For more information about some of the open issues in Spark, see the following links: Fetch failure related issues 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. In fact, client request is not reaching to the server and result to loop/EAGAIN. Spark streaming task stuck indefinitely in EAGAIN in TabletLookupProc. Spark job task stuck after join. 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. Our monitoring dashboards showed that job execution times kept getting worse and worse, and jobs started to pile up. You can refer https://community.hortonworks.com/questions/9790/orgapachehadoopipcstandbyexception.html for this issue. Could be a data skew issue. First of all, in this case, the punchline here is … Even 100 MB files take a long time to write. ‎07-18-2016 Deploying Applications 13. Exception in thread "dispatcher-event-loop-3" java.lang.OutOfMemoryError: Java heap space. Note. These errors are ignored and also recorded under the badRecordsPath, and Spark will continue to run the tasks. Caching / Persistence 10. Can you see why the thread can't finish its work? 16/07/18 09:24:52 INFO RetryInvocationHandler: Exception while invoking renewLease of class ClientNamenodeProtocolTranslatorPB over . Hello and good morning, we have a problem with the submit of Spark Jobs. The last two tasks are not processed and the system is blocked. Delta Lake will treat transient errors as failures. It extends the concept of MapReduce in the cluster-based scenario to efficiently run a task. ‎07-18-2016 At least he links in the UI give nothing useful 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. Transformations on DStreams 6. 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. ContextService.getHiveContext.sql("SET spark.driver.maxResultSize= 8192"); 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. ‎07-18-2016 ContextService.getHiveContext.sql("SET spark.default.parallelism = 350"); 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. 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. 04:57 AM. Alert: Welcome to the Unified Cloudera Community. 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. Driver doesn't need 15g memory if you are not collecting data on driver. Trying to fail over immediately. Former HCC members be sure to read and learn how to activate your account. ‎07-19-2016 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. 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. In other words, each job which gets divided into smaller sets of tasks is a stage. No exception or error is found. Is there any configuration required for improving the spark or code performance. It is a set of parallel tasks i.e. ‎04-20-2018 Scala 2. If it reads above 100000 records, it will hange there. by From the link above, copy the function "partitionStats" and pass in your data as a dataframe. 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. Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type. The last two tasks are not processed and the system is blocked. Spark creates 74 stages for this job. ContextService.getHiveContext.sql("SET spark.sql.hive.metastore.version=0.14.0.2.2.4.10-1"); Try setting it to 4g rather. Alert: Welcome to the Unified Cloudera Community. Can anybody advise on this. 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). 1. Spark job gets stuck at somewhere around 98%. If it just reads few records, for example, 2000 records, it could finish the last task quickly. This value concerns one particular task, e.g. ContextService.getHiveContext.sql("SET hive.exec.dynamic.partition.mode=nonstrict "); I can see many message on console i:e "INFO: BlockManagerInfo : Removed broadcast in memory" . Hadoop can be utilized by Spark in the following ways (see below): I am working on HDP 2.4.2 ( hadoop 2.7, hive 1.2.1 , JDK 1.8, scala 2.10.5 ) . one task per partition. I am trying to write 4 GB of data from hdfs to SQL server using DataFrameToRDBMSSink. Created The error needs fine tuning your configurations between executor memory and driver memory. If any further log / dump etc. Every RDD comes with a defined number of partitions. Hi, I am working on HDP 2.4.2 ( hadoop 2.7, hive 1.2.1 , JDK 1.8, scala 2.10.5 ) . The source tables having apprx 50millions of records. At Airbnb, event logging is crucial for us to understand guests and hosts and then p… Monitoring Applications 4. it always stuck at the last task. A quick look at our monitoring dashboard revealed above average load, but nothing out of the ordinary. However, we can say it is as same as the map and reduce stages in MapReduce. Consider the following example: The sequence of events here is fairly straightforward. it may take 30 minutes to finish this last task, or maybe hange foreaver. Performance Tuning 1. sc.parallelize(data, 10)). 1. Work Around. Could you share more details like command used to execute and input size? It seems that the thread with the ID 63 is waiting for the one with the ID 71. Find answers, ask questions, and share your expertise. I am using spark-submit in yarn client mode . It will show the maximum, minimum and average amount of data across your partitions like below. - last edited on ContextService.getHiveContext.sql("set hive.vectorized.execution.reduce.enabled = true "); How Apache Spark builds a DAG and Physical Execution Plan ? 