I am trying to connect to azure cosmos db using mongo db api (spark mongo db connector )to export data to hdfs but I get the below exception:
Below is the complete stacktrace:
{ "_t" : "OKMongoResponse", "ok" : 0, "code" : 115, "errmsg" : "Command is not supported", "$err" : "Command is not supported" }
at com.mongodb.connection.ProtocolHelper.getCommandFailureException(ProtocolHelper.java:115)
at com.mongodb.connection.CommandProtocol.execute(CommandProtocol.java:107)
at com.mongodb.connection.DefaultServer$DefaultServerProtocolExecutor.execute(DefaultServer.java:159)
at com.mongodb.connection.DefaultServerConnection.executeProtocol(DefaultServerConnection.java:289)
at com.mongodb.connection.DefaultServerConnection.command(DefaultServerConnection.java:176)
at com.mongodb.operation.CommandOperationHelper.executeWrappedCommandProtocol(CommandOperationHelper.java:216)
at com.mongodb.operation.CommandOperationHelper.executeWrappedCommandProtocol(CommandOperationHelper.java:187)
at com.mongodb.operation.CommandOperationHelper.executeWrappedCommandProtocol(CommandOperationHelper.java:179)
at com.mongodb.operation.CommandOperationHelper.executeWrappedCommandProtocol(CommandOperationHelper.java:92)
at com.mongodb.operation.CommandOperationHelper.executeWrappedCommandProtocol(CommandOperationHelper.java:85)
at com.mongodb.operation.CommandReadOperation.execute(CommandReadOperation.java:55)
at com.mongodb.Mongo.execute(Mongo.java:810)
at com.mongodb.Mongo$2.execute(Mongo.java:797)
at com.mongodb.MongoDatabaseImpl.runCommand(MongoDatabaseImpl.java:137)
at com.mongodb.MongoDatabaseImpl.runCommand(MongoDatabaseImpl.java:131)
at com.mongodb.spark.rdd.partitioner.MongoSplitVectorPartitioner$$anonfun$partitions$2$$anonfun$4.apply(MongoSplitVectorPartitioner.scala:76)
at com.mongodb.spark.rdd.partitioner.MongoSplitVectorPartitioner$$anonfun$partitions$2$$anonfun$4.apply(MongoSplitVectorPartitioner.scala:76)
at scala.util.Try$.apply(Try.scala:192)
at com.mongodb.spark.rdd.partitioner.MongoSplitVectorPartitioner$$anonfun$partitions$2.apply(MongoSplitVectorPartitioner.scala:76)
at com.mongodb.spark.rdd.partitioner.MongoSplitVectorPartitioner$$anonfun$partitions$2.apply(MongoSplitVectorPartitioner.scala:75)
at com.mongodb.spark.MongoConnector$$anonfun$withDatabaseDo$1.apply(MongoConnector.scala:171)
at com.mongodb.spark.MongoConnector$$anonfun$withDatabaseDo$1.apply(MongoConnector.scala:171)
at com.mongodb.spark.MongoConnector.withMongoClientDo(MongoConnector.scala:154)
at com.mongodb.spark.MongoConnector.withDatabaseDo(MongoConnector.scala:171)
at com.mongodb.spark.rdd.partitioner.MongoSplitVectorPartitioner.partitions(MongoSplitVectorPartitioner.scala:75)
at com.mongodb.spark.rdd.MongoRDD.getPartitions(MongoRDD.scala:137)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.sql.Dataset.<init>(Dataset.scala:182)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:67)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:636)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:691)
Maven dependency added :
<dependency>
<groupId>org.mongodb.spark</groupId>
<artifactId>mongo-spark-connector_2.11</artifactId>
<version>2.2.0</version>
</dependency>
Code :
SparkSession spark = SparkSession.builder()
.getOrCreate();
jsc = new JavaSparkContext(spark.sparkContext());
HiveContext hiveContext = new org.apache.spark.sql.hive.HiveContext(jsc);
Dataset<Row> implicitDS = MongoSpark.load(jsc).toDF();
FYI :
implicitDS.count() gives 0
I am using MongoSplitVectorPartitioner. Updated with the complete stacktrace.
Related
I'm using Apache Hudi to write non partitioned table to AWS S3 and sync that to hive. Here's the DataSourceWriteOptions being used.
