Develop Reference

How to avoid java.io.StreamCorruptedException: invalid stream header: 204356EC when using toPandas() with PySpark?

Whenever I try to read a Spark dataset using PySpark and convert it to a Pandas df for modeling I get the error: java.io.StreamCorruptedException: invalid stream header: 204356EC on the toPandas() step.
I am not a Java coder (hence PySpark) and so these errors can be pretty cryptic to me. I tried the following things, but I still have this issue:
Made sure my Spark and PySpark versions matched as suggested here: java.io.StreamCorruptedException when importing a CSV to a Spark DataFrame
Reinstalled Spark using the methods suggested here: Complete Guide to Installing PySpark on MacOS
The logging in the test script below verifies the Spark and PySpark versions are aligned.
test.py:
import logging
from pyspark.sql import SparkSession
from pyspark import SparkContext
import findspark
findspark.init()
logging.basicConfig(
format='%(asctime)s %(levelname)-8s %(message)s',
level=logging.INFO,
datefmt='%Y-%m-%d %H:%M:%S')
sc = SparkContext('local[*]', 'test')
spark = SparkSession(sc)
logging.info('Spark location: {}'.format(findspark.find()))
logging.info('PySpark version: {}'.format(spark.sparkContext.version))
logging.info('Reading spark input dataframe')
test_df = spark.read.csv('./data', header=True, sep='|', inferSchema=True)
logging.info('Converting spark DF to pandas DF')
pandas_df = test_df.toPandas()
logging.info('DF record count: {}'.format(len(pandas_df)))
sc.stop()
Output:
$ python ./test.py
21/05/13 11:54:32 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
2021-05-13 11:54:34 INFO Spark location: /Users/username/server/spark-3.1.1-bin-hadoop2.7
2021-05-13 11:54:34 INFO PySpark version: 3.1.1
2021-05-13 11:54:34 INFO Reading spark input dataframe
2021-05-13 11:54:42 INFO Converting spark DF to pandas DF
21/05/13 11:54:42 WARN package: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.sql.debug.maxToStringFields'.
21/05/13 11:54:45 ERROR TaskResultGetter: Exception while getting task result12]
java.io.StreamCorruptedException: invalid stream header: 204356EC
at java.io.ObjectInputStream.readStreamHeader(ObjectInputStream.java:936)
at java.io.ObjectInputStream.<init>(ObjectInputStream.java:394)
at org.apache.spark.serializer.JavaDeserializationStream$$anon$1.<init>(JavaSerializer.scala:64)
at org.apache.spark.serializer.JavaDeserializationStream.<init>(JavaSerializer.scala:64)
at org.apache.spark.serializer.JavaSerializerInstance.deserializeStream(JavaSerializer.scala:123)
at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:108)
at org.apache.spark.scheduler.TaskResultGetter$$anon$3.$anonfun$run$1(TaskResultGetter.scala:97)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1996)
at org.apache.spark.scheduler.TaskResultGetter$$anon$3.run(TaskResultGetter.scala:63)
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)
Traceback (most recent call last):
File "./test.py", line 23, in <module>
pandas_df = test_df.toPandas()
File "/Users/username/server/spark-3.1.1-bin-hadoop2.7/python/pyspark/sql/pandas/conversion.py", line 141, in toPandas
pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns)
File "/Users/username/server/spark-3.1.1-bin-hadoop2.7/python/pyspark/sql/dataframe.py", line 677, in collect
sock_info = self._jdf.collectToPython()
File "/Users/username/server/spark-3.1.1-bin-hadoop2.7/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1304, in __call__
File "/Users/username/server/spark-3.1.1-bin-hadoop2.7/python/pyspark/sql/utils.py", line 111, in deco
return f(*a, **kw)
File "/Users/username/server/spark-3.1.1-bin-hadoop2.7/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 326, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o31.collectToPython.
: org.apache.spark.SparkException: Job aborted due to stage failure: Exception while getting task result: java.io.StreamCorruptedException: invalid stream header: 204356EC
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2253)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2202)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2201)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2201)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1078)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1078)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1078)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2440)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2382)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2371)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2202)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2223)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2242)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2267)
at org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1030)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:414)
at org.apache.spark.rdd.RDD.collect(RDD.scala:1029)
at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:390)
at org.apache.spark.sql.Dataset.$anonfun$collectToPython$1(Dataset.scala:3519)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3687)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:772)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3685)
at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:3516)
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)

The issue was resolved for me by ensuring that the serialisation option (registered in configuration under spark.serlializer) was not incompatible with pyarrow (typically used during the conversion of pandas to pyspark and vice versa if you've got it enabled).
The fix was to remove the often recommended spark.serializer: org.apache.spark.serializer.KryoSerializer from the configuration and rely instead on the potentially slower default.
For context, our set-up was with a ML version of the databricks spark cluster (v7.3).