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. There was plenty of processing capacity left in the cluster, but it seemed to go unused. The badRecordsPath data source with Delta Lake has a few important limitations: It is non-transactional and can lead to inconsistent results. It executes 72 stages successfully but hangs at 499th task of 73rd stage, and not able to execute the final stage no 74. ContextService.getHiveContext.sql("SET hive.warehouse.data.skipTrash=true "); However, its running forever. 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. Tasks in each stage are bundled together and are sent to the executors (worker nodes). Output Operations on DStreams 7. Reduce number of executors and consider allocating less memory(4g to start with). 02:07 PM. 2nd table has - 49275922 records....all the tables have records in this range. ‎07-19-2016 Hi I have problems importing a Scala+Spark project in IDEA CE 2016.3 on macOS. 1. It only helps to quit the application. By default, Spark has a 1-1 mapping of topicPartitions to Spark partitions consuming from Kafka. 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. Discretized Streams (DStreams) 4. 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. 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. We can associate the spark stage with many other dependent parent stages. ‎07-18-2016 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. 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. Created 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… ...it doesn't show any error/exception...even after 1 hours it doesn't come out and only way is to Kill the job. The timeline view is available on three levels: across all jobs, within one job, and within one stage. would be generated (and anonymized for privacy protection). Created 3. Created "Accepted" means here that Spark will retrigger the execution of the task failed such number of times. If you use saveAsTable only spark sql will be able to use it. PythonOne important parameter for parallel collections is the number of partitions to cut the dataset into. thank you, Created 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). whats could be the issue? ContextService.getHiveContext.sql("SET hive.exec.dynamic.partition = true "); 08:09 AM. Although, it totally depends on each other. For HDFS files, each Spark task will read a 128 MB block of data. Created on Spark will run one task for each partition of the cluster. ContextService.getHiveContext.sql("set spark.sql.shuffle.partitions=2050"); I have set Each event carries a specific piece of information. 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. Spark currently faces various shortcomings while dealing with node loss. ‎07-17-2016 01:07 PM, Before your suggestion, I had started a run with same configuration...I got below issues in my logs. Created ContextService.getHiveContext.sql("SET hive.optimize.tez=true"); Apache Spark is a framework built on top of Hadoop for fast computations. I already tried it in Standalone mode (both client and cluster deploy mode) and in YARN client mode, successfully. Spark Command is written in Scala. Scheduling is configured as FIFO and my job is consuming 79% of resources. cjervis. I hope u r not using .collect() or similar operations which collect all data to driver. Initializing StreamingContext 3. I have total 15 nodes with 40Gb RAM with 6 cores in each node. https://github.com/adnanalvee/spark-assist/blob/master/spark-assist.scala. Our Spark cluster was having a bad day. ... Last known version where issue was found: MapR v6.0.1 MapR v6.1.0. 05:27 AM Find answers, ask questions, and share your expertise. ‎07-18-2016 Basic Concepts 1. Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type. The spark-003.txt contains the last ~200 lines of the job log. It only helps to quit the application. 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. Java 3. join joins stage failure stuck task. 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. 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. Linking 2. Input DStreams and Receivers 5. Although it wasn’t a Ford, this is also what killed my first car. I tested codes below with hdp 2.3.2 sandbox and spark 1.4.1. Created This can cause jobs to get stuck trying to recover and recompute lost tasks and data, and in some cases eventually crashing the job. Created Number of partitions determines the no of tasks. Typically you want 2-4 partitions for each CPU in your cluster. 01:11 PM. However, you can also set it manually by passing it as a second parameter to parallelize (e.g. Hi @maxpumperla, I encounter unexplainable problem, my spark task is stuck when fit() or train_on_batch() finished. 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 In the thread dump we have found the following. 08:30 PM. Increase the number of tasks per stage. On the landing page, the timeline displays all Spark events in an application across all jobs. Spark 2.2 Write to RDBMS does not complete stuck at 1st task. Created Normally, Spark tries to set the number of partitions automatically based on your cluster. ContextService.getHiveContext.sql("SET hive.execution.engine=tez"); 09:03 AM, Okay...I will try these optiona and update. 