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- Check if a block is corrupt
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QUESTION
I followed the instructions at Structured Streaming + Kafka and built a program that receives data streams sent from kafka as input, when I receive the data stream I want to pass it to SparkSession variable to do some query work with Spark SQL, so I extend the ForeachWriter class again as follows:
package stream;
import java.io.FileNotFoundException;
import java.io.PrintWriter;
import org.apache.spark.sql.ForeachWriter;
import org.apache.spark.sql.SparkSession;
import org.json.simple.JSONObject;
import org.json.simple.parser.JSONParser;
import org.json.simple.parser.ParseException;
import dataservices.OrderDataServices;
import models.SuccessEvent;
public class MapEventWriter extends ForeachWriter{
private SparkSession spark;
public MapEventWriter(SparkSession spark) {
this.spark = spark;
}
private static final long serialVersionUID = 1L;
@Override
public void close(Throwable errorOrNull) {
// TODO Auto-generated method stub
}
@Override
public boolean open(long partitionId, long epochId) {
// TODO Auto-generated method stub
return true;
}
@Override
public void process(String input) {
OrderDataServices services = new OrderDataServices(this.spark);
}
}
however in the process function, if I use spark variable, the program gives an error, the program passes in my spark as follows:
package demo;
import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.FileNotFoundException;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.concurrent.TimeoutException;
import org.apache.hadoop.fs.Path;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.streaming.StreamingQuery;
import org.apache.spark.sql.streaming.StreamingQueryException;
import org.json.simple.parser.ParseException;
import dataservices.OrderDataServices;
import models.MapperEvent;
import models.OrderEvent;
import models.SuccessEvent;
import stream.MapEventWriter;
import stream.MapEventWriter1;
public class Demo {
public static void main(String[] args) throws TimeoutException, StreamingQueryException, ParseException, IOException {
try (SparkSession spark = SparkSession.builder().appName("Read kafka").getOrCreate()) {
Dataset data = spark
.readStream()
.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("subscribe", "tiki-1")
.load()
.selectExpr("CAST(value AS STRING)")
.as(Encoders.STRING());
MapEventWriter eventWriter = new MapEventWriter(spark);
StreamingQuery query = data
.writeStream()
.foreach(eventWriter)
.start();
query.awaitTermination();
}
}
}
The error is NullPointerException at the spark call location, that is, no spark variable is initialized. Hope anyone can help me, I really appreciate it.
Caused by: java.lang.NullPointerException
at org.apache.spark.sql.SparkSession.sessionState$lzycompute(SparkSession.scala:151)
at org.apache.spark.sql.SparkSession.sessionState(SparkSession.scala:149)
at org.apache.spark.sql.DataFrameReader.(DataFrameReader.scala:998)
at org.apache.spark.sql.SparkSession.read(SparkSession.scala:655)
at dataservices.OrderDataServices.(OrderDataServices.java:18)
at stream.MapEventWriter.process(MapEventWriter.java:38)
at stream.MapEventWriter.process(MapEventWriter.java:15)
ANSWER
Answered 2021-Jun-15 at 04:42do some query work with Spark SQL
You wouldn't use a ForEachWriter for that
.selectExpr("CAST(value AS STRING)")
.as(Encoders.STRING()); // or parse your JSON here using a schema
data.select(...) // or move this to a method / class that takes the Dataset as a parameter
// await termination
QUESTION
I am new to Spark and am trying to run on a hadoop cluster a simple spark jar file built through maven in intellij. But I am getting classnotfoundexception in all the ways I tried to submit the application through spark-submit.