val hudiOptions: Map[String, String] = Map[String, String](
DataSourceWriteOptions.TABLE_TYPE_OPT_KEY -> "MERGE_ON_READ",
DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY -> "PERSON_ID",
DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY -> "",
DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY -> "UPDATED_DATE",
DataSourceWriteOptions.HIVE_PARTITION_FIELDS_OPT_KEY -> "",
DataSourceWriteOptions.HIVE_PARTITION_EXTRACTOR_CLASS_OPT_KEY -> classOf[NonPartitionedExtractor].getName,
DataSourceWriteOptions.HIVE_STYLE_PARTITIONING_OPT_KEY -> "true",
DataSourceWriteOptions.KEYGENERATOR_CLASS_OPT_KEY -> "org.apache.hudi.keygen.NonpartitionedKeyGenerator"
)
Table is being written successfully if partitioned but gives error in case I try to write non-partitioned table. Here's the error output snippet
Caused by: java.lang.NullPointerException
at org.apache.hudi.hadoop.utils.HoodieInputFormatUtils.getTableMetaClientForBasePath(HoodieInputFormatUtils.java:283)
at org.apache.hudi.hadoop.InputPathHandler.parseInputPaths(InputPathHandler.java:100)
at org.apache.hudi.hadoop.InputPathHandler.<init>(InputPathHandler.java:60)
at org.apache.hudi.hadoop.HoodieParquetInputFormat.listStatus(HoodieParquetInputFormat.java:81)
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:288)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:204)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
at org.apache.spark.rdd.RDD.getNumPartitions(RDD.scala:289)
at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.mapOutputStatisticsFuture$lzycompute(ShuffleExchangeExec.scala:83)
at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.mapOutputStatisticsFuture(ShuffleExchangeExec.scala:82)
at org.apache.spark.sql.execution.adaptive.ShuffleQueryStageExec.cancel(QueryStageExec.scala:152)
at org.apache.spark.sql.execution.adaptive.MaterializeExecutable.cancel(AdaptiveExecutable.scala:357)
at org.apache.spark.sql.execution.adaptive.AdaptiveExecutorRuntime.fail(AdaptiveExecutor.scala:280)
... 41 more
Here's the code for HoodieInputFormatUtils.getTableMetaClientForBasePath()
/**
* Extract HoodieTableMetaClient from a partition path(not base path).
* #param fs
* #param dataPath
* #return
* #throws IOException
*/
public static HoodieTableMetaClient getTableMetaClientForBasePath(FileSystem fs, Path dataPath) throws IOException {
int levels = HoodieHiveUtils.DEFAULT_LEVELS_TO_BASEPATH;
if (HoodiePartitionMetadata.hasPartitionMetadata(fs, dataPath)) {
HoodiePartitionMetadata metadata = new HoodiePartitionMetadata(fs, dataPath);
metadata.readFromFS();
levels = metadata.getPartitionDepth();
}
Path baseDir = HoodieHiveUtils.getNthParent(dataPath, levels);
LOG.info("Reading hoodie metadata from path " + baseDir.toString());
return new HoodieTableMetaClient(fs.getConf(), baseDir.toString());
}
Line 283 is LOG.info() which is causing NullPointerException. So it looks like that config values provided for partitioning have been messed up. This code is being run on AWS EMR.
Release label:emr-5.30.1
Hadoop distribution:Amazon 2.8.5
Applications:Hive 2.3.6, Spark 2.4.5
I doubt PARTITIONPATH_FIELD_OPT_KEY and HIVE_PARTITION_FIELDS_OPT_KEY should be left undefined.
To validate your config, I suggest going to https://doc.hcs.huawei.com/usermanual/mrs/mrs_01_24035.html
hoodie.datasource.write.partitionpath.field and hoodie.datasource.hive_sync.partition_fields are supposed be blank
hoodie.datasource.write.keygenerator.class -> org.apache.hudi.keygen.NonpartitionedKeyGenerator
hoodie.datasource.hive_sync.partition_extractor_class->org.apache.hudi.hive.NonPartitionedExtractor
I was facing hive sync issue on pySpark with Hudi 0.9.0, the above documentation helped.
I'm starting with Structured Spark Streaming with Kafka source and was following a simple tutorial. My Kafka server is OSS kafka. My producer source code is as follows
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import java.util.Properties;
public class LogGenerator {
public static void main(String[] args) throws Exception {
Properties prop = new Properties();
prop.load(ClassLoader.getSystemResourceAsStream("kafka-producer.properties"));
KafkaProducer<String, String> producer = new KafkaProducer<String, String>(prop);
for (int i=0; i<10000; i++) {
System.out.printf("%d ", i);
producer.send(new ProducerRecord<>("my-log-topic", Integer.toString(i), Integer.toString(i)));
}
}
}
The producer writes just (0,0) through (999,999)
The structured Streaming code to read from this topic is as follows
mport org.apache.spark.sql.SparkSession
object DimaStreamer {
//initialize configs
val spark: SparkSession = SparkSession.builder().master("local").appName("StreamingData").getOrCreate()
def main(args: Array[String]): Unit = {
val dfRefundStream = spark.readStream.format("kafka")
.option("kafka.bootstrap.servers", "0.0.0.0:9091,0.0.0.0:9092")
.option("subscribe", "my-log-topic")
.load()
import org.apache.spark.sql.functions._
dfRefundStream.printSchema()
dfRefundStream.select(col("value")).writeStream
.format("console")
.queryName("inittest")
.start()
.awaitTermination()
}
}
Its a maven project. The job is executed as follows with the --jars option used for passing the jars with comma separation.