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]. Commandine the … ‎11-09-2020 It does not finish, just stops running. Spark events have been part of the user-facing API since early versions of Spark. Checkpointing 11. ‎07-18-2016 Executor ID Address Status RDD Blocks Storage Memory Disk Used Cores Active Tasks Failed Tasks Complete Tasks Total Tasks … I just loaded dataset and ran count on dataset. You have two ways to create orc tables from spark (compatible with hive). Checkout if any partition has huge chunk of the data compared to the rest. In the thread dump I could find the following inconsistency. Please note that this configuration is like a hint: the number of Spark tasks will be approximately minPartitions. If you set this option to a value greater than your topicPartitions, Spark will divvy up large Kafka partitions to smaller pieces. Hi, So I'm just trying out Spark and the add a brand feature, it all seemed to go well. 06:54 AM DataFrame and SQL Operations 8. MLlib Operations 9. A Quick Example 3. ‎04-16-2018 Error : Reducing the Batch Processing Tim… 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. Overview 2. We re… S… needed I will try to provide and post it. When refreshing the sbt project IDEA cannot resolve dependencies. we have a problem with the submit of Spark Jobs. It remains for a long time and throws error. Accumulators, Broadcast Variables, and Checkpoints 12. 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. ContextService.getHiveContext.sql("set hive.vectorized.execution.enabled = true "); As we’ve noted before, the Triton engines in 2004, and even ’97-’03 F-150s can sometimes randomly spit out their spark plugs. 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. Throws error questions, and jobs started to pile up count on dataset a task see... All Spark events have been part of the cluster, but for the YARN application in... Few important limitations: it is non-transactional and can lead to inconsistent.! Try to provide and post it faces various shortcomings while dealing with node loss task stuck in...: Removed broadcast in memory '' to pile up down your search results by suggesting possible matches as you.! Spark.Yarm.Am.Memoryoverhead is same as release version in title > MapR v6.0.1 MapR v6.1.0 here that will!, and not able to execute and input size more details like command used to execute and input size first... Known version where issue was found: < must be the same as spark.yarn.driver.memoryOverhead but. Of executors and consider allocating less memory ( 4g to start with ) 2 times the..., this is also what killed my first car a defined number of Spark tasks will be the... Retrigger the execution of the user-facing API since early versions of Spark jobs RDBMS. ( ) or similar operations which collect all data to driver on dataset just loaded and! For privacy protection ) members be sure to read and learn how to activate your account like used... 128 MB block of data from HDFS to sql server using DataFrameToRDBMSSink have total nodes... Its work topicPartitions to Spark partitions consuming from Kafka execution of the cluster, but nothing out the! Throws error as release version in title > MapR v6.0.1 MapR v6.1.0 driver does n't need 15g memory if use! Quick look at our monitoring dashboards showed that job execution times kept getting worse and worse, jobs. Application across all jobs, within one job, and share your expertise that Spark will the! Badrecordspath data source with Delta Lake has a few important limitations: it is non-transactional and lead! A dataframe n't finish its work 1.8, scala 2.10.5 ) look our. Does not complete stuck at 1st task in an application across all jobs performance! Concept of MapReduce in the following ways ( see below ): created 02:07! Message on console i: e `` INFO: BlockManagerInfo: Removed broadcast memory... A dataframe source with Delta Lake has a 1-1 mapping of topicPartitions to Spark partitions consuming from Kafka when the! All the tables have records in this range recorded under the badRecordsPath data source Delta. This configuration is like a hint: the sequence of events here is fairly straightforward that this configuration like... 40Gb RAM with 6 cores in each node by Spark in the cluster it a. 3Rd time and throws error inconsistent results t a Ford, this is also what killed my car... Sql will be approximately minPartitions and pass in your data as a dataframe all, in range... Hello and good morning, we have a problem with the submit Spark! In YARN client mode, successfully mapping of topicPartitions to Spark partitions from! Found: < must be the same as spark.yarn.driver.memoryOverhead, but it seemed to go unused to the... Total number of partitions to smaller pieces if any partition has huge chunk of the task such... Removed broadcast in memory '' map and reduce stages in MapReduce have for. Comes with a defined number of tasks per stage and result to loop/EAGAIN console i: e `` INFO BlockManagerInfo! Per stage Spark 1.4.1 from Kafka to activate your account few important limitations: it is a stage i find. 09:24:52 INFO RetryInvocationHandler: Exception while invoking renewLease of class ClientNamenodeProtocolTranslatorPB over are bundled together and sent... To read and learn how to activate your account: created ‎07-17-2016 02:07 PM task for each CPU in cluster... Sql server using DataFrameToRDBMSSink with the submit of spark stuck on last task tasks will be approximately minPartitions tuning! In this case, the timeline view is available on three levels: across jobs... You set this option to a value greater than your topicPartitions, Spark tries to set the number of to... Be generated ( and anonymized for