My pom.xml:
4.0.0
org.example
SparkTrans
1.0-SNAPSHOT
org.apache.spark
spark-core_2.11
2.4.3
org.apache.spark
spark-sql_2.11
2.4.3
org.apache.spark
spark-hive_2.11
2.4.3
compile
org.apache.logging.log4j
log4j-slf4j-impl
2.8
test
org.apache.logging.log4j
log4j-api
2.8
com.typesafe
config
1.3.4
org.scalatest
scalatest_2.11
3.1.1
test
org.apache.maven.plugins
maven-shade-plugin
3.2.0
shade-libs
package
shade
*:*
META-INF/*.SF
META-INF/*.DSA
META-INF/*.RSA
resources/*
fat
true
org.apache.http.client
shaded.org.apache.http.client
My main scala object (SparkTrans.scala):
import common.InputConfig
import org.apache.spark.sql.{DataFrame,SparkSession}
importorg.slf4j.LoggerFactory
object SparkTrans{
private val logger=LoggerFactory.getLogger(getClass.getName)
def main(args:Array[String]):Unit={
try{
logger.info("main method started")
logger.warn("This is a warning")
val arg_length=args.length
if(arg_length==0){
logger.warn("No Argument passed")
System.exit(1)
}
val inputConfig:InputConfig=InputConfig(env=args(0),targetDB=args(1))
println("The first argument passed is" + inputConfig.env)
println("The second argument passed is" + inputConfig.targetDB)
val spark=SparkSession
.builder()
.appName("SparkPOCinside")
.config("spark.master","yarn")
.enableHiveSupport()
.getOrCreate()
println("Created Spark Session")
val sampleSeq=Seq((1,"Spark"),(2,"BigData"))
val df1=spark.createDataFrame(sampleSeq).toDF("courseid","coursename")
df1.show()
logger.warn("sql_test_a method started")
val courseDF=spark.sql("select * from MYINSTANCE.sql_test_a")
logger.warn("sql_test_a method ended")
courseDF.show()
}
catch{
case e:Exception=>
logger.error("An error has occurred in the main method" + e.printStackTrace())
}
}
}
I tried the below commands to spark-submit, but all of them give classnotfoundexception. I tried to switch the arguments around where I mention the --class right after --deploy-mode but in vain:
spark-submit --master yarn --deploy-mode cluster --queue ABCD --conf spark.yarn.security.tokens.hive.enabled=false --files hdfs://nameservice1/user/XMLs/hive-site.xml --keytab hdfs://nameservice1/user/MYINSTANCE/landing/workflow/wf_data/lib/MYKEY.keytab --num-executors 1 --executor-cores 1 --executor-memory 2g --conf spark.yarn.executor.memoryOverhead=3072 --class org.example.SparkTrans hdfs://nameservice1/user/MYINSTANCE/landing/workflow/wf_data/SparkTrans-1.0-SNAPSHOT-fat.jar dev somedb
spark-submit --master yarn --deploy-mode cluster --queue ABCD --conf spark.yarn.security.tokens.hive.enabled=false --files hdfs://nameservice1/user/XMLs/hive-site.xml --keytab hdfs://nameservice1/user/MYINSTANCE/landing/workflow/wf_data/lib/MYKEY.keytab --num-executors 1 --executor-cores 1 --executor-memory 2g --conf spark.yarn.executor.memoryOverhead=3072 --class org.example.SparkTrans --name org.example.SparkTrans hdfs://nameservice1/user/MYINSTANCE/landing/workflow/wf_data/SparkTrans-1.0-SNAPSHOT-fat.jar dev somedb
spark-submit --master yarn --deploy-mode cluster --queue ABCD --conf spark.yarn.security.tokens.hive.enabled=false --files hdfs://nameservice1/user/XMLs/hive-site.xml --keytab hdfs://nameservice1/user/MYINSTANCE/landing/workflow/wf_data/lib/MYKEY.keytab --num-executors 1 --executor-cores 1 --executor-memory 2g --conf spark.yarn.executor.memoryOverhead=3072 --class SparkTrans hdfs://nameservice1/user/MYINSTANCE/landing/workflow/wf_data/SparkTrans-1.0-SNAPSHOT-fat.jar dev somedb
Exact error I am getting:
btrace WARNING: No output stream. DataCommand output is ignored.
[main] INFO ResourceCollector - Unravel Sensor 4.6.1.8rc0013/2.0.3 initializing.