spark-submit --class com.apple.arcadia.solr.batch.DimaStreamer --jars $JARSPATH target/mainproject-1.0-SNAPSHOT.jar
The job throws the following exception
19/06/13 15:10:05 INFO internals.ConsumerCoordinator: Setting newly assigned partitions [my-log-topic-1, my-log-topic-0] for group spark-kafka-source-0ce55060-09ad-430a-9916-e0063d8103fe--48657453-driver-0
19/06/13 15:10:06 INFO kafka010.KafkaSource: Initial offsets: {"my-log-topic":{"1":9878,"0":10122}}
19/06/13 15:10:06 INFO streaming.StreamExecution: Committed offsets for batch 0. Metadata OffsetSeqMetadata(0,1560463806021,Map(spark.sql.shuffle.partitions -> 200))
19/06/13 15:10:06 INFO kafka010.KafkaSource: GetBatch called with start = None, end = {"my-log-topic":{"1":9878,"0":10122}}
19/06/13 15:10:06 INFO kafka010.KafkaSource: Partitions added: Map()
19/06/13 15:10:06 INFO kafka010.KafkaSource: GetBatch generating RDD of offset range: KafkaSourceRDDOffsetRange(my-log-topic-0,10122,10122,None), KafkaSourceRDDOffsetRange(my-log-topic-1,9878,9878,None)
19/06/13 15:10:06 ERROR streaming.StreamExecution: Query inittest [id = 305e10aa-a446-4f42-a4e9-8d2372250fa8, runId = 2218049f-08a4-40bf-9b46-b7e898321b85] terminated with error
java.lang.NoSuchMethodError: org.apache.spark.sql.SQLContext.internalCreateDataFrame(Lorg/apache/spark/rdd/RDD;Lorg/apache/spark/sql/types/StructType;Z)Lorg/apache/spark/sql/Dataset;
at org.apache.spark.sql.kafka010.KafkaSource.getBatch(KafkaSource.scala:301)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$2$$anonfun$apply$7.apply(StreamExecution.scala:614)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$2$$anonfun$apply$7.apply(StreamExecution.scala:610)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at org.apache.spark.sql.execution.streaming.StreamProgress.foreach(StreamProgress.scala:25)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at org.apache.spark.sql.execution.streaming.StreamProgress.flatMap(StreamProgress.scala:25)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$2.apply(StreamExecution.scala:610)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$2.apply(StreamExecution.scala:610)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:279)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch(StreamExecution.scala:609)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(StreamExecution.scala:306)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$apply$mcZ$sp$1.apply(StreamExecution.scala:294)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$apply$mcZ$sp$1.apply(StreamExecution.scala:294)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:279)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1.apply$mcZ$sp(StreamExecution.scala:294)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches(StreamExecution.scala:290)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:206)
Exception in thread "stream execution thread for inittest [id = 305e10aa-a446-4f42-a4e9-8d2372250fa8, runId = 2218049f-08a4-40bf-9b46-b7e898321b85]" java.lang.NoSuchMethodError: org.apache.spark.sql.SQLContext.internalCreateDataFrame(Lorg/apache/spark/rdd/RDD;Lorg/apache/spark/sql/types/StructType;Z)Lorg/apache/spark/sql/Dataset;
at org.apache.spark.sql.kafka010.KafkaSource.getBatch(KafkaSource.scala:301)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$2$$anonfun$apply$7.apply(StreamExecution.scala:614)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$2$$anonfun$apply$7.apply(StreamExecution.scala:610)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at org.apache.spark.sql.execution.streaming.StreamProgress.foreach(StreamProgress.scala:25)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at org.apache.spark.sql.execution.streaming.StreamProgress.flatMap(StreamProgress.scala:25)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$2.apply(StreamExecution.scala:610)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$2.apply(StreamExecution.scala:610)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:279)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch(StreamExecution.scala:609)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(StreamExecution.scala:306)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$apply$mcZ$sp$1.apply(StreamExecution.scala:294)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$apply$mcZ$sp$1.apply(StreamExecution.scala:294)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:279)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1.apply$mcZ$sp(StreamExecution.scala:294)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches(StreamExecution.scala:290)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:206)
Exception in thread "main" org.apache.spark.sql.streaming.StreamingQueryException: org.apache.spark.sql.SQLContext.internalCreateDataFrame(Lorg/apache/spark/rdd/RDD;Lorg/apache/spark/sql/types/StructType;Z)Lorg/apache/spark/sql/Dataset;
=== Streaming Query ===
Identifier: inittest [id = 305e10aa-a446-4f42-a4e9-8d2372250fa8, runId = 2218049f-08a4-40bf-9b46-b7e898321b85]
Current Committed Offsets: {}
Current Available Offsets: {KafkaSource[Subscribe[my-log-topic]]: {"my-log-topic":{"1":9878,"0":10122}}}
Current State: ACTIVE
Thread State: RUNNABLE
Logical Plan:
Project [value#1]
+- StreamingExecutionRelation KafkaSource[Subscribe[my-log-topic]], [key#0, value#1, topic#2, partition#3, offset#4L, timestamp#5, timestampType#6]
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches(StreamExecution.scala:343)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:206)
Caused by: java.lang.NoSuchMethodError: org.apache.spark.sql.SQLContext.internalCreateDataFrame(Lorg/apache/spark/rdd/RDD;Lorg/apache/spark/sql/types/StructType;Z)Lorg/apache/spark/sql/Dataset;
at org.apache.spark.sql.kafka010.KafkaSource.getBatch(KafkaSource.scala:301)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$2$$anonfun$apply$7.apply(StreamExecution.scala:614)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$2$$anonfun$apply$7.apply(StreamExecution.scala:610)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at org.apache.spark.sql.execution.streaming.StreamProgress.foreach(StreamProgress.scala:25)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at org.apache.spark.sql.execution.streaming.StreamProgress.flatMap(StreamProgress.scala:25)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$2.apply(StreamExecution.scala:610)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$2.apply(StreamExecution.scala:610)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:279)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch(StreamExecution.scala:609)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(StreamExecution.scala:306)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$apply$mcZ$sp$1.apply(StreamExecution.scala:294)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$apply$mcZ$sp$1.apply(StreamExecution.scala:294)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:279)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1.apply$mcZ$sp(StreamExecution.scala:294)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches(StreamExecution.scala:290)
... 1 more
Based on the exception, it seems like kafka010.KafkaSource class seems to be missing a method with signature SQLContext.internalCreateDataFrame(rdd, schema)
Upon checking the source code here[https://github.com/apache/spark/blob/master/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSource.scala]
I see the method exists. Has anyone seen such an issue and resolved it? If yes, could you please help understand what the issue was
Finally, the details are the POM are
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>2.3.1</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>2.3.1</version>
<classifier>tests</classifier>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>2.3.1</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming-kafka-0-10 -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
<version>2.3.1</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql-kafka-0-10_2.11</artifactId>
<version>2.3.1</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>2.3.1</version>
</dependency>
I was able to run this as I noticed that the spark version where I'm running spark-submit was 2.2 and the libraries were all (as you can see) are 2.3.1. I changed spark-version and was able to successfully run it.
