)
at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:488)
at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:431)
at line3f718bbfd7704a88872ddbd33faa7db546.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(command-392784447715347:4)
at line3f718bbfd7704a88872ddbd33faa7db546.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(command-392784447715347:63)
at line3f718bbfd7704a88872ddbd33faa7db546.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(command-392784447715347:65)
at line3f718bbfd7704a88872ddbd33faa7db546.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw.<init>(command-392784447715347:67)
at line3f718bbfd7704a88872ddbd33faa7db546.$read$$iw$$iw$$iw$$iw$$iw$$iw.<init>(command-392784447715347:69)
at line3f718bbfd7704a88872ddbd33faa7db546.$read$$iw$$iw$$iw$$iw$$iw.<init>(command-392784447715347:71)
at line3f718bbfd7704a88872ddbd33faa7db546.$read$$iw$$iw$$iw$$iw.<init>(command-392784447715347:73)
at line3f718bbfd7704a88872ddbd33faa7db546.$read$$iw$$iw$$iw.<init>(command-392784447715347:75)
at line3f718bbfd7704a88872ddbd33faa7db546.$read$$iw$$iw.<init>(command-392784447715347:77)
at line3f718bbfd7704a88872ddbd33faa7db546.$read$$iw.<init>(command-392784447715347:79)
at line3f718bbfd7704a88872ddbd33faa7db546.$read.<init>(command-392784447715347:81)
at line3f718bbfd7704a88872ddbd33faa7db546.$read$.<init>(command-392784447715347:85)
at line3f718bbfd7704a88872ddbd33faa7db546.$read$.<clinit>(command-392784447715347)
at line3f718bbfd7704a88872ddbd33faa7db546.$eval$.$print$lzycompute(<notebook>:7)
at line3f718bbfd7704a88872ddbd33faa7db546.$eval$.$print(<notebook>:6)
at line3f718bbfd7704a88872ddbd33faa7db546.$eval.$print(<notebook>)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:793)
at scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:1054)
at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:645)
at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:644)
at scala.reflect.internal.util.ScalaClassLoader$class.asContext(ScalaClassLoader.scala:31)
at scala.reflect.internal.util.AbstractFileClassLoader.asContext(AbstractFileClassLoader.scala:19)
at scala.tools.nsc.interpreter.IMain$WrappedRequest.loadAndRunReq(IMain.scala:644)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:576)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:572)
at com.databricks.backend.daemon.driver.DriverILoop.execute(DriverILoop.scala:215)
at com.databricks.backend.daemon.driver.ScalaDriverLocal$$anonfun$repl$1.apply$mcV$sp(ScalaDriverLocal.scala:202)
at com.databricks.backend.daemon.driver.ScalaDriverLocal$$anonfun$repl$1.apply(ScalaDriverLocal.scala:202)
at com.databricks.backend.daemon.driver.ScalaDriverLocal$$anonfun$repl$1.apply(ScalaDriverLocal.scala:202)
at com.databricks.backend.daemon.driver.DriverLocal$TrapExitInternal$.trapExit(DriverLocal.scala:714)
at com.databricks.backend.daemon.driver.DriverLocal$TrapExit$.apply(DriverLocal.scala:667)
at com.databricks.backend.daemon.driver.ScalaDriverLocal.repl(ScalaDriverLocal.scala:202)
at com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$9.apply(DriverLocal.scala:396)
at com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$9.apply(DriverLocal.scala:373)
at com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:238)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:233)
at com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:49)
at com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:275)
at com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:49)
at com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:373)
at com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:644)
at com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:644)
at scala.util.Try$.apply(Try.scala:192)
at com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:639)
at com.databricks.backend.daemon.driver.DriverWrapper.getCommandOutputAndError(DriverWrapper.scala:485)
at com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:597)
at com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:390)
at com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:337)
at com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:219)
at java.lang.Thread.run(Thread.java:748)
Caused by: com.databricks.sql.io.FileReadException: Error while reading file dbfs:/mnt/raw_data_1583107200.tar.gz.