21/06/11 10:09:27 INFO yarn.ApplicationMaster: Registered signal handlers for [TERM, HUP, INT]
21/06/11 10:09:28 INFO yarn.ApplicationMaster: ApplicationAttemptId: appattempt_1614625006458_6646161_000001
21/06/11 10:09:30 INFO spark.SecurityManager: Changing view acls to: MYKEY
21/06/11 10:09:30 INFO spark.SecurityManager: Changing modify acls to: MYKEY
21/06/11 10:09:30 INFO spark.SecurityManager: Changing view acls groups to:
21/06/11 10:09:30 INFO spark.SecurityManager: Changing modify acls groups to:
21/06/11 10:09:30 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(MYKEY); groups with view permissions: Set(); users with modify permissions: Set(MYKEY); groups with modify permissions: Set()
21/06/11 10:09:30 INFO yarn.ApplicationMaster: Starting the user application in a separate Thread
21/06/11 10:09:30 ERROR yarn.ApplicationMaster: Uncaught exception:
java.lang.ClassNotFoundException: SparkTrans
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at org.apache.spark.deploy.yarn.ApplicationMaster.startUserApplication(ApplicationMaster.scala:561)
at org.apache.spark.deploy.yarn.ApplicationMaster.runDriver(ApplicationMaster.scala:347)
at org.apache.spark.deploy.yarn.ApplicationMaster.run(ApplicationMaster.scala:197)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anonfun$main$1.apply$mcV$sp(ApplicationMaster.scala:695)
at org.apache.spark.deploy.SparkHadoopUtil$$anon$1.run(SparkHadoopUtil.scala:69)
at org.apache.spark.deploy.SparkHadoopUtil$$anon$1.run(SparkHadoopUtil.scala:68)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1924)
at org.apache.spark.deploy.SparkHadoopUtil.runAsSparkUser(SparkHadoopUtil.scala:68)
at org.apache.spark.deploy.yarn.ApplicationMaster$.main(ApplicationMaster.scala:693)
at org.apache.spark.deploy.yarn.ApplicationMaster.main(ApplicationMaster.scala)
21/06/11 10:09:30 INFO yarn.ApplicationMaster: Final app status: FAILED, exitCode: 10, (reason: Uncaught exception: java.lang.ClassNotFoundException: SparkTrans)
21/06/11 10:09:30 INFO util.ShutdownHookManager: Shutdown hook called
Can any of you let me know what I am doing wrong? I have checked and see the hive-site.xml and my jar are in the correct locations in hdfs as mentioned in my commands.
ANSWER
Answered 2021-Jun-14 at 09:36You need to add scala-compiler configuration to your pom.xml. The problem is without that there is nothing to compile your SparkTrans.scala file into java classes.
Add:
src/main/scala
src/test/scala
net.alchim31.maven
scala-maven-plugin
4.5.2
compile
testCompile
to your pom.xml and ensure your scala file is in src/main/scala
Then it should be compiled and added to your jar. Here's the documentation for the scala plugin.
You can check what's in your jar with jar tf jar-file, see guide here.
QUESTION
I have setup a small size Hadoop Yarn cluster where Apache Spark is running. I have some data (JSON, CSV) that I upload to Spark (data-frame) for some analysis. Later, I have to index all data-frame data into Apache SOlr. I am using Spark 3 and Solr 8.8 version.
In my search, I have found a solution here but it is for different version of Spark. Hence, I have decided to ask someone for this.
Is there any builtin option for this task. I am open to use SolrJ and pySpark (not scal shell).
ANSWER
Answered 2021-Jun-14 at 07:42I found a solution myself. Till now Lucidword spark-solr module does not support these versions of Spark (3.0.2) and Solr (8.8). I have first installed PySolr module and then use following example code to finish my job:
import pysolr
import json
def solrIndexer(row):
solr = pysolr.Solr('http://localhost:8983/solr/spark-test')
obj = json.loads(row)
solr.add(obj)
#load data to dataframe from HDFS
csvDF = spark.read.load("hdfs://hms/data/*.csv", format="csv", sep=",", inferSchema="true", header="true")
csvDF.toJSON().map(solrIndexer).count()
If there is some better option or improvement in above code, you are welcome to answer.