I am running a Spark streaming application where each batches writes its final output to S3 in parquet format by using SqlContext.
I am able to get this application to run successfully in EMR.
However, after running for a couple of hours, the spark jobs suddenly halts on a FileNotFoundException.
I am not sure what to do next here.
Any pointers in how to debug/fix this issue would be useful.
I use Spark 2.2.1, EMR 5.1.1 and Java 8 for my application.
My streaming application code
public class StreamingApp {
JavaStreamingContext initDAG() {
JavaSparkContext sc = new JavaSparkContext(sparkConf);
// new context
JavaStreamingContext jssc = new JavaStreamingContext(sc, batchInterval);
SQLContext sqlContext = new SQLContext(sc);
...
// Converting to Dataset's Row type
JavaDStream<Row> rowStream = inputStream.map(new ObjectToRowMapperFunction());
// Writing to Disk
rowStream.foreachRDD(new RddToParquetFunction(sqlContext));
return jssc;
}
...
}
public class RddToParquetFunction implements VoidFunction<JavaRDD<Row>> {
private final StructType userStructType;
private final SQLContext sqlContext;
public RddToParquetFunction(SQLContext sqlContext) {
userStructType = ProtobufSparkStructMapper.schemaFor(UserMessage.class);
this.sqlContext = sqlContext;
}
#Override
public void call(JavaRDD<Row> rowRDD) throws Exception {
Dataset<Row> userDataFrame = sqlContext.createDataFrame(rowRDD, userStructType);
userDataFrame.write().mode(SaveMode.Append).parquet("s3://XXXXXXX/XXXXX/");
}
}
appropriate spark driver logs
18/02/15 22:47:57 ERROR ApplicationMaster: User class threw exception: org.apache.spark.SparkException: Job aborted.
org.apache.spark.SparkException: Job aborted.
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply$mcV$sp(FileFormatWriter.scala:213)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:166)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:166)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:166)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:145)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(comm ands.scala:58)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92)
at org.apache.spark.sql.execution.datasources.DataSource.writeInFileFormat(DataSource.scala:435)
at org.apache.spark.sql.execution.datasources.DataSource.write(DataSource.scala:471)
at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:50)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:609)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:233)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:217)
at org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:508)
at app.functions.RddToParquetFunction.call(RddToParquetFunction.java:37)
at app.functions.RddToParquetFunction.call(RddToParquetFunction.java:17)
at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$1.apply(JavaDStreamLike.scala:272)
at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$1.apply(JavaDStreamLike.scala:272)
at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:628)
at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:628)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:51)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:416)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
at scala.util.Try$.apply(Try.scala:192)
at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:257)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:256)
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.FileNotFoundException: File s3://XXXXXXX/XXXXX/output/_temporary/0/task_20180215224653_0267_m_000032 does not exist.
at com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem.listStatus(S3NativeFileSystem.java:996)
at com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem.listStatus(S3NativeFileSystem.java:937)
at com.amazon.ws.emr.hadoop.fs.EmrFileSystem.listStatus(EmrFileSystem.java:337)
at org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter.mergePaths(FileOutputCommitter.java:426)
at org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter.commitJobInternal(FileOutputCommitter.java:362)
at org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter.commitJob(FileOutputCommitter.java:334)
at org.apache.parquet.hadoop.ParquetOutputCommitter.commitJob(ParquetOutputCommitter.java:47)
at org.apache.spark.internal.io.HadoopMapReduceCommitProtocol.commitJob(HadoopMapReduceCommitProtocol.scala:142)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply$mcV$sp(FileFormatWriter.scala:207)
... 57 more
Unless you pay the premium for Amazon's consistent EMR you can't reliably use S3 as a destination for your work.
