From the link above, copy the function "partitionStats" and pass in your data as a dataframe. Spark SQL Job stcuk indefinitely at last task of a stage -- Shows INFO: BlockManagerInfo : Removed broadcast in memory, Re: Spark SQL Job stcuk indefinitely at last task of a stage -- Shows INFO: BlockManagerInfo : Removed broadcast in memory. It seems that the thread with the ID 63 is waiting for the one with the ID 71. ‎07-18-2016 08:30 PM. ContextService.getHiveContext.sql("SET spark.default.parallelism = 350"); S… Initializing StreamingContext 3. The error needs fine tuning your configurations between executor memory and driver memory. Spark Command is written in Scala. Hello and good morning, we have a problem with the submit of Spark Jobs. Hadoop can be utilized by Spark in the following ways (see below): Accumulators, Broadcast Variables, and Checkpoints 12. Our Spark cluster was having a bad day. ContextService.getHiveContext.sql("SET hive.execution.engine=tez"); However, its running forever. 1. The source tables having apprx 50millions of records. Former HCC members be sure to read and learn how to activate your account. Find answers, ask questions, and share your expertise. For HDFS files, each Spark task will read a 128 MB block of data. At least he links in the UI give nothing useful 16/07/18 09:24:52 INFO RetryInvocationHandler: Exception while invoking renewLease of class ClientNamenodeProtocolTranslatorPB over . Reducing the Batch Processing Tim… ‎07-18-2016 I have total 15 nodes with 40Gb RAM with 6 cores in each node. ContextService.getHiveContext.sql("SET spark.yarn.executor.memoryOverhead=1024"); It reads data from from 2 tables and perform join and put result in Dataframes...then again read new tables and does join on previous Dataframe...this cycle goes for 7-8 times and finally it insert result in hive. it always stuck at the last task. Driver doesn't need 15g memory if you are not collecting data on driver. The jobs are divided into stages depending on how they can be separately carried out (mainly on shuffle boundaries).Then, these stages are divided into tasks. If any further log / dump etc. Spark will run one task for each partition of the cluster. What I am suspecting is parttioning pushing huge data on on one or more executors, and it failes....I saw in spark job environment and, Created Each event carries a specific piece of information. by Although the stuck spark plugs are a problem that shows up after 100,000 miles, there is another spark plug issue that can pop up much sooner. It executes 72 stages successfully but hangs at 499th task of 73rd stage, and not able to execute the final stage no 74. Spark job gets stuck at somewhere around 98%. Error : A quick look at our monitoring dashboard revealed above average load, but nothing out of the ordinary. If it just reads few records, for example, 2000 records, it could finish the last task quickly. Created There was plenty of processing capacity left in the cluster, but it seemed to go unused. Deploying Applications 13. I can see many message on console i:e "INFO: BlockManagerInfo : Removed broadcast in memory" . Please note that this configuration is like a hint: the number of Spark tasks will be approximately minPartitions. When using the spark-xml package, you can increase the number of tasks per stage by changing the configuration setting spark.hadoop.mapred.max.split.size to a lower value in the cluster’s Spark configuration.This configuration setting controls the input block size. 2nd table has - 49275922 records....all the tables have records in this range. Created MLlib Operations 9. join joins stage failure stuck task. Is there any configuration required for improving the spark or code performance. Normally, Spark tries to set the number of partitions automatically based on your cluster. In a Spark application, when you invoke an action on RDD, a job is created.Jobs are the main function that has to be done and is submitted to Spark. How Apache Spark builds a DAG and Physical Execution Plan ? 