QUESTION
I have an Apache Kylin container running in docker. I was getting a Java heap space error in map reduce phase so I tried updating some parameters in Hadoop mapred-default.xml file. After making the changes, I restarted the container but, when I go to Yarn ResourceManager Web UI and then to Configuration:
An xml file is opened, looking like this:
However my new values for the properties that I set inside the mapred-default.xml are not here, it is showing the old values for those properties... Does anyone have any idea why that is happening and what I should do to make it register the new values? I tried restarting the container, but it didn't help...
ANSWER
Answered 2021-Jun-12 at 07:08To override a default value for a property, specify the new value within the tags, inside mapred-site.xml not mapred-default.xml, using the following format:
mapreduce.map.memory.mb
1024
Be sure restart yarn after reconfigure by stop-yarn.sh and start-yarn.sh.
QUESTION
I had used the below command in GCP Shell terminal to create a project wordcount
mvn archetype:generate
-DarchetypeGroupId=org.apache.maen.archetypes
-DgroupId=com.wordcount
-DartifactId=wordcount
and then added a Map java file in the below path /wordcount/src/main/java/com/wordcount.
When i'm using the below import statements it's throwing an error.
import org.apache.hadoop.io.Intwritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
I'm unable to find the path in GCP shell am i missing the apache/hadoop classes which should be added so that i can resolve this issue.
ANSWER
Answered 2021-Jun-10 at 21:48I'd suggest finding an archetype for creating MapReduce applications, otherwise, you need to add hadoop-client as a dependency in your pom.xml
QUESTION
I have 3 remote computers (servers):
- computer 1 has internal IP: 10.1.7.245
- computer 2 has internal IP: 10.1.7.246
- computer 3 has internal IP: 10.1.7.247
(The 3 computers above are in the same network, these 3 computers are all using Ubuntu 18.04.5 LTS Operating System)
(My personal laptop is in another different network, my laptop also uses Ubuntu 18.04.5 LTS Operating System)
I use my personal laptop to connect to the 3 remote computers using SSH protocol and using user root : (Below ABC is a name)
- computer 1:
ssh root@ABC.University.edu.vn -p 12001 - computer 2:
ssh root@ABC.University.edu.vn -p 12002 - computer 3:
ssh root@ABC.University.edu.vn -p 12003
I have successfully set up a Hadoop Cluster which contains 3 above computer:
- computer 1 is the Hadoop Master
- computer 2 is the Hadoop Slave 1
- computer 3 is the Hadoop Slave 2
======================================================
I starts HDFS of the Hadoop Cluster by using the below command on Computer 1: start-dfs.sh
Everything is successful:
- computer 1 (the Master) is running the NameNode
- computer 2 (the Slave 1) is running the DataNode
- computer 3 (the Slave 2) is running the DataNode
I know that the the Web Interface for the NameNode is running on Computer 1, on IP 0.0.0.0 and on port 9870 . Therefore, if I open the web browser on computer 1 (or on computer 2, or on computer 3), I will enter the 10.1.7.245:9870 on the URL bar (address bar) of the web browser to see the Web Interface of the NameNode.
======================================================
Now, I am using the web browser of my personal laptop.
How could I access to the Web Interface of the NameNode ?
ANSWER
Answered 2021-Jun-08 at 17:56Unless you expose port 9870, your personal laptop on another network will not be able to access the web interface.
You can check to see if it is exposed by trying :9870 to see if it is exposed. IP-address here has to be the global IP-address, not the local (10.* ) address.
To get the NameNode's IP address, ssh into the NameNode server, and type ifconfig (sudo apt install ifconfig if not already installed - I'm assuming Ubuntu/Linux here). ifconfig should give you a global IP address (not the 255.* - that is a mask).
QUESTION
I've always heard that Spark is 100x faster than classic Map Reduce frameworks like Hadoop. But recently I'm reading that this is only true if RDDs are cached, which I thought was always done but instead requires the explicit cache () method.
I would like to understand how all produced RDDs are stored throughout the work. Suppose we have this workflow:
- I read a file -> I get the RDD_ONE
- I use the map on the RDD_ONE -> I get the RDD_TWO
- I use any other transformation on the RDD_TWO
QUESTIONS:
if I don't use cache () or persist () is every RDD stored in memory, in cache or on disk (local file system or HDFS)?
if RDD_THREE depends on RDD_TWO and this in turn depends on RDD_ONE (lineage) if I didn't use the cache () method on RDD_THREE Spark should recalculate RDD_ONE (reread it from disk) and then RDD_TWO to get RDD_THREE?