ASF Hadoop+ Spark has fixed this on Hadoop 3.1+ with the S3A committers. Without that, and on amazon EMR, you need to write to HDFS and then use distcp to copy up the results if needed. If chaining together work, leave on HDFS.
I've set up an AWS EMR with Spark and Zeppelin and with AWS Glue catalog as the metastore for Hive. I used this instruction: https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-spark-glue.html
It seems that the EMR cluster and Zeppeling are working.
When I run the following paragraph:
%sql
show databases
It works and outputs my databases set up in Glue.
However, when I try to query any of the tables, this way:
%sql
select * from `fus-bear-parquet-db-prod`.fus_bear_parquet_prod limit 10
I get the following exception:
java.lang.NullPointerException: Name is null
at java.lang.Enum.valueOf(Enum.java:236)
at org.apache.hadoop.hive.ql.metadata.Table.getTableType(Table.java:401)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$getTableOption$1$$anonfun$apply$11.apply(HiveClientImpl.scala:394)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$getTableOption$1$$anonfun$apply$11.apply(HiveClientImpl.scala:373)
at scala.Option.map(Option.scala:146)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$getTableOption$1.apply(HiveClientImpl.scala:373)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$getTableOption$1.apply(HiveClientImpl.scala:371)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$withHiveState$1.apply(HiveClientImpl.scala:290)
at org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:231)
at org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:230)
at org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:273)
at org.apache.spark.sql.hive.client.HiveClientImpl.getTableOption(HiveClientImpl.scala:371)
at org.apache.spark.sql.hive.client.HiveClient$class.getTable(HiveClient.scala:75)
at org.apache.spark.sql.hive.client.HiveClientImpl.getTable(HiveClientImpl.scala:79)
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$getRawTable$1.apply(HiveExternalCatalog.scala:118)
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$getRawTable$1.apply(HiveExternalCatalog.scala:118)
at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:97)
at org.apache.spark.sql.hive.HiveExternalCatalog.getRawTable(HiveExternalCatalog.scala:117)
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$getTable$1.apply(HiveExternalCatalog.scala:675)
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$getTable$1.apply(HiveExternalCatalog.scala:675)
at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:97)
at org.apache.spark.sql.hive.HiveExternalCatalog.getTable(HiveExternalCatalog.scala:674)
at org.apache.spark.sql.catalyst.catalog.SessionCatalog.lookupRelation(SessionCatalog.scala:667)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveRelations$$lookupTableFromCatalog(Analyzer.scala:646)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.resolveRelation(Analyzer.scala:601)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$8.applyOrElse(Analyzer.scala:631)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$8.applyOrElse(Analyzer.scala:624)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:62)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:62)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:61)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:59)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:624)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:570)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82)
at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
at scala.collection.immutable.List.foldLeft(List.scala:84)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:69)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:67)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:50)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:67)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:632)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:691)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.zeppelin.spark.SparkSqlInterpreter.interpret(SparkSqlInterpreter.java:116)
at org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:97)
at org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:498)
at org.apache.zeppelin.scheduler.Job.run(Job.java:175)
at org.apache.zeppelin.scheduler.FIFOScheduler$1.run(FIFOScheduler.java:139)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
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)
Appreciate any help.
I will rephrase #Andrey Cheptsov's comment as an answer as this is solving the problem and this way will be more prominent for others facing the same issue. Thanks Andrey!
The problem is caused by the TableType (EXTERNAL_TABLE, VIRTUAL_VIEW, etc.) property not being set for the table. It's set automatically when creating tables from Athena, but can get forgotten when using Cloudformation or Terraform.
To solve the problem make sure you add the TableType property to you table and re-try the query.
If you are using the CDK I could fix that by using the table_type
aws_glue.CfnTable.TableInputProperty(description="My Description",name="my_table_name",table_type="EXTERNAL_TABLE",...,)
Using only CloudFormation:
"GlueTable": {
"Type": "AWS::Glue::Table",
"Properties": {
"CatalogId": {
"Ref": "AWS::AccountId"
},
"DatabaseName": "my_data_base_name",
"TableInput": {
"Description": "My Description",
"Name": "my_table_name",
...
"TableType": "EXTERNAL_TABLE"
}
}
}
From Spark Java App submitted to the Spark Cluster hosted on my machine, I am trying to connect to a Cassandra DB hosted on my machine # 127.0.0.1:9042 and my Spring Boot application is failing to start.