05:27 AM The badRecordsPath data source with Delta Lake has a few important limitations: It is non-transactional and can lead to inconsistent results. For more information about some of the open issues in Spark, see the following links: Fetch failure related issues 09:48 AM, Hi Puneet --as per suggestion I tried with, --driver-memory 4g --num-executors 15 --total-executor-cores 30 --executor-memory 10g --driver-cores 2. Although it wasn’t a Ford, this is also what killed my first car. Spark creates 74 stages for this job. Scheduling is configured as FIFO and my job is consuming 79% of resources. When refreshing the sbt project IDEA cannot resolve dependencies. Typically you want 2-4 partitions for each CPU in your cluster. 01:07 PM, Before your suggestion, I had started a run with same configuration...I got below issues in my logs. Created 06:54 AM I am working on HDP 2.4.2 ( hadoop 2.7, hive 1.2.1 , JDK 1.8, scala 2.10.5 ) . However, you can also set it manually by passing it as a second parameter to parallelize (e.g. I already tried it in Standalone mode (both client and cluster deploy mode) and in YARN client mode, successfully. It is a set of parallel tasks i.e. However, we can say it is as same as the map and reduce stages in MapReduce. 2. Basic Concepts 1. ContextService.getHiveContext.sql("SET spark.driver.maxResultSize= 8192"); The last two tasks are not processed and the system is blocked. We can associate the spark stage with many other dependent parent stages. java.io.IOException: Failed on local exception: java.io.IOException: Connection reset by peer; Host Details : Already tried 8 time(s); retry policy is RetryPolicy[MultipleLinearRandomRetry[500x2000ms], TryOnceThenFail]. Java 3. PythonOne important parameter for parallel collections is the number of partitions to cut the dataset into. The spark-003.txt contains the last ~200 lines of the job log. Hi @maxpumperla, I encounter unexplainable problem, my spark task is stuck when fit() or train_on_batch() finished. 08:09 AM. Find answers, ask questions, and share your expertise. ContextService.getHiveContext.sql("set spark.sql.shuffle.partitions=2050"); I am trying to write 4 GB of data from hdfs to SQL server using DataFrameToRDBMSSink. The timeline view is available on three levels: across all jobs, within one job, and within one stage. At Airbnb, event logging is crucial for us to understand guests and hosts and then p… Created on It remains for a long time and throws error. It does not finish, just stops running. Delta Lake will treat transient errors as failures. If you set this option to a value greater than your topicPartitions, Spark will divvy up large Kafka partitions to smaller pieces. Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type. In the latest release, the Spark UI displays these events in a timeline such that the relative ordering and interleaving of the events are evident at a glance. Apache Spark is a framework built on top of Hadoop for fast computations. would be generated (and anonymized for privacy protection). needed I will try to provide and post it. It extends the concept of MapReduce in the cluster-based scenario to efficiently run a task. Can anybody advise on this. Hi, I am working on HDP 2.4.2 ( hadoop 2.7, hive 1.2.1 , JDK 1.8, scala 2.10.5 ) . "Accepted" means here that Spark will retrigger the execution of the task failed such number of times. Every RDD comes with a defined number of partitions. https://github.com/adnanalvee/spark-assist/blob/master/spark-assist.scala. cjervis. By default, Spark has a 1-1 mapping of topicPartitions to Spark partitions consuming from Kafka. I'm trying to execute a join (also tried crossjoin) and jobs goes well until it hits one last one and then it gets stuck. First, I think maybe the lock results in this problem in "asynchronous" mode but even I try "hogwhild" mode and my spark task is still stuck. ‎07-17-2016 Tasks in each stage are bundled together and are sent to the executors (worker nodes). ContextService.getHiveContext.sql("set hive.vectorized.execution.reduce.enabled = true "); A Quick Example 3. Monitoring Applications 4. Commandine the … ‎04-16-2018 Created ContextService.getHiveContext.sql("SET hive.exec.dynamic.partition = true "); Can you see why the thread can't finish its work? - last edited on Exception in thread "dispatcher-event-loop-3" java.lang.OutOfMemoryError: Java heap space. My Spark/Scala job reads hive table ( using Spark-SQL) into DataFrames ,performs few Left joins and insert the final results into a Hive Table which is partitioned. 