I have this exception with Spark Thrift server.
Driver version and cluster version was different.
In my case i delete this, for using version from driver in all cluster.
spark.yarn.archive=hdfs:///spark/3.1.1.zip

Using drools in a Spark job

I am trying to use drools in spark job submitted to a cluster. THe job will start by getting the drools jar from a drools server then initialize the base and sessions.
My code work when executed in Spark but when submitting to spark cluster a NPE happens.
This is how I am doing
String url = "{my server address}/drools-wb/maven2/com/myspace/Project1/1.0.0/Project1-1.0.0.jar";
KieServices ks = KieServices.Factory.get();
//ERROR is in the below line
ReleaseId releaseId = ks.newReleaseId("com.myspace", "Project1", "1.0.0");
KieRepository kr = ks.getRepository();
UrlResource urlResource = (UrlResource) ks.getResources().newUrlResource(url);
The error shown after submitting the code:
Exception in thread "main" java.lang.NullPointerException
at org.opencell.spark.jobs.TestWithDrools.main(TestWithDrools.java:47)
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.JavaMainApplication.start(SparkApplication.scala:52)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:879)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:197)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:227)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:136)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
2018-08-29 10:08:09 INFO ShutdownHookManager:54 - Shutdown hook called
Do you have an idea about solving this issue ?

The issue is caused because ks is null.
So to resolve this issue please refer to this post:
Drools 7.4.1 kieservices.factory.get() returns null

Spark and Spring boot integration

We are trying to integrate Spark and Spring boot, unfortunately we are facing each time lot of issues. After resolving the most of them, we are now stuck on the exception below
Job aborted due to stage failure: Task 0 in stage 11.0 failed 4 times, most recent failure: Lost task 0.3 in stage 11.0 (TID 14, xxxxx.ax.internal.cloudapp.net, executor 1): java.lang.ClassCastException: cannot assign instance of scala.collection.immutable.List$SerializationProxy to field org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$dependencies_ of type scala.collection.Seq in instance of org.apache.spark.rdd.MapPartitionsRDD
at java.io.ObjectStreamClass$FieldReflector.setObjFieldValues(ObjectStreamClass.java:2233)
at java.io.ObjectStreamClass.setObjFieldValues(ObjectStreamClass.java:1405)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2284)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:2202)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2060)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1567)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2278)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:2202)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:2060)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1567)
at java.io.ObjectInputStream.readObject(ObjectInputStream.java:427)
at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:75)
at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:114)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:80)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala: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$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1928)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1941)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1954)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:336)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:2386)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withNewExecutionId(Dataset.scala:2788)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2385)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2392)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2128)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2127)