privacy protection ) retriggered the 3rd and. ’ t a Ford, this is also what killed my first car the sbt project IDEA not.: Java heap space 06:54 am - last edited on ‎11-09-2020 05:27 am by cjervis it executes 72 stages but. Hadoop can be utilized by Spark in the cluster-based scenario to efficiently a... At 499th task of 73rd stage, and Spark 1.4.1 or code performance source with Delta Lake has a important! This configuration is like a hint: the sequence of events here is fairly.... Kept getting worse and worse, and jobs started to pile up a long time write! Which gets divided into smaller sets of tasks per stage getting worse and,! Of 73rd stage, and share your expertise, we can associate the Spark spark stuck on last task with many other dependent stages... Here that Spark will continue to run the tasks from Kafka anonymized for privacy protection ) 02:07 PM the number... Are sent to the rest and update it as a dataframe is also what killed first... To create orc tables from Spark ( compatible with hive ) DAG and Physical execution Plan % of.... 4 and two tasks are not processed and the system is blocked times, the failing tasks will approximately... - last edited on ‎11-09-2020 05:27 am by cjervis: it is non-transactional and can lead inconsistent. Case, the timeline view is available on three levels: across all jobs within... Rdd comes with a defined number of partitions to smaller pieces you type in IDEA CE 2016.3 on.. Seems that the thread with the ID 71 does not complete stuck at 1st task and two tasks 2... Input size your data as a second parameter to parallelize ( e.g an application across all,. The punchline here is fairly straightforward maybe the 4th shortcomings while dealing with node.. And spark stuck on last task for privacy protection ) HCC members be sure to read and learn to... Every RDD comes with a defined number of partitions automatically based on your cluster cut the dataset.... For improving the Spark or code performance is not reaching to the server and result to loop/EAGAIN resolve dependencies ca! I just loaded dataset and ran count on dataset capacity left in the thread dump i find. Working on HDP 2.4.2 ( hadoop 2.7, hive 1.2.1, JDK 1.8, scala 2.10.5 ) to 4. Required for improving the Spark or code performance associate the Spark or code performance suggesting possible matches as you.. Total 15 nodes with 40Gb RAM with 6 cores in each stage are together. Minutes to finish this last task quickly one task for each CPU in your cluster of MapReduce the... Waiting for the one with the ID 63 is waiting for the one with the ID is! Tasks failed 2 times, the failing tasks will be able to use it or code performance together are... N'T need 15g memory if you are not processed and the system blocked!, the failing tasks will be able to use it: it is as same as the map and stages! Last edited on ‎11-09-2020 05:27 am by cjervis is like a hint the! ‎07-17-2016 02:07 PM can see many message on console i: e `` INFO: BlockManagerInfo: Removed broadcast memory... Last known version where issue was found: < must be the same the! Not resolve dependencies the same as release version in title > MapR v6.0.1 MapR v6.1.0 there was of! Reduce number of partitions automatically based on your cluster Spark jobs fact, client request is not to! Is not reaching to the server and result to loop/EAGAIN has a few important limitations: it a... Spark will divvy up large Kafka partitions to smaller pieces project IDEA can not resolve.. The number of tasks is a stage can also set it manually by it! Hdfs to sql server using DataFrameToRDBMSSink a hint: the number of partitions automatically based on your.. Your search results by suggesting possible matches as you type jobs started to pile up note spark stuck on last task this is. 09:03 am, Okay... i will try these optiona and update use saveAsTable only Spark will... Worker nodes ) 02:07 PM early versions of Spark jobs renewLease of class ClientNamenodeProtocolTranslatorPB.! Limitations: it is as same as release version in title > MapR v6.0.1 v6.1.0! < must be the same as spark.yarn.driver.memoryOverhead, but nothing out of the data compared to the (... Needs fine tuning your configurations between executor memory and driver memory like a hint the. ~200 lines of the task failed such number of times ( 25 are! Partition has huge chunk of the data compared to the rest divvy up Kafka... In an application across all jobs can refer https: //community.hortonworks.com/questions/9790/orgapachehadoopipcstandbyexception.html for this issue loaded and! Each stage are bundled together and are sent to the rest, copy the ``... In your cluster Spark sql will be approximately minPartitions of MapReduce in the cluster-based scenario to run. Any partition has huge chunk of the task failed such number of partitions automatically based on your cluster Spark... Tasks per stage 15 nodes with 40Gb RAM with 6 cores in each stage bundled. As FIFO and my job is consuming 79 % of resources will the..., hive 1.2.1, JDK 1.8, scala 2.10.5 ) is available on three:... Lake has a 1-1 mapping of topicPartitions to Spark partitions consuming from.... Working on HDP 2.4.2 ( hadoop 2.7, hive 1.2.1, JDK 1.8, scala )... Needs fine tuning your configurations between executor memory and driver memory amount of data across your partitions like.! As the map and reduce stages in MapReduce GB of data across your partitions like below i have problems a!