Thanks in advance.
ANSWER
Answered 2021-Jun-09 at 06:13In spark there are two types of operations: transformations and actions. A transformation on a dataframe will return another dataframe and an action on a dataframe will return a value.
Transformations are lazy, so when a transformation is performed spark will add it to the DAG and execute it when an action is called.
Suppose, you read a file into a dataframe, then perform a filter, join, aggregate, and then count. The count operation which is an action will actually kick all the previous transformation.
If we call another action(like show) the whole operation is executed again which can be time consuming. So, if we want not to run the whole set of operation again and again we can cache the dataframe.
Few pointers you can consider while caching:
- Cache only when the resulting dataframe is generated from significant transformation. If spark can regenerate the cached dataframe in few seconds then caching is not required.
- Cache should be performed when the dataframe is used for multiple actions. If there are only 1-2 actions on the dataframe then it is not worth saving that dataframe in memory.
QUESTION
I'm try to execute the below hive query on Azure HDInsight cluster but it's taking unprecedented amount of time to finish. Did implemented hive settings but of no use. Below are the details:
Table
CREATE TABLE DB_MYDB.TABLE1(
MSTR_KEY STRING,
SDNT_ID STRING,
CLSS_CD STRING,
BRNCH_CD STRING,
SECT_CD STRING,
GRP_CD STRING,
GRP_NM STRING,
SUBJ_DES STRING,
GRP_DESC STRING,
DTL_DESC STRING,
ACTV_FLAG STRING,
CMP_NM STRING)
STORED AS ORC
TBLPROPERTIES ('ORC.COMPRESS'='SNAPPY');
Hive Query
INSERT OVERWRITE TABLE DB_MYDB.TABLE1
SELECT
CURR.MSTR_KEY,
CURR.SDNT_ID,
CURR.CLSS_CD,
CURR.BRNCH_CD,
CURR.SECT_CD,
CURR.GRP_CD,
CURR.GRP_NM,
CURR.SUBJ_DES,
CURR.GRP_DESC,
CURR.DTL_DESC,
'Y',
CURR.CMP_NM
FROM DB_MYDB.TABLE2 CURR
LEFT OUTER JOIN DB_MYDB.TABLE3 PREV
ON (CURR.SDNT_ID=PREV.SDNT_ID
AND CURR.CLSS_CD=PREV.CLSS_CD
AND CURR.BRNCH_CD=PREV.BRNCH_CD
AND CURR.SECT_CD=PREV.SECT_CD
AND CURR.GRP_CD=PREV.GRP_CD
AND CURR.GRP_NM=PREV.GRP_NM)
WHERE PREV.SDNT_ID IS NULL;
But the query is running for hours. Below is the detail:
--------------------------------------------------------------------------------
VERTICES STATUS TOTAL COMPLETED RUNNING PENDING FAILED KILLED
--------------------------------------------------------------------------------
Map 1 .......... SUCCEEDED 46 46 0 0 0 0
Map 3 .......... SUCCEEDED 169 169 0 0 0 0
Reducer 2 .... RUNNING 1009 825 184 0 0 0
--------------------------------------------------------------------------------
VERTICES: 02/03 [======================>>----] 84% ELAPSED TIME: 13622.73 s
--------------------------------------------------------------------------------
I did set some hive properties
SET hive.execution.engine=tez;
SET hive.tez.container.size=10240;
SET tez.am.resource.memory.mb=10240;
SET tez.task.resource.memory.mb=10240;
SET hive.auto.convert.join.noconditionaltask.size=3470;
SET hive.vectorized.execution.enabled = true;
SET hive.vectorized.execution.reduce.enabled=true;
SET hive.vectorized.execution.reduce.groupby.enabled=true;
SET hive.cbo.enable=true;
SET hive.exec.compress.output=true;
SET mapred.output.compression.codec=org.apache.hadoop.io.compress.SnappyCodec;
SET mapred.output.compression.type=BLOCK;
SET hive.compute.query.using.stats=true;
SET hive.merge.mapfiles = true;
SET hive.merge.mapredfiles = true;
SET hive.merge.tezfiles = true;
SET hive.merge.size.per.task=268435456;
SET hive.merge.smallfiles.avgsize=16777216;
SET hive.merge.orcfile.stripe.level=true;
Records in Tables:
DB_MYDB.TABLE2= 337319653
DB_MYDB.TABLE3= 1946526625
There doesn't seem to be any impact on the query. Can anyone help me to:
- Understand that why this query is not completing and taking indefinite time?