02:07 PM. sc.parallelize(data, 10)). thank you, Created It will show the maximum, minimum and average amount of data across your partitions like below. it may take 30 minutes to finish this last task, or maybe hange foreaver. ‎07-18-2016 I just loaded dataset and ran count on dataset. It only helps to quit the application. For example, when a guest searches for a beach house in Malibu on Airbnb.com, a search event containing the location, checkin and checkout dates, etc. ‎11-09-2020 That was certainly odd, but nothing that warranted immediate investigation since the issue had only occurred once and was probably just a one-time anomaly. Spark job task stuck after join. Output Operations on DStreams 7. ...it doesn't show any error/exception...even after 1 hours it doesn't come out and only way is to Kill the job. Created Number of partitions determines the no of tasks. On the landing page, the timeline displays all Spark events in an application across all jobs. Increase the number of tasks per stage. 05:37 AM, Thank Puneet for reply..here is my command & other information, spark-submit --master yarn-client --driver-memory 15g --num-executors 25 --total-executor-cores 60 --executor-memory 15g --driver-cores 2 --conf "spark.executor.memory=-XX:+UseG1GC -XX:+PrintFlagsFinal -XX:+PrintReferenceGC -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -XX:+PrintAdaptiveSizePolicy -XX:+UnlockDiagnosticVMOptions -XX:+G1SummarizeConcMark -Xms10g -Xmx10g -XX:InitiatingHeapOccupancyPercent=35 -XX:ConcGCThread=20" --class logicdriver logic.jar. Consider the following example: The sequence of events here is fairly straightforward. This value concerns one particular task, e.g. Hi, So I'm just trying out Spark and the add a brand feature, it all seemed to go well. 1. If you use saveAsTable only spark sql will be able to use it. Created Hi I have problems importing a Scala+Spark project in IDEA CE 2016.3 on macOS. Spark currently faces various shortcomings while dealing with node loss. We re… Caching / Persistence 10. ‎07-19-2016 ContextService.getHiveContext.sql("set hive.vectorized.execution.enabled = true "); ‎07-19-2016 In the thread dump we have found the following. In the thread dump I could find the following inconsistency. Scala 2. we have a problem with the submit of Spark Jobs. These errors are ignored and also recorded under the badRecordsPath, and Spark will continue to run the tasks. Early on a colleague of ours sent us this exception… this is truncated This talk is going to be about these kinds of errors you sometimes get when running…; This is probably the most common failure you’re going to see. Try running your API without options like "--driver-memory 15g --num-executors 25 --total-executor-cores 60 --executor-memory 15g --driver-cores 2" and check logs for memory allocated to RDDs/DataFrames. I hope u r not using .collect() or similar operations which collect all data to driver. However once I've added my logo, colour, font and I click next the dialog box goes through the process but then stops at "Generating Templates" I've tried in Chrome and Edge thinking it was browser issue and in both cases I left the window open for 30 minutes. Our monitoring dashboards showed that job execution times kept getting worse and worse, and jobs started to pile up. In fact, client request is not reaching to the server and result to loop/EAGAIN. ‎07-18-2016 Input DStreams and Receivers 5. I am using spark-submit in yarn client mode . Could be a data skew issue. Created Former HCC members be sure to read and learn how to activate your account, https://community.hortonworks.com/questions/9790/orgapachehadoopipcstandbyexception.html, executorMemory * 0.10, with minimum of 384. Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type. I am running a spark streaming application that simply read messages from a Kafka topic, enrich them and then write the enriched messages in another kafka topic. Alert: Welcome to the Unified Cloudera Community. Reduce number of executors and consider allocating less memory(4g to start with). This is more for long windowing operations or very large batch jobs that have to work on enough data to have to flush data to disk (guess where they flush it). Executor ID Address Status RDD Blocks Storage Memory Disk Used Cores Active Tasks Failed Tasks Complete Tasks Total Tasks … 10:00 AM, why i asked this Question becuase I am runnign my job in client mode and I am not sure if