at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2818)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2127)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2342)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:248)
at org.apache.spark.sql.Dataset.show(Dataset.scala:638)
at org.apache.spark.sql.Dataset.show(Dataset.scala:597)
at org.apache.spark.sql.Dataset.show(Dataset.scala:606)
at com.xxx.xxx.spark.Execute.run(Execute.java:46)
at com.xxx.xxx.spark.Loader.process(Loader.java:505)
at com.xxx.xxx.spark.Loader.run(Loader.java:122)
at org.springframework.boot.SpringApplication.callRunner(SpringApplication.java:732)
at org.springframework.boot.SpringApplication.callRunners(SpringApplication.java:716)
at org.springframework.boot.SpringApplication.afterRefresh(SpringApplication.java:703)
at org.springframework.boot.SpringApplication.run(SpringApplication.java:304)
at org.springframework.boot.SpringApplication.run(SpringApplication.java:1118)
at org.springframework.boot.SpringApplication.run(SpringApplication.java:1107)
at com.xxx.xxx.spark.AnalyseFec.main(AnalyseFec.java:11)
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)
This exception is raised when trying to manipulate a transformed data set (created with a map). The count and collect methods works fine,
The sample below throw the exception on dataf22.show();
StructType schemata = DataTypes.createStructType(
new StructField[]{
DataTypes.createStructField("Column1", DataTypes.StringType, false),
DataTypes.createStructField("Column2", DataTypes.DoubleType, false),
DataTypes.createStructField("Column3", DataTypes.DoubleType, false),
});
ExpressionEncoder<Row> encoder = RowEncoder.apply(schemata);
Dataset<Row> dataf2 = session.read()
.option("header", "true")
.option("delimiter",separateur)
// .schema(schemata)
.csv(csvPath);
dataf2.write().mode(SaveMode.Overwrite).parquet("xxx.parquet");
Dataset<Row> parquetFileDF = session.read().parquet("xxx.parquet");
Dataset<Row> dataf22 = parquetFileDF.map(row -> {
return RowFactory.create(row.getAs("Column1"),
Double.parseDouble(row.getAs("Column2").toString().replace(",", ".")),
Double.parseDouble(row.getAs("Column3").toString().replace(",", ".")));
}, encoder);
dataf22.printSchema();
dataf22.show();
dataf22.groupBy("Column1");
Dataset<Row> ds1 = dataf22.groupBy("Column1").sum("Column2");
ds1.show();
Dataset<Row> ds2 = dataf22.groupBy("Column1").sum("Column3");
ds2.show();
Initially we were packaging using the spring-boot-maven-plugin, the spark-submit was calling the org.springframework.boot.loader.JarLauncher that launch our starter class.
When we moved to maven-shade-plugin with some modification to support spring boot, the exception above disappear and we were able to execute our program, but only in client mode. In cluster mode the application is never running in Yarn, after multiple attempt the application Fail without any error that can help to fix the issue.
I feel that once the program is executed on executors, the problem will appear related to classpath or classloader issues
Did you succeed to made this integration working ? If yes, what maven plugin did you used ? what extra parameters of spark-submit command did you uses ( extraclasspath … )
Thank you