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1$$anon$2.logFileNameAndThrow(FileScanRDD.scala:331)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1$$anon$2.getNext(FileScanRDD.scala:310)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:397)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:250)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:640)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.execution.columnar.CachedRDDBuilder$$anonfun$3$$anon$1.hasNext(InMemoryRelation.scala:137)
at org.apache.spark.storage.memory.MemoryStore.putIterator(MemoryStore.scala:221)
at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:299)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1235)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1226)
at org.apache.spark.storage.BlockManager.org$apache$spark$storage$BlockManager$$doPut(BlockManager.scala:1161)
at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1226)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:1045)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:364)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:315)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:60)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:353)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:317)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:60)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:353)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:317)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.doRunTask(Task.scala:140)
at org.apache.spark.scheduler.Task.run(Task.scala:113)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$13.apply(Executor.scala:537)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1541)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:543)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.OutOfMemoryError: Java heap space
I am trying to extract keywords from a column of menu names in a PySpark dataframe.
Below is how the keyword processor was generated. keywords is a list of keyword like ['sandwiches', 'burgers', ...].
from flashtext import KeywordProcessor
kp = KeywordProcessor()
for keyword in keywords:
kp.add_keyword(keyword)
I defined a function to extract keywords from menu names.
def extractKeywords(menu_name, kp=kp):
keywords = kp.extract_keywords(menu_name)
return keywords
However, errors occurred when I tried to apply this function to my PySpark dataframe.
from pyspark.sql.functions import udf
from pyspark.sql.types import ArrayType, StringType
extractKeywords = udf(extractKeywords, ArrayType(StringType()))
df = df.withColumn("keywords_extracted", extractKeywords(df["menu_name"]))
df.show()
The errors are like this:
Py4JJavaError: An error occurred while calling o86.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 4.0 failed 1 times, most recent failure: Lost task 0.0 in stage 4.0 (TID 87, localhost, executor driver): java.io.IOException: Cannot run program "
/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7
": error=2, No such file or directory
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1048)
at org.apache.spark.api.python.PythonWorkerFactory.startDaemon(PythonWorkerFactory.scala:197)
at org.apache.spark.api.python.PythonWorkerFactory.createThroughDaemon(PythonWorkerFactory.scala:122)
at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:95)
at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:117)
at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:109)
at org.apache.spark.sql.execution.python.BatchEvalPythonExec.evaluate(BatchEvalPythonExec.scala:77)
at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127)
at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.io.IOException: error=2, No such file or directory
at java.lang.UNIXProcess.forkAndExec(Native Method)
at java.lang.UNIXProcess.<init>(UNIXProcess.java:247)
at java.lang.ProcessImpl.start(ProcessImpl.java:134)
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1029)
... 30 more
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1889)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3389)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)
at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3369)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2550)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2764)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:254)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:291)
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.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.io.IOException: Cannot run program "
/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7
": error=2, No such file or directory
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1048)
at org.apache.spark.api.python.PythonWorkerFactory.startDaemon(PythonWorkerFactory.scala:197)
at org.apache.spark.api.python.PythonWorkerFactory.createThroughDaemon(PythonWorkerFactory.scala:122)
at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:95)
at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:117)
at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:109)
at org.apache.spark.sql.execution.python.BatchEvalPythonExec.evaluate(BatchEvalPythonExec.scala:77)
at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127)
at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:801)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
Caused by: java.io.IOException: error=2, No such file or directory
at java.lang.UNIXProcess.forkAndExec(Native Method)
at java.lang.UNIXProcess.<init>(UNIXProcess.java:247)
at java.lang.ProcessImpl.start(ProcessImpl.java:134)
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1029)
... 30 more
The error suggests this may be a problem of environment configuration. However, the PySpark environment seems okay because I am able to do dataframe / Spark SQL operation. Can anyone tell me how I can solve this problem? Thank you!
I have figured it out:
kp = KeywordProcessor()
for keyword in keywords:
kp.add_keyword(keyword)
df = df.withColumn(
"extracted_keyword",
udf(lambda x: kp.extract_keywords(x), ArrayType(StringType()))(orders.source_text_column)
)