Approach 1 -
** Based on the Spark-Cassandra-Connector link I had included the below in the POM file -**
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>2.0.0</version>
</dependency>
<dependency>
<groupId>com.datastax.spark</groupId>
<artifactId>spark-cassandra-connector_2.11</artifactId>
<version>2.0.0-M3</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.11</artifactId>
<version>2.0.0</version>
</dependency>
Approach 1 - NoSuchMethodError - Log File:
16/09/08 15:12:50 ERROR SpringApplication: Application startup failed
java.lang.NoSuchMethodError: com.datastax.driver.core.KeyspaceMetadata.getMaterializedViews()Ljava/util/Collection;
at com.datastax.spark.connector.cql.Schema$.com$datastax$spark$connector$cql$Schema$$fetchTables$1(Schema.scala:281)
at com.datastax.spark.connector.cql.Schema$$anonfun$com$datastax$spark$connector$cql$Schema$$fetchKeyspaces$1$2.apply(Schema.scala:305)
at com.datastax.spark.connector.cql.Schema$$anonfun$com$datastax$spark$connector$cql$Schema$$fetchKeyspaces$1$2.apply(Schema.scala:304)
at scala.collection.TraversableLike$WithFilter$$anonfun$map$2.apply(TraversableLike.scala:683)
at scala.collection.immutable.HashSet$HashSet1.foreach(HashSet.scala:316)
at scala.collection.immutable.HashSet$HashTrieSet.foreach(HashSet.scala:972)
at scala.collection.immutable.HashSet$HashTrieSet.foreach(HashSet.scala:972)
at scala.collection.TraversableLike$WithFilter.map(TraversableLike.scala:682)
at com.datastax.spark.connector.cql.Schema$.com$datastax$spark$connector$cql$Schema$$fetchKeyspaces$1(Schema.scala:304)
at com.datastax.spark.connector.cql.Schema$$anonfun$fromCassandra$1.apply(Schema.scala:325)
at com.datastax.spark.connector.cql.Schema$$anonfun$fromCassandra$1.apply(Schema.scala:322)
at com.datastax.spark.connector.cql.CassandraConnector$$anonfun$withClusterDo$1.apply(CassandraConnector.scala:122)
at com.datastax.spark.connector.cql.CassandraConnector$$anonfun$withClusterDo$1.apply(CassandraConnector.scala:121)
at com.datastax.spark.connector.cql.CassandraConnector$$anonfun$withSessionDo$1.apply(CassandraConnector.scala:111)
at com.datastax.spark.connector.cql.CassandraConnector$$anonfun$withSessionDo$1.apply(CassandraConnector.scala:110)
at com.datastax.spark.connector.cql.CassandraConnector.closeResourceAfterUse(CassandraConnector.scala:140)
at com.datastax.spark.connector.cql.CassandraConnector.withSessionDo(CassandraConnector.scala:110)
at com.datastax.spark.connector.cql.CassandraConnector.withClusterDo(CassandraConnector.scala:121)
at com.datastax.spark.connector.cql.Schema$.fromCassandra(Schema.scala:322)
at com.datastax.spark.connector.cql.Schema$.tableFromCassandra(Schema.scala:342)
at com.datastax.spark.connector.rdd.CassandraTableRowReaderProvider$class.tableDef(CassandraTableRowReaderProvider.scala:50)
at com.datastax.spark.connector.rdd.CassandraTableScanRDD.tableDef$lzycompute(CassandraTableScanRDD.scala:60)
at com.datastax.spark.connector.rdd.CassandraTableScanRDD.tableDef(CassandraTableScanRDD.scala:60)
at com.datastax.spark.connector.rdd.CassandraTableRowReaderProvider$class.verify(CassandraTableRowReaderProvider.scala:137)
at com.datastax.spark.connector.rdd.CassandraTableScanRDD.verify(CassandraTableScanRDD.scala:60)
at com.datastax.spark.connector.rdd.CassandraTableScanRDD.getPartitions(CassandraTableScanRDD.scala:232)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:248)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:246)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:246)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1911)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1.apply(RDD.scala:875)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1.apply(RDD.scala:873)
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:358)
at org.apache.spark.rdd.RDD.foreach(RDD.scala:873)
at org.apache.spark.api.java.JavaRDDLike$class.foreach(JavaRDDLike.scala:350)
at org.apache.spark.api.java.AbstractJavaRDDLike.foreach(JavaRDDLike.scala:45)
at com.initech.myapp.cassandra.service.CassandraDataService.getMatches(CassandraDataService.java:45)
at com.initech.myapp.processunit.MySparkApp.receive(MySparkApp.java:120)
at com.initech.myapp.processunit.MySparkApp.process(MySparkApp.java:61)
at com.initech.myapp.processunit.MySparkApp.run(MySparkApp.java:144)
at org.springframework.boot.SpringApplication.callRunner(SpringApplication.java:789)
at org.springframework.boot.SpringApplication.callRunners(SpringApplication.java:779)
at org.springframework.boot.SpringApplication.afterRefresh(SpringApplication.java:769)
at org.springframework.boot.SpringApplication.run(SpringApplication.java:314)
at org.springframework.boot.SpringApplication.run(SpringApplication.java:1185)
at org.springframework.boot.SpringApplication.run(SpringApplication.java:1174)
at com.initech.myapp.MySparkAppBootApp.main(MyAppProcessingUnitsApplication.java:20)
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.springframework.boot.loader.MainMethodRunner.run(MainMethodRunner.java:48)
at org.springframework.boot.loader.Launcher.launch(Launcher.java:87)
at org.springframework.boot.loader.Launcher.launch(Launcher.java:50)