Spark Streaming with Elasticsearch connector throws JVM_Bind error

I am using Spark 2.1.1 in Java and elasticsearch-spark-20_2.11 (version 5.3.2) in order to write data in Elasticsearch.I create JavaStreamingContext which I then set to await termination, so the application should always retrieve new data.
After I read the stream, I split it into RDDs and for each one I apply SQL aggregations and then write it to Elasticsearch as follows:
recordStream.foreachRDD(rdd -> {
if (rdd.count() > 0) {
/*
* Create RDD from JSON
*/
Dataset<Row> df = spark.read().json(rdd.rdd());
df.createOrReplaceTempView("data");
df.cache();
/*
* Apply the aggregations
*/
Dataset aggregators = spark.sql(ORDER_TYPE_DB);
JavaEsSparkSQL.saveToEs(aggregators.toDF(), "order_analytics/record");
aggregators = spark.sql(ORDER_CUSTOMER_DB);
JavaEsSparkSQL.saveToEs(aggregators.toDF(), "customer_analytics/record");
}
});
This works fine the first time data is read and inserted to Elasticsearch, but when more data are retrieved by the stream, I get the following error:
org.elasticsearch.hadoop.EsHadoopIllegalArgumentException: Cannot detect ES version - typically this happens if the network/Elasticsearch cluster is not accessible or when targeting a WAN/Cloud instance without the proper setting 'es.nodes.wan.only'
at org.elasticsearch.hadoop.rest.InitializationUtils.discoverEsVersion(InitializationUtils.java:250)
at org.elasticsearch.hadoop.rest.RestService.createWriter(RestService.java:546)
at org.elasticsearch.spark.rdd.EsRDDWriter.write(EsRDDWriter.scala:58)
at org.elasticsearch.spark.sql.EsSparkSQL$$anonfun$saveToEs$1.apply(EsSparkSQL.scala:94)
at org.elasticsearch.spark.sql.EsSparkSQL$$anonfun$saveToEs$1.apply(EsSparkSQL.scala:94)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: org.elasticsearch.hadoop.rest.EsHadoopTransportException: java.net.BindException: Address already in use: JVM_Bind
at org.elasticsearch.hadoop.rest.NetworkClient.execute(NetworkClient.java:129)
at org.elasticsearch.hadoop.rest.RestClient.execute(RestClient.java:461)
at org.elasticsearch.hadoop.rest.RestClient.execute(RestClient.java:425)
at org.elasticsearch.hadoop.rest.RestClient.execute(RestClient.java:429)
at org.elasticsearch.hadoop.rest.RestClient.get(RestClient.java:155)
at org.elasticsearch.hadoop.rest.RestClient.remoteEsVersion(RestClient.java:627)
at org.elasticsearch.hadoop.rest.InitializationUtils.discoverEsVersion(InitializationUtils.java:243)
... 10 more
Caused by: java.net.BindException: Address already in use: JVM_Bind
at java.net.DualStackPlainSocketImpl.bind0(Native Method)
at java.net.DualStackPlainSocketImpl.socketBind(DualStackPlainSocketImpl.java:106)
at java.net.AbstractPlainSocketImpl.bind(AbstractPlainSocketImpl.java:387)
at java.net.PlainSocketImpl.bind(PlainSocketImpl.java:190)
at java.net.Socket.bind(Socket.java:644)
at java.net.Socket.<init>(Socket.java:433)
at java.net.Socket.<init>(Socket.java:286)
at org.apache.commons.httpclient.protocol.DefaultProtocolSocketFactory.createSocket(DefaultProtocolSocketFactory.java:80)
at org.apache.commons.httpclient.protocol.DefaultProtocolSocketFactory.createSocket(DefaultProtocolSocketFactory.java:122)
at org.apache.commons.httpclient.HttpConnection.open(HttpConnection.java:707)
at org.apache.commons.httpclient.HttpMethodDirector.executeWithRetry(HttpMethodDirector.java:387)
at org.apache.commons.httpclient.HttpMethodDirector.executeMethod(HttpMethodDirector.java:171)
at org.apache.commons.httpclient.HttpClient.executeMethod(HttpClient.java:397)
at org.apache.commons.httpclient.HttpClient.executeMethod(HttpClient.java:323)
at org.elasticsearch.hadoop.rest.commonshttp.CommonsHttpTransport.execute(CommonsHttpTransport.java:478)
at org.elasticsearch.hadoop.rest.NetworkClient.execute(NetworkClient.java:112)
... 16 more
Any ideas what the problem could be?
Spark uses default configuration and is instantiated in Java as
SparkConf conf = new SparkConf().setAppName(topic).setMaster("local");
JavaStreamingContext streamingContext = new JavaStreamingContext(conf, Durations.seconds(2));
Elasticsearch is configured via Docker compose with the following environment parameters:
- cluster.name=cp-es-cluster
- node.name=cloud1
- http.cors.enabled=true
- http.cors.allow-origin="*"
- network.host=0.0.0.0
- discovery.zen.ping.unicast.hosts=${ENV_IP}
- network.publish_host=${ENV_IP}
- discovery.zen.minimum_master_nodes=1
- xpack.security.enabled=false
- xpack.monitoring.enabled=false

TApplicationException exception when running a mapreduce job on an Accumulo Table

