[SPARK-24593] can not find hive table after spark streaming started

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Invalid
• Affects Version/s: 2.3.0, 2.3.1
• Component/s: DStreams, SQL

Environment

// demo code
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

package org.apache.spark.examples.streaming;

import java.util.Arrays;
import java.util.regex.Pattern;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.api.java.StorageLevels;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;

/**
* Use DataFrames and SQL to count words in UTF8 encoded, '\n' delimited text received from the
* network every second.
*
* Usage: JavaSqlNetworkWordCount <hostname> <port>
* <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data.
*
* To run this on your local machine, you need to first run a Netcat server
* `$ nc -lk 9999`
* and then run the example
* `$ bin/run-example org.apache.spark.examples.streaming.JavaSqlNetworkWordCount localhost 9999`
*/
public final class JavaSqlNetworkWordCount {
private static final Pattern SPACE = Pattern.compile(" ");

public static void main(String[] args) throws Exception {
if (args.length < 2) {
System.err.println("Usage: JavaNetworkWordCount <hostname> <port>");
System.exit(1);
}

StreamingExamples.setStreamingLogLevels();

// Create the context with a 1 second batch size
SparkConf sparkConf = new SparkConf().setAppName("JavaSqlNetworkWordCount");
JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(1));

// Create a JavaReceiverInputDStream on target ip:port and count the
// words in input stream of \n delimited text (eg. generated by 'nc')
// Note that no duplication in storage level only for running locally.
// Replication necessary in distributed scenario for fault tolerance.
JavaReceiverInputDStream<String> lines = ssc.socketTextStream(
args[0], Integer.parseInt(args[1]), StorageLevels.MEMORY_AND_DISK_SER);
JavaDStream<String> words = lines.flatMap(x -> Arrays.asList(SPACE.split(x)).iterator());

// Convert RDDs of the words DStream to DataFrame and run SQL query
words.foreachRDD((rdd, time) -> {
SparkSession spark = JavaSparkSessionSingleton.getInstance(rdd.context().getConf());

// Convert JavaRDD[String] to JavaRDD[bean class] to DataFrame
JavaRDD<JavaRecord> rowRDD = rdd.map(word -> {
JavaRecord record = new JavaRecord();
record.setWord(word);
return record;
});
Dataset<Row> wordsDataFrame = spark.createDataFrame(rowRDD, JavaRecord.class);

// Creates a temporary view using the DataFrame
wordsDataFrame.createOrReplaceTempView("words");

// Do word count on table using SQL and print it
Dataset<Row> wordCountsDataFrame =
spark.sql("select word, count(*) as total from words group by word");
System.out.println("========= " + time + "=========");
wordCountsDataFrame.show();

// access hive talbe failed
spark.sql("select * from test_fch");
});

ssc.start();
ssc.awaitTermination();
}
}

/** Lazily instantiated singleton instance of SparkSession */
class JavaSparkSessionSingleton {
private static transient SparkSession instance = null;
public static SparkSession getInstance(SparkConf sparkConf) {
if (instance == null) {
instance = SparkSession
.builder()
.config(sparkConf)
.enableHiveSupport()
.getOrCreate();
}
return instance;
}
}

• Assignee: Unassigned
• Reporter: lhq
• Created: 19/Jun/18 10:16
• Updated: 12/Dec/22 18:10
• Resolved: 20/Jun/18 02:09

Description

org.apache.spark.sql.AnalysisException: Table or view not found: test_fch; line 1 pos 14
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:47)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveRelations$$lookupTableFromCatalog(Analyzer.scala:665)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.resolveRelation(Analyzer.scala:617)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$8.applyOrElse(Analyzer.scala:647)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$8.applyOrElse(Analyzer.scala:640)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:288)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:640)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:586)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:87)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:84)
at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
at scala.collection.immutable.List.foldLeft(List.scala:84)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:84)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:76)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:76)
at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:124)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:118)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:103)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:57)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:55)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:47)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:74)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:641)
at org.apache.spark.examples.streaming.JavaSqlNetworkWordCount.lambda$main$3dd8454f$1(JavaSqlNetworkWordCount.java:89)
at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$2.apply(JavaDStreamLike.scala:280)
at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$2.apply(JavaDStreamLike.scala:280)
at org.apache.spark.streaming.dstream.ForEachDStream$$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$$anonfun$run$1.apply(JobScheduler.scala:257)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:256)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.sql.catalyst.analysis.NoSuchTableException: Table or view 'test_fch' not found in database 'default';
at org.apache.spark.sql.catalyst.catalog.ExternalCatalog.requireTableExists(ExternalCatalog.scala:46)
at org.apache.spark.sql.catalyst.catalog.InMemoryCatalog.getTable(InMemoryCatalog.scala:326)
at org.apache.spark.sql.catalyst.catalog.SessionCatalog.lookupRelation(SessionCatalog.scala:669)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveRelations$$lookupTableFromCatalog(Analyzer.scala:662)
... 51 more

Issue Links

• is duplicated by: SPARK-24592 can not find hive table after spark streaming start