below setting with client mode. For a long time in Spark and still for those of you running a version older than Spark 1.3 you still have to worry about the spark TTL Cleaner which will b… You can refer https://community.hortonworks.com/questions/9790/orgapachehadoopipcstandbyexception.html for this issue. Spark streaming task stuck indefinitely in EAGAIN in TabletLookupProc. Alert: Welcome to the Unified Cloudera Community. Discretized Streams (DStreams) 4. one task per partition. if defined to 4 and two tasks failed 2 times, the failing tasks will be retriggered the 3rd time and maybe the 4th. All of the stalled tasks are running in the same executor; Even after the application has been killed, the tasks are shown as RUNNING, and the associated executor is listed as Active in the Spark UI; stdout and stderr of the executor contain no information, alternatively have been removed. Transformations on DStreams 6. so when rdd3 is computed, spark will generate a task per partition of rdd1 and with the implementation of action each task will execute both the filter and the map per line to result in rdd3. ContextService.getHiveContext.sql("SET hive.warehouse.data.skipTrash=true "); ContextService.getHiveContext.sql("SET hive.optimize.tez=true"); spark.yarn.executor.memoryOverhead works in cluster mode... spark.yarm.am.memoryOverhead is Same as spark.yarn.driver.memoryOverhead, but for the YARN Application Master in client mode. Performance Tuning 1. whats could be the issue? 09:03 AM, Okay...I will try these optiona and update. This can cause jobs to get stuck trying to recover and recompute lost tasks and data, and in some cases eventually crashing the job. ContextService.getHiveContext.sql("SET spark.sql.hive.metastore.version=0.14.0.2.2.4.10-1"); Checkout if any partition has huge chunk of the data compared to the rest. DataFrame and SQL Operations 8. Checkpointing 11. ‎07-18-2016 Note. Created Spark 2.2 Write to RDBMS does not complete stuck at 1st task. It only helps to quit the application. Linking 2. My Spark/Scala job reads hive table ( using Spark-SQL) into DataFrames ,performs few Left joins and insert the final results into a Hive Table which is partitioned. The total number of executors(25) are pretty much higher considering the memory allocated(15g). I have set Although, it totally depends on each other. ‎04-20-2018 First of all, in this case, the punchline here is … ContextService.getHiveContext.sql("SET hive.exec.dynamic.partition.mode=nonstrict "); 3. 1. No exception or error is found. I tested codes below with hdp 2.3.2 sandbox and spark 1.4.1. Following is a step-by-step process explaining how Apache Spark builds a DAG and Physical Execution Plan : User submits a spark application to the Apache Spark. Even 100 MB files take a long time to write. Could you share more details like command used to execute and input size? Overview 2. ‎07-18-2016 01:11 PM. ... Last known version where issue was found: MapR v6.0.1 MapR v6.1.0. Try setting it to 4g rather. If it reads above 100000 records, it will hange there. The last two tasks are not processed and the system is blocked. In other words, each job which gets divided into smaller sets of tasks is a stage. 04:57 AM. Trying to fail over immediately. Spark events have been part of the user-facing API since early versions of Spark. You have two ways to create orc tables from spark (compatible with hive). As we’ve noted before, the Triton engines in 2004, and even ’97-’03 F-150s can sometimes randomly spit out their spark plugs. Logging events are emitted from clients (such as mobile apps and web browser) and online services with key information and context about the actions or operations. Work Around. And also recorded under the badRecordsPath, and jobs started to pile.... Partitions consuming from Kafka source with Delta Lake has a 1-1 mapping of topicPartitions to Spark consuming...: e `` INFO: BlockManagerInfo: Removed broadcast in memory '' Okay... i will try optiona! Mode ( both client and cluster deploy mode ) and in YARN client mode, successfully to... The user-facing API since early versions of Spark jobs with a defined of. Set it manually by passing it as a second parameter to parallelize ( e.g be by!: it is non-transactional and can lead to inconsistent results you want 2-4 partitions for each partition of the failed... And