- How can I optimize it to work faster and complete?
Using the versions:
Hadoop 2.7.3.2.6.5.3033-1
Hive 1.2.1000.2.6.5.3033-1
Azure HDInsight 3.6
Attempt_1:
As suggested by @leftjoin tried to set the set hive.exec.reducers.bytes.per.reducer=32000000;. This worked until the second last step of the hive script but at the last it failed with Caused by: java.io.IOException: Map_1: Shuffle failed with too many fetch failures and insufficient progress!
Last Query:
INSERT OVERWRITE TABLE DB_MYDB.TABLE3
SELECT
CURR_FULL.MSTR_KEY,
CURR_FULL.SDNT_ID,
CURR_FULL.CLSS_CD,
CURR_FULL.BRNCH_CD,
CURR_FULL.GRP_CD,
CURR_FULL.CHNL_CD,
CURR_FULL.GRP_NM,
CURR_FULL.GRP_DESC,
CURR_FULL.SUBJ_DES,
CURR_FULL.DTL_DESC,
(CASE WHEN CURR_FULL.SDNT_ID = SND_DELTA.SDNT_ID THEN 'Y' ELSE
CURR_FULL.SDNT_ID_FLAG END) AS SDNT_ID_FLAG,
CURR_FULL.CMP_NM
FROM
DB_MYDB.TABLE2 CURR_FULL
LEFT OUTER JOIN DB_MYDB.TABLE1 SND_DELTA
ON (CURR_FULL.SDNT_ID = SND_DELTA.SDNT_ID);
-----------------------------------------------------------------
VERTICES STATUS TOTAL COMPLETED RUNNING PENDING FAILED KILLED
-----------------------------------------------------------------
Map 1 ......... RUNNING 1066 1060 6 0 0 0
Map 4 .......... SUCCEEDED 3 3 0 0 0 0
Reducer 2 RUNNING 1009 0 22 987 0 0
Reducer 3 INITED 1 0 0 1 0 0
-----------------------------------------------------------------
VERTICES: 01/04 [================>>--] 99% ELAPSED TIME: 18187.78 s
Error:
Caused by: java.io.IOException: Map_1: Shuffle failed with too many fetch failures and insufficient progress!failureCounts=8, pendingInputs=1058, fetcherHealthy=false, reducerProgressedEnough=false, reducerStalled=false
ANSWER
Answered 2021-Jun-07 at 03:19if you don't have index on your fk columns , you should add them for sure , here is my suggestion:
create index idx_TABLE2 on table DB_MYDB.TABLE2 (SDNT_ID,CLSS_CD,BRNCH_CD,SECT_CD,GRP_CD,GRP_NM) AS 'COMPACT' WITH DEFERRED REBUILD;
create index idx_TABLE3 on table DB_MYDB.TABLE3(SDNT_ID,CLSS_CD,BRNCH_CD,SECT_CD,GRP_CD,GRP_NM) AS 'COMPACT' WITH DEFERRED REBUILD;
be noticed from hive version 3.0 , indexing has been removed from hive and alternatively you can use materialized views (supported from Hive 2.3.0 and above) that gives you the same performance.
QUESTION
The goal is to have a Spark Streaming application that read data from Kafka and use Delta Lake to create store data. The granularity of the delta table is pretty granular, the first partition is the organization_id (there are more than 5000 organizations) and the second partition is the date.
The application has a expected latency, but it does not last more than one day up. The error is always about memory as I'll show below.
