@vamsi krishna , can you please check if resource manager is up ? ... View more

@Alaa Nabil , The stack trace posted above is related to Nodemanager trying to recover a container. Do you know what is this application (application_1536072828773_1909) and what was the status of this application ? ... View more

Are you running Distributed shell application ? Default client timeout for Dshell is 600 secs. You can extend client timeout using "-timeout <milliseconds>" in application launch command. ... View more

@kanna k , you can find out location of standby namenode logs from hadoop-env.sh. Look for HADOOP_LOG_DIR value to find out correct location of the log . Example: export HADOOP_LOG_DIR=/var/log/hadoop/$USER In this example, standby namenode log will be present at /var/log/hadoop/hdfs dir. ... View more

@kanna k, you can find out location of standby namenode logs from hadoop-env.sh. Look for HADOOP_LOG_DIR value to find out correct location of the log . Example: export HADOOP_LOG_DIR=/var/log/hadoop/$USER In this example, standby namenode log will be present at /var/log/hadoop/hdfs dir. ... View more

@Moti Ben Ivgi, Please look at below thread, you may be hitting issue with host setup. https://community.hortonworks.com/questions/26802/data-node-process-not-starting-up.html ... View more

@Dukool Sharma, Reduce only job is not possible with Mapreduce. Reducer requires intermediate data in the form of key-pair value from mapper. Thus its not possible to run just reducers without mappers. ... View more

@Anurag Mishra, Unfortunately there is no yarn cli / rest api available to validate user-queue mapping without launching application. ... View more

@Renuka Peshwani, Spark is a data processing engine to develop faster and ease of use analytics. Spark ecosystem consist of below components. Spark Core Spark Core is the underlying general execution engine for the Spark platform that all other functionality is built on top of. It provides in-memory computing capabilities to deliver speed, a generalized execution model to support a wide variety of applications, and Java, Scala, and Python APIs for ease of development. Spark SQL / Dataframe Many data scientists, analysts, and general business intelligence users rely on interactive SQL queries for exploring data. Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrames and can also act as distributed SQL query engine. It enables unmodified Hadoop Hive queries to run up to 100x faster on existing deployments and data. It also provides powerful integration with the rest of the Spark ecosystem (e.g., integrating SQL query processing with machine learning). Spark Streaming Many applications need the ability to process and analyze not only batch data, but also streams of new data in real-time. Running on top of Spark, Spark Streaming enables powerful interactive and analytical applications across both streaming and historical data, while inheriting Spark’s ease of use and fault tolerance characteristics. It readily integrates with a wide variety of popular data sources, including HDFS, Flume, Kafka, and Twitter. Spark Machine Learning Machine learning is critical piece in mining Big Data for actionable insights. Built on top of Spark, MLlib is a scalable machine learning library that delivers both high-quality algorithms (e.g., multiple iterations to increase accuracy) and blazing speed (up to 100x faster than MapReduce). The library is usable in Java, Scala, and Python as part of Spark applications, so that you can include it in complete workflows. GraphX GraphX is a graph computation engine built on top of Spark that enables users to interactively build, transform and reason about graph structured data at scale. It comes complete with a library of common algorithms. ... View more

@Pritam Pal, Big data is a concept which can handle a large amount of data. However, Hadoop is an open source framework which can be used for distributed storage and processing of datasets of big data. Here's a good link which elaborates the difference between big data and Hadoop nicely https://www.quora.com/What-is-the-difference-between-big-data-and-Hadoop ... View more

@Pritam Pal , Combiner is used to summarize map output records with same key. This process reduces the data transfer across network between mapper and reducer. Hadoop doesn’t guarantee on how many times a combiner function will be called for each map output key. At times, it may not be executed at all, while at times it may be used once, twice, or more times depending on the size and number of output files generated by the mapper for each reducer. https://www.tutorialspoint.com/map_reduce/map_reduce_combiners.htm ... View more