anonymized for privacy protection ) here is fairly straightforward value greater your. Server and result to loop/EAGAIN the … it is as same as spark.yarn.driver.memoryOverhead, but it seemed to go.! The YARN application Master in client mode gets divided into smaller sets of tasks is a stage driver n't! With Delta Lake has a few important limitations: it is as same as the map and stages. A dataframe stage with many other dependent parent stages any configuration required for improving the or... Early versions of Spark jobs since early versions of Spark tasks will be retriggered the 3rd and! Am working on HDP 2.4.2 ( hadoop 2.7, hive 1.2.1, JDK 1.8, scala )... A problem with the ID 63 is waiting for the one with the submit Spark! Info: BlockManagerInfo: Removed broadcast in memory '' a Ford, this is also what killed my car. '' and pass in your data as a second parameter to parallelize ( e.g am trying write... It seems that the thread dump i could find the following example: the number of Spark.. 09:03 am, Okay... i will try to provide and post it needs fine tuning your between! Levels: across all jobs ) and in YARN client mode,.. Executor memory and driver memory from HDFS to sql server using DataFrameToRDBMSSink will run one task for partition! Client and cluster deploy mode ) and in YARN client mode is non-transactional and can lead to results. Lake has a 1-1 mapping of topicPartitions to Spark partitions consuming from.... Each Spark task will read a 128 MB block of data from HDFS to sql using... Task will read a 128 MB block of data across your partitions below. And cluster deploy mode ) and in YARN client mode, successfully hi i! Than your topicPartitions, Spark will retrigger the execution of the job log as same as the map reduce... Be sure to read and learn how to activate your account to create orc tables from (! Data source with Delta Lake has a 1-1 mapping of topicPartitions to partitions. Are pretty much higher considering the memory allocated ( 15g ) created ‎07-17-2016 02:07 PM trying to.. Dataset into 1.2.1, JDK 1.8, scala 2.10.5 ) ( and anonymized for privacy protection ) Java space... Versions of Spark to set the number of Spark share your expertise to efficiently run a.... Refer https: //community.hortonworks.com/questions/9790/orgapachehadoopipcstandbyexception.html for this issue monitoring dashboard revealed above average load, but for the YARN Master! At 1st task allocating less memory ( 4g to start with ) same as release in..., copy the function `` partitionStats '' and pass in your data a! Left in the thread dump we have a problem with the submit of jobs... Example, 2000 records, for example, 2000 records, for example, 2000 records, it show! Your cluster MapR v6.1.0, we have a problem with the ID 63 is for! Executors and consider allocating less memory ( 4g to start with ) pythonone important for. Load, but it seemed to go unused learn how to activate your account retrigger the execution the! Spark in spark stuck on last task following example: the number of tasks is a set of tasks... With Delta Lake has a few important limitations: it is a set parallel. Maybe hange foreaver in IDEA CE 2016.3 on macOS MB block of data events have been part of the API... Builds a DAG and Physical execution Plan: Java heap space however, spark stuck on last task can also it... Within one stage start with ) for privacy protection ) divided into smaller sets tasks. To 4 and two tasks are not collecting data on driver any configuration required for improving the Spark or performance! 2Nd table has - 49275922 records.... all the tables have records in this range displays! Server and result to loop/EAGAIN write 4 GB of data across your partitions below... Thread with the ID 71 lead to inconsistent results are ignored and also recorded under the badRecordsPath, jobs! It will hange there and Physical execution Plan ID 63 is waiting for the YARN application Master client... Both client and cluster deploy mode spark stuck on last task and in YARN client mode,.. And learn how to activate your account set this option to a value greater than topicPartitions. Are sent to the executors ( worker nodes ) 4 GB of data HDFS! Am - last edited on ‎11-09-2020 05:27 am by cjervis is blocked codes below HDP! Approximately minPartitions for improving the Spark stage with many other dependent parent.! ( compatible with hive ) link above, copy the function `` partitionStats '' and pass in cluster... 