OpenJDK 64-Bit Server VM warning: INFO: os::commit_memory(0x00000006f8000000, 671088640, 0) failed; error='Cannot allocate memory' (errno=12)
There is no persistence and the memory is already high for the whole application.
What I've triedIncreasing memory and workes were the first things I've tried, but the number of partitions were changed as well, from 4 to 16.
Script of Executionspark-submit \
--verbose \
--master yarn \
--deploy-mode cluster \
--driver-memory 2G \
--executor-memory 4G \
--executor-cores 2 \
--num-executors 4 \
--files s3://my-bucket/log4j-driver.properties,s3://my-bucket/log4j-executor.properties \
--jars /home/hadoop/delta-core_2.12-0.8.0.jar,/usr/lib/spark/external/lib/spark-sql-kafka-0-10.jar \
--class my.package.app \
--conf spark.driver.memoryOverhead=512 \
--conf spark.executor.memoryOverhead=1024 \
--conf spark.memory.fraction=0.8 \
--conf spark.memory.storageFraction=0.3 \
--conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
--conf spark.rdd.compress=true \
--conf spark.yarn.max.executor.failures=100 \
--conf spark.yarn.maxAppAttempts=100 \
--conf spark.task.maxFailures=100 \
--conf spark.executor.heartbeatInterval=20s \
--conf spark.network.timeout=300s \
--conf spark.driver.maxResultSize=0 \
--conf spark.driver.extraJavaOptions="-XX:-PrintGCDetails -XX:-PrintGCDateStamps -XX:-UseParallelGC -XX:+UseG1GC -XX:-UseConcMarkSweepGC -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/tmp/dump-driver.hprof -Dlog4j.configuration=log4j-driver.properties -Dvm.logging.level=ERROR -Dvm.logging.name=UsageFact -Duser.timezone=UTC" \
--conf spark.executor.extraJavaOptions="-XX:-PrintGCDetails -XX:-PrintGCDateStamps -XX:-UseParallelGC -XX:+UseG1GC -XX:-UseConcMarkSweepGC -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/tmp/dump-executor.hprof -Dlog4j.configuration=log4j-executor.properties -Dvm.logging.level=ERROR -Dvm.logging.name=UsageFact -Duser.timezone=UTC" \
--conf spark.sql.session.timeZone=UTC \
--conf spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension \
--conf spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog \
--conf spark.delta.logStore.class=org.apache.spark.sql.delta.storage.S3SingleDriverLogStore \
--conf spark.databricks.delta.retentionDurationCheck.enabled=false \
--conf spark.databricks.delta.vacuum.parallelDelete.enabled=true \
--conf spark.sql.shuffle.partitions=16 \
--name "UsageFactProcessor" \
application.jar
val source = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", broker)
.option("subscribe", topic)
.option("startingOffsets", "latest")
.option("failOnDataLoss", value = false)
.option("fetchOffset.numRetries", 10)
.option("fetchOffset.retryIntervalMs", 1000)
.option("maxOffsetsPerTrigger", 50000L)
.option("kafkaConsumer.pollTimeoutMs", 300000L)
.load()
val transformed = source
.transform(applySchema)
val query = transformed
.coalesce(16)
.writeStream
.trigger(Trigger.ProcessingTime("1 minute"))
.outputMode(OutputMode.Append)
.format("delta")
.partitionBy("organization_id", "date")
.option("path", table)
.option("checkpointLocation", checkpoint)
.option("mergeSchema", "true")
.start()
spark.catalog.clearCache()
query.awaitTermination()
Spark: 3.0.1
Delta: 0.8.0
QuestionWhat do you think may be causing this problem?
ANSWER
Answered 2021-Jun-08 at 11:11Just upgraded the version to Delta.io 1.0.0 and it stopped happening.