I am running a map reduce job taking data from a table in Accumulo as the input and storing the result in another table in Accumulo. To do this, I am using the AccumuloInputFormat and AccumuloOutputFormat classes. Here is the code
public int run(String[] args) throws Exception {
Opts opts = new Opts();
opts.parseArgs(PivotTable.class.getName(), args);
Configuration conf = getConf();
conf.set("formula", opts.formula);
Job job = Job.getInstance(conf);
job.setJobName("Pivot Table Generation");
job.setJarByClass(PivotTable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setMapperClass(PivotTableMapper.class);
job.setCombinerClass(PivotTableCombiber.class);
job.setReducerClass(PivotTableReducer.class);
job.setInputFormatClass(AccumuloInputFormat.class);
ClientConfiguration zkConfig = new ClientConfiguration().withInstance(opts.getInstance().getInstanceName()).withZkHosts(opts.getInstance().getZooKeepers());
AccumuloInputFormat.setInputTableName(job, opts.dataTable);
AccumuloInputFormat.setZooKeeperInstance(job, zkConfig);
AccumuloInputFormat.setConnectorInfo(job, opts.getPrincipal(), new PasswordToken(opts.getPassword().value));
job.setOutputFormatClass(AccumuloOutputFormat.class);
BatchWriterConfig bwConfig = new BatchWriterConfig();
AccumuloOutputFormat.setBatchWriterOptions(job, bwConfig);
AccumuloOutputFormat.setZooKeeperInstance(job, zkConfig);
AccumuloOutputFormat.setConnectorInfo(job, opts.getPrincipal(), new PasswordToken(opts.getPassword().value));
AccumuloOutputFormat.setDefaultTableName(job, opts.pivotTable);
AccumuloOutputFormat.setCreateTables(job, true);
return job.waitForCompletion(true) ? 0 : 1;
}
PivotTable is the name of the class that contains the main method (and this one too). I have made the mapper, combiner and reducer classes as well. But when I try to exectute this job, I get an error
Exception in thread "main" java.io.IOException: org.apache.accumulo.core.client.AccumuloException: org.apache.thrift.TApplicationException: Internal error processing hasTablePermission
at org.apache.accumulo.core.client.mapreduce.lib.impl.InputConfigurator.validatePermissions(InputConfigurator.java:707)
at org.apache.accumulo.core.client.mapreduce.AbstractInputFormat.validateOptions(AbstractInputFormat.java:397)
at org.apache.accumulo.core.client.mapreduce.AbstractInputFormat.getSplits(AbstractInputFormat.java:668)
at org.apache.hadoop.mapreduce.JobSubmitter.writeNewSplits(JobSubmitter.java:301)
at org.apache.hadoop.mapreduce.JobSubmitter.writeSplits(JobSubmitter.java:318)
at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:196)
at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1290)
at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1287)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1657)
at org.apache.hadoop.mapreduce.Job.submit(Job.java:1287)
at org.apache.hadoop.mapreduce.Job.waitForCompletion(Job.java:1308)
at com.latize.ulysses.accumulo.postprocess.PivotTable.run(PivotTable.java:247)
at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:70)
at com.latize.ulysses.accumulo.postprocess.PivotTable.main(PivotTable.java:251)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.hadoop.util.RunJar.run(RunJar.java:221)
at org.apache.hadoop.util.RunJar.main(RunJar.java:136)
Caused by: org.apache.accumulo.core.client.AccumuloException: org.apache.thrift.TApplicationException: Internal error processing hasTablePermission
at org.apache.accumulo.core.client.impl.SecurityOperationsImpl.execute(SecurityOperationsImpl.java:87)
at org.apache.accumulo.core.client.impl.SecurityOperationsImpl.hasTablePermission(SecurityOperationsImpl.java:220)
at org.apache.accumulo.core.client.mapreduce.lib.impl.InputConfigurator.validatePermissions(InputConfigurator.java:692)
... 21 more
Caused by: org.apache.thrift.TApplicationException: Internal error processing hasTablePermission
at org.apache.thrift.TApplicationException.read(TApplicationException.java:111)
at org.apache.thrift.TServiceClient.receiveBase(TServiceClient.java:71)
at org.apache.accumulo.core.client.impl.thrift.ClientService$Client.recv_hasTablePermission(ClientService.java:641)
at org.apache.accumulo.core.client.impl.thrift.ClientService$Client.hasTablePermission(ClientService.java:624)
at org.apache.accumulo.core.client.impl.SecurityOperationsImpl$8.execute(SecurityOperationsImpl.java:223)
at org.apache.accumulo.core.client.impl.SecurityOperationsImpl$8.execute(SecurityOperationsImpl.java:220)
at org.apache.accumulo.core.client.impl.ServerClient.executeRaw(ServerClient.java:79)
at org.apache.accumulo.core.client.impl.SecurityOperationsImpl.execute(SecurityOperationsImpl.java:73)
Can someone tell me what am I doing wrong here? Any help would be appreciated.
EDIT : I am running Accumulo 1.7.0

A TApplicationException indicates the error occurred on the Accumulo tablet server, rather than in your client (MapReduce) code. You'll need to examine your tablet server logs to get more information about the particular error wherever you see TApplicationException.
Table permissions are usually retrieved from ZooKeeper, so it may indicate a problem with the tserver connecting to ZooKeeper.
Unfortunately, I don't see the hostname or the IP in the stack trace, so you may have to check all the tserver logs to find it.