at org.springframework.boot.loader.JarLauncher.main(JarLauncher.java:58)
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.worker.DriverWrapper$.main(DriverWrapper.scala:58)
at org.apache.spark.deploy.worker.DriverWrapper.main(DriverWrapper.scala)
16/09/08 15:12:50 INFO AnnotationConfigApplicationContext: Closing org.springframework.context.annotation.AnnotationConfigApplicationContext#3381b4fc: startup date [Thu Sep 08 15:12:40 PDT 2016]; root of context hierarchy
Approach 2 -
** Since what I am developing is a Java Spark app, I thought of using the Spark-Cassandra-Connector-Java and had included the below in the POM file -**
<dependency>
<groupId>com.datastax.spark</groupId>
<artifactId>spark-cassandra-connector_2.11</artifactId>
<version>2.0.0-M3</version>
</dependency>
<dependency>
<groupId>com.datastax.spark</groupId>
<artifactId>spark-cassandra-connector-java_2.11</artifactId>
<version>1.2.6</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>2.0.0</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.11</artifactId>
<version>2.0.0</version>
</dependency>
and ended up with this
Approach 2 - SelectableColumnRef NoClassDefFoundError - Log File:
16/09/08 16:28:07 ERROR SpringApplication: Application startup failed
java.lang.NoClassDefFoundError: com/datastax/spark/connector/SelectableColumnRef
at com.initech.myApp.cassandra.service.CassandraDataService.getMatches(CassandraDataService.java:41)
** My Spark Main method calls the process() method below**
public boolean process() throws InterruptedException {
logger.debug("In the process() method");
SparkConf sparkConf = new SparkConf().setAppName("My Process Unit");
sparkConf.set("spark.cassandra.connection.host", "127.0.0.1");
sparkConf.set("spark.cassandra.connection.port","9042");
logger.debug("SparkConf = " + sparkConf);
JavaStreamingContext javaStreamingContext = new JavaStreamingContext(sparkConf, new Duration(1000));
logger.debug("JavaStreamingContext = " + javaStreamingContext);
JavaSparkContext javaSparkContext = javaStreamingContext.sparkContext();
logger.debug("Java Spark context = " + javaSparkContext);
JavaRDD<MyData> myDataJavaRDD = receive(javaSparkContext);
myDataJavaRDD.foreach(myData -> {
logger.debug("myData = " + myData);
});
javaStreamingContext.start();
javaStreamingContext.awaitTermination();
return true; }
** which calls the receive() below **
private JavaRDD<MyData> receive(JavaSparkContext javaSparkContext) {
logger.debug("receive method called...");
List<String> myAppConfigsStrings = myAppConfiguration.get();
logger.debug("Received ..." + myAppConfigsStrings);
for(String myAppConfigStr : myAppConfigsStrings)
{
ObjectMapper mapper = new ObjectMapper();
MyAppConfig myAppConfig;
try {
logger.debug("Parsing the myAppConfigStr..." + myAppConfigStr);
myAppConfig = mapper.readValue(myAppConfigStr, MyAppConfig.class);
logger.debug("Parse Complete...");
// Check for matching data in Cassandra
JavaRDD<MyData> cassandraRowsRDD = cassandraDataService.getMatches(myAppConfig, javaSparkContext);
cassandraRowsRDD.foreach(myData -> {
logger.debug("myData = " + myData);
});
return cassandraRowsRDD;
} catch (IOException e) {
e.printStackTrace();
}
}
return null;
}
** Eventually calling the Cassandra Data Service getMatches() below **
#Service
public class CassandraDataService implements Serializable {
private static final Log logger = LogFactory.getLog(CassandraDataService.class);
public JavaRDD<MyData> getMatches(MyAppConfig myAppConfig, JavaSparkContext javaSparkContext) {
logger.debug("Creating the MyDataID...");
MyDataID myDataID = new MyDataID();
myDataID.set...(myAppConfig.get...);
myDataID.set...(myAppConfig.get...);
myDataID.set...(myAppConfig.get...);
logger.debug("MyDataID = " + myDataID);
JavaRDD<MyData> cassandraRowsRDD = javaFunctions(javaSparkContext).cassandraTable("myKeySpace", "myData", mapRowTo(MyData.class));
cassandraRowsRDD.foreach(myData -> {
logger.debug("====== Cassandra Data Service ========");
logger.debug("myData = " + myData);
logger.debug("====== Cassandra Data Service ========");
});
return cassandraRowsRDD;
}
}
Has anyone experienced similar error or could provide me in some direction?
I have tried googling and reading through several items - but none to rescue. Thanks.
Update 9/9/2016 2:15 PM PST
I tried the approach above. Here is what I have done -
Spark cluster running with 1 worker thread
Submitted my Spark App using the Spring Boot Uber Jar using spark-submit command below -
./bin/spark-submit --class org.springframework.boot.loader.JarLauncher --master spark://localhost:6066 --deploy-mode cluster /Users/apple/Repos/Initech/Officespace/target/my-spring-spark-boot-streaming-app-0.1-SNAPSHOT.jar
The Spark Driver program started successfully and initiated my Spark App and was set to "WAITING" state as there was only one worker running that was allocated to the driver program
I then started another worker thread and then the App worker thread had failed because of "java.lang.ClassNotFoundException: com.datastax.spark.connector.rdd.partitioner.CassandraPartition". Below is the stack trace.