QUESTION
I have a webapp that runs fine in JBoss EAP 6.4. I want to add some functionality to my webapp so that it can process Parquet files that reside in AzureBlob storage. I add a single dependency to my pom.xml:
org.apache.hadoop
hadoop-azure
3.1.0
If I now try to start my webapp, it fails at start up:
09:29:43,703 ERROR [org.jboss.msc.service.fail] (MSC service thread 1-10) MSC000001: Failed to start service jboss.deployment.unit."myApp-0.0.1-SNAPSHOT.war".POST_MODULE: org.jboss.msc.service.StartException in service jboss.deployment.unit."myApp-0.0.1-SNAPSHOT.war".POST_MODULE: JBAS018733: Failed to process phase POST_MODULE of deployment "myApp-0.0.1-SNAPSHOT.war" at org.jboss.as.server.deployment.DeploymentUnitPhaseService.start(DeploymentUnitPhaseService.java:166) [jboss-as-server-7.5.0.Final-redhat-21.jar:7.5.0.Final-redhat-21] at org.jboss.msc.service.ServiceControllerImpl$StartTask.startService(ServiceControllerImpl.java:1980) [jboss-msc-1.1.5.Final-redhat-1.jar:1.1.5.Final-redhat-1] ... Caused by: org.jboss.as.server.deployment.DeploymentUnitProcessingException: JBAS011232: Only one JAX-RS Application Class allowed. com.sun.jersey.api.core.ResourceConfig com.sun.jersey.api.core.DefaultResourceConfig com.sun.jersey.api.core.PackagesResourceConfig com.mycompany.myapp.rest.RestApplication com.sun.jersey.api.core.ClassNamesResourceConfig com.sun.jersey.api.core.ScanningResourceConfig com.sun.jersey.api.core.servlet.WebAppResourceConfig com.sun.jersey.api.core.ApplicationAdapter com.sun.jersey.server.impl.application.DeferredResourceConfig com.sun.jersey.api.core.ClasspathResourceConfig at org.jboss.as.jaxrs.deployment.JaxrsScanningProcessor.scan(JaxrsScanningProcessor.java:206) at org.jboss.as.jaxrs.deployment.JaxrsScanningProcessor.deploy(JaxrsScanningProcessor.java:104) at org.jboss.as.server.deployment.DeploymentUnitPhaseService.start(DeploymentUnitPhaseService.java:159) [jboss-as-server-7.5.0.Final-redhat-21.jar:7.5.0.Final-redhat-21] ... 5 more
09:29:43,709 ERROR [org.jboss.as.controller.management-operation] (Controller Boot Thread) JBAS014612: Operation ("deploy") failed - address: ([("deployment" => "myApp-0.0.1-SNAPSHOT.war")]) - failure description: {"JBAS014671: Failed services" => {"jboss.deployment.unit."myApp-0.0.1-SNAPSHOT.war".POST_MODULE" => "org.jboss.msc.service.StartException in service jboss.deployment.unit."myApp-0.0.1-SNAPSHOT.war".POST_MODULE: JBAS018733: Failed to process phase POST_MODULE of deployment "myApp-0.0.1-SNAPSHOT.war" Caused by: org.jboss.as.server.deployment.DeploymentUnitProcessingException: JBAS011232: Only one JAX-RS Application Class allowed. com.sun.jersey.api.core.ResourceConfig com.sun.jersey.api.core.DefaultResourceConfig com.sun.jersey.api.core.PackagesResourceConfig com.mycompany.myapp.rest.RestApplication com.sun.jersey.api.core.ClassNamesResourceConfig com.sun.jersey.api.core.ScanningResourceConfig com.sun.jersey.api.core.servlet.WebAppResourceConfig com.sun.jersey.api.core.ApplicationAdapter com.sun.jersey.server.impl.application.DeferredResourceConfig com.sun.jersey.api.core.ClasspathResourceConfig"}}
The message "JBAS011232: Only one JAX-RS Application Class allowed" seems to be caused by my webapp trying to use both RestEasy and Jersey. JBoss uses RestEasy by default. Apparently, hadoop-azure must have a Jersey application class. How can I eliminate this problem by indicating that I don't want to use the Jersey-based application class?
ANSWER
Answered 2021-Jun-03 at 20:31hadoop-azure pulls in hadoop-common, which pulls in Jersey. In the version of hadoop-azure you're using, hadoop-common is in compile . In new version, it is in provided scope. So you can just upgrade the hadoop-azure dependency to the latest one. If you need hadoop-common to compile, then you can redeclare hadoop-common and put it in provided scope.
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