@Pritam Pal, Hadoop is a combination of HDFS ( data storage), YARN (app execution framework) And Mapreduce ( data processing engine). Thus, it is not fair to compare Hadoop and Spark. Mapreduce and Spark can be comparable because both of them are data processing engine. Here is a good link which compares Mapreduce and Spark in detail https://www.xplenty.com/blog/apache-spark-vs-hadoop-mapreduce/ ... View more

@Nayeem Janmohamed, It seems that datanodes in this cluster are not in good state. Can you please restart datanodes and restart History Server ? ... View more

@AA MJ, How many datanodes do you have in cluster ? As per message , it seams you only have one datanode. Ideally, you should have 3 datanodes atleast to manage replication correctly. However, as per this message it means that you have only one data node and that data node is excluded. Can you please check content of /etc/hadoop/conf/dfs.hosts.exclude ? If the datanode is added to exclude host list, this behavior is expected. If data node is not added to exclude list and still you are noticing issue with replication, you can restart the datanode and try again. ... View more

@kotesh banoth, you can setup user-queue mapping in capacity scheduler. This will restrict access to the queue as per users. Once this mapping is set, you can set priority to respective queue. As per above mentioned case, the queue related to user1 should have highest priority, this way application started in this queue will execute first. user-queue mapping: http://tamastarjanyi.blogspot.com/2015/01/user-based-queue-mapping-for-capacity.html Queue priority setting: https://docs.hortonworks.com/HDPDocuments/Ambari-2.6.0.0/bk_ambari-views/content/setting_yarn_queue_priorities.html ... View more

@Amod Kulkarni , this issue should be related to mismatch in scala version. Few relevant links: https://stackoverflow.com/questions/25089852/what-is-the-reason-for-java-lang-nosuchmethoderror-scala-predef-arrowassoc-upo https://issues.apache.org/jira/browse/SPARK-5483 ... View more

@Fernando Lopez Bello , you can use Yarn Node label feature to achieve your goals. 1) add a node label such as "spark nodes" on hosts where you want keep spark application running. 2) Map "spark nodes" node label with a Yarn Queue such as "SparkQueue". 3) Run your Spark applications from "SparkQueue". This way you can ensure that Spark application will run on specific hosts you want. Find few useful links for Node labels feature as below. https://hadoop.apache.org/docs/r2.7.3/hadoop-yarn/hadoop-yarn-site/NodeLabel.html https://community.hortonworks.com/articles/72450/node-labels-configuration-on-yarn.html ... View more

@yassine sihi, there are two different concepts. HDFS can be deployed in two modes. 1) Without HA 2) With HA. In without HA mode, HDFS will have Namenode and Secondary Namenode. Here, secondary namenode periodically take snapshot of namenode and keep the metadata and audit logs up to date. So in case of Namenode failure, Secondary Namenode will have copy of latest namenode activity and prevent data loss. In HA mode, HDFS have two set of Namenodes. One acts as active namenode and another acts as Standby Namenode. The duties of standby namenode is similar to Secondary namenode where it keeps the track of active namenode activity and take snapshot periodically. Here, in case of active namenode failure, standby namenode automatically takes the control and becomes active. This way user will not notice the failure in namenode. This way High availability is guaranteed. ... View more

@raouia , you can check application status using yarn ui or yarn cli. Run "yarn application -status application_1507646811049_0004" to get status of the application. If application is finished, you can use "yarn logs -applicationId application_1507646811049_0004" cmd to get logs for this application. ... View more

It looks like start operation is trying to create /hdp/apps/2.6.2.0-205/mapreduce/mapreduce.tar.gz. Can you please check if /hdp/apps/2.6.2.0-205/mapreduce/mapreduce.tar.gz is present on HDFS ? Ideally mapreduce tar ball should already be present on hdfs. ... View more

@Roberto Sancho , currently yarn does not have feature to time out the application. YARN-3813 is apache jira tracking this feature. For now. you will need to write a long running script, which can monitor each application's state and kill if it stays in accepted state for long time. ... View more