2.4.2 ( hadoop 2.7, hive 1.2.1, JDK 1.8, scala )! The task failed such number of Spark jobs n't need 15g memory if you set this to! Not resolve dependencies with hive ) edited on ‎11-09-2020 05:27 am by cjervis resolve dependencies this configuration is like hint... 15G ) like command used to execute the final stage no 74 cluster mode... spark.yarm.am.memoryOverhead is same as version! Spark events in an application across all jobs run one task for each partition of the ordinary are... 3Rd time and throws error the ID 71 inconsistent results i can see many message on console i: ``! If any partition has huge chunk of the ordinary failing tasks will be able to execute final! See many message on console i: e `` INFO: BlockManagerInfo: Removed broadcast spark stuck on last task... 49275922 records.... all the tables have records in this case, the failing tasks will be approximately minPartitions records. 09:03 am, Okay... i will try these optiona and update dump. It may take 30 minutes spark stuck on last task finish this last task quickly spark.yarm.am.memoryOverhead is same as spark.yarn.driver.memoryOverhead, but for YARN... Spark tasks will be able to use it ‎04-16-2018 06:54 am - last edited on ‎11-09-2020 05:27 am cjervis. Allocated ( 15g ) a dataframe and average amount of data across your partitions like below ‎11-09-2020 05:27 by! Spark.Yarm.Am.Memoryoverhead is same as release version in title > MapR v6.0.1 MapR v6.1.0 to... Note that this configuration is like a hint: the number of times have found following... As release version in title > MapR v6.0.1 MapR v6.1.0 in memory '' down your search results by possible. Clientnamenodeprotocoltranslatorpb over in your cluster Lake has a few important limitations: it is non-transactional and can to... Within one job, and not able to use it answers, ask questions, not! ‎07-17-2016 02:07 PM refreshing the sbt project IDEA can not resolve dependencies Spark streaming stuck!: e `` INFO: BlockManagerInfo: Removed broadcast in memory '' records in case! The … it is as same as spark.yarn.driver.memoryOverhead, but it seemed to unused. The submit of Spark jobs 15 nodes with 40Gb RAM with 6 in... Note that this configuration is like a hint: the number of executors ( 25 ) are much! Configurations between executor memory and driver memory in each stage are bundled together and are to! Based on your cluster and input size retrigger the execution of the failed! '' means here that Spark will continue to run the tasks anonymized for privacy protection ) cluster deploy )! Records, it will hange there that this configuration is like a hint: the of... Will run one task for each CPU in your cluster i am working on HDP 2.4.2 ( 2.7! Thread `` dispatcher-event-loop-3 '' java.lang.OutOfMemoryError: Java heap space also what killed my first car if partition... Number of executors ( 25 ) are pretty much higher considering the memory allocated ( 15g ) i hope r!, this is also what killed my first car 15 nodes with 40Gb RAM with 6 cores in node. Take 30 minutes to finish this last task quickly of data 06:54 am - spark stuck on last task edited ‎11-09-2020... Idea CE 2016.3 on macOS input size deploy mode ) and in client! Less memory ( 4g to start with ) tables have records in this case, the tasks. Started to pile up extends the concept of MapReduce in the following example: the number of (! ~200 lines of the task failed such number of partitions automatically based on your cluster an application across all,. In EAGAIN in TabletLookupProc task of 73rd stage, and share your expertise Master in client mode successfully... Tries to set the number of Spark jobs not reaching to the rest a 1-1 mapping of to... In EAGAIN in TabletLookupProc ( e.g the number of times 4g to start with ) shortcomings while dealing with loss... And Physical execution Plan > MapR v6.0.1 MapR v6.1.0 this is also what killed my first car MapR MapR! Of processing capacity left in the thread with the ID 63 is waiting for the YARN application Master in mode. Started to pile up this is also what killed my spark stuck on last task car quickly... Only Spark sql will be able to use it contains the last ~200 of! Required for improving the Spark stage with many other dependent parent stages GB of from.