If it is useful in anyway - he is the stack I am using
1. cqlsh 5.0.1 | Cassandra 2.2.7 | CQL spec 3.3.1
2. Spark - 2.0.0
3. Spring Boot - 1.4.0.RELEASE
4. Jar's listed in the Approach 1 above
Exception Stack Tracke
16/09/09 14:13:24 ERROR SpringApplication: Application startup failed
java.lang.IllegalStateException: Failed to execute ApplicationRunner
at org.springframework.boot.SpringApplication.callRunner(SpringApplication.java:792)
at org.springframework.boot.SpringApplication.callRunners(SpringApplication.java:779)
at org.springframework.boot.SpringApplication.afterRefresh(SpringApplication.java:769)
at org.springframework.boot.SpringApplication.run(SpringApplication.java:314)
at org.springframework.boot.SpringApplication.run(SpringApplication.java:1185)
at org.springframework.boot.SpringApplication.run(SpringApplication.java:1174)
at com.initech.officespace.MySpringBootSparkApp.main(MySpringBootSparkApp.java:23)
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.springframework.boot.loader.MainMethodRunner.run(MainMethodRunner.java:48)
at org.springframework.boot.loader.Launcher.launch(Launcher.java:87)
at org.springframework.boot.loader.Launcher.launch(Launcher.java:50)
at org.springframework.boot.loader.JarLauncher.main(JarLauncher.java:58)
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.worker.DriverWrapper$.main(DriverWrapper.scala:58)
at org.apache.spark.deploy.worker.DriverWrapper.main(DriverWrapper.scala)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 6, 192.168.0.30): java.lang.ClassNotFoundException: com.datastax.spark.connector.rdd.partitioner.CassandraPartition
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:348)
at org.apache.spark.serializer.JavaDeserializationStream$$anon$1.resolveClass(JavaSerializer.scala:67)
at java.io.ObjectInputStream.readNonProxyDesc(ObjectInputStream.java:1620)
at java.io.ObjectInputStream.readClassDesc(ObjectInputStream.java:1521)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1781)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1353)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2018)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1942)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1808)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1353)
at java.io.ObjectInputStream.readObject(ObjectInputStream.java:373)
at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:75)
at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:114)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:253)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1450)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1438)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1437)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1437)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1659)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1618)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1607)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1871)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1884)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1897)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1911)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1.apply(RDD.scala:875)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1.apply(RDD.scala:873)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:358)
at org.apache.spark.rdd.RDD.foreach(RDD.scala:873)
at org.apache.spark.api.java.JavaRDDLike$class.foreach(JavaRDDLike.scala:350)
at org.apache.spark.api.java.AbstractJavaRDDLike.foreach(JavaRDDLike.scala:45)
at com.initech.officespace.cassandra.service.CassandraDataService.getMatches(CassandraDataService.java:43)
at com.initech.officespace.processunit.MyApp.receive(MyApp.java:120)
at com.initech.officespace.processunit.MyApp.process(MyApp.java:61)
at com.initech.officespace.processunit.MyApp.run(MyApp.java:144)
at org.springframework.boot.SpringApplication.callRunner(SpringApplication.java:789)
... 20 more
Caused by: java.lang.ClassNotFoundException: com.datastax.spark.connector.rdd.partitioner.CassandraPartition
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:348)
at org.apache.spark.serializer.JavaDeserializationStream$$anon$1.resolveClass(JavaSerializer.scala:67)
at java.io.ObjectInputStream.readNonProxyDesc(ObjectInputStream.java:1620)
at java.io.ObjectInputStream.readClassDesc(ObjectInputStream.java:1521)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1781)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1353)
at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2018)
at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1942)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1808)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1353)
at java.io.ObjectInputStream.readObject(ObjectInputStream.java:373)
at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:75)
at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:114)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:253)
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)
16/09/09 14:13:24 INFO AnnotationConfigApplicationContext: Closing org.springframework.context.annotation.AnnotationConfigApplicationContext#3381b4fc: startup date [Fri Sep 09 14:10:40 PDT 2016]; root of context hierarchy
Update 2 on 9/9/2016 3:20 PM PST
Issue is now resolved based on the answer provided by RussS # Issues with datastax spark-cassandra connector
After updating my spark-submit to the below, I am seeing that the worker is able to pickup the connecter and start working on the RDDs :)
./bin/spark-submit --class org.springframework.boot.loader.JarLauncher --master spark://localhost:6066 --deploy-mode cluster --packages com.datastax.spark:spark-cassandra-connector_2.11:2.0.0-M3 /Users/apple/Repos/Initech/Officespace/target/my-spring-spark-boot-streaming-app-0.1-SNAPSHOT.jar
Solution could be different.
I had this exception when tried to run spark with cassandra from PC(driver) on java.
You can add jar with spark-cassandra-connector to SparkContext in my case it was like in example below:
JavaSparkContext sc = new JavaSparkContext(conf);
sc.addJar("./build/libs/spark-cassandra-connector_2.11-2.4.2.jar"); // location of driver could be different.
com.datastax.driver.core.KeyspaceMetadata.getMaterializedViews is present starting version 3.0 of the driver.
Try adding this dependency to version 1:
<dependency>
<groupId>com.datastax.cassandra</groupId>
<artifactId>cassandra-driver-core</artifactId>
<version>3.1.0</version>
</dependency>