@LPottakkattil  Do the following  to resolve that problem Clean out all packages and meta data from cache # Update the system metadata yum clean all yum check-update # Update the system yum update # update the Java connect you don't need to stop Ambari yum install -y mysql-connector-java (Optional) ambari-server setup --jdbc-db=mysql --jdbc-driver=/usr/share/java/mysql-connector-java.jar   Hope that helps     ... View more

@Seeker90  Zookeepers by default are supposed to be running in an ensemble [french together] thats a quorum in English. Minimum number of servers required to run the Zookeeper is called   Quorum. Zookeeper replicates whole data tree to all the quorum servers. This number is also the minimum number of servers required to store a client’s data before telling the client it is safely stored. Quorum size should be calculated by Majority Rule   Majority rule: QN = (N + 1) / 2   QN : Minimum number of servers in quorum N : Total number of servers. (should be an odd number) So, if we have 5 servers, then Quorum should be minimum of 3 servers. As long as a majority of the ensemble are up, the service will be available. Because Zookeeper requires a majority, it is best to use an odd number of machines  ZooKeeper uses Quorums by default to prevent the "brain split" phenomenon. That is, only more than half of the nodes in the cluster can vote for the Leader. This way can ensure the uniqueness of the leader,  Split-Brain is like when you have two nodes in your cluster, they both know that a master needs to be elected in this cluster. Then when there is no problem in the communication between the two of them, a consensus will be reached and one of them will be selected as the master. But if there is a problem with the communication between them, both nodes will feel that there is no master now, so each elects itself as the master, so there will be two masters in the cluster.   So to avoid the current problem you are facing add 2 more Zk'ers and that problem will vanish Happy hadooping    ... View more

@vidanimegh  Ye that s the usual format but I didn't want to complicate the  explanation  the alias is optionaél anyways. But you see the logic the 2 hosts must be pingable for  eachother.   Happy hadooping ... View more

@vidanimegh  What is the /etc/hosts entry  remember both hosts  /etc/hosts files should have the same entries?  On Source <source-nameservice> IP <destination-nameservice IP On destination <source-nameservice> IP <destination-nameservice IP   Please let me know      ... View more

@vidanimegh  Can you try it differently like below and let me know?   hadoop distcp -D ipc.client.fallback-to-simple-auth-allowed=true hdfs://<source-nameservice>/path/to/source/folder hdfs://<destination-nameservice>/path/to/destination/folder     Always try to use nameservice !   ... View more

@TDStephanieSoft  I am highly suspecting you download the  HDF 3.1.1 instead of HDP 3.0.1  because the links are on the same page     Please verify that you are really trying to access HDP. Can you double check the ova file you downloaded? if you see 3.1.1 then you downloaded HDF aka Nifi Hope that helps   ... View more

@shaz11    Can you share the screenshot of your policy in Ranger ? Once you enable Ranger plugin then the authorization is automatically delegated to Ranger so for any user to access the hive tables the permissions should be explicitly given from Ranger!   Hope that helps! ... View more

@swapko  That's bizarre can you share your step? and source of download? ... View more

@emeric  https://www.linkedin.com/in/sheltong/   ... View more

@emeric  Same name and photo on linkedin profile.  ... View more

@emeric  Cool so how will you send it safely?  check my linkedin should be easy to connect 🙂     ... View more

@emeric  Twitter has made it di^fficult to register any app, so I am waiting for approval. Sincerely I couldn't stand the 200-word essay to explain what I intend to do whether I am part of a govt etc bla  bla bla. I just copied some text from the website and paste from some website I hope I pass the review 🙂 By Friday I should be good to go  ... View more

@emeric  Could you try substituting the current values with the below flume.conf hdfs://10.0.2.15:8020/user/flume/tweets/ hdfs://127.0.0.1:8020/user/flume/tweets/ hdfs://192.168.56.101:8020/user/flume/tweets/ Let me know      ... View more

@emeric  what is the output from the Quickstart sandbox CLI of the below command? $ ifconfig I am thinking we are on the right path. I will download a sandbox tomorrow if you don't success and try to reproduce your situation. Happy hadooping       ... View more

@emeric  That looks a hostname issue this looks like the offending line   TwitterAgent.sinks.HDFS.hdfs.path = hdfs://quickstart.cloudera:8020/user/flume/tweets/   Can you replace the quickstart.cloudera:8020/user/flume/tweets/ with <Sandbox-IP>:8020/user/flume/tweets/ Please let me know  ... View more

@ryu  try the following solution. Always note all the changes you make incase you will need to revert  Follow these steps to resolve the issue:  1. Open Ambari. 2. Go to TEZ / Configs / Advanced tez-site. 3. Locate the configuration   tez.history.logging.service.class. 4. Replace the value   org.apache.tez.dag.history.logging.ats.ATSV15HistoryLoggingService with the new value:   org.apache.tez.dag.history.logging.ats.ATSHistoryLoggingService" 5. Save the configuration changes. 6. Restart all services that Ambari is asking you to restart. Then retry the    [root@test02 ~]# hive   Please revert    ... View more

@totti1  This all about the HMS  hive  Metadata Refreshing  Spark SQL caches Parquet metadata for better performance. When Hive metastore Parquet table conversion is enabled, metadata of those converted tables are also cached. If these tables are updated by Hive or other external tools, you need to refresh them manually to ensure consistent metadata.   from os.path import expanduser, join from pyspark.sql import SparkSession from pyspark.sql import Row # warehouse_location points to the default location for managed databases and tables warehouse_location = 'spark-warehouse' spark = SparkSession \ .builder \ .appName("Python Spark SQL Hive integration example") \ .config("spark.sql.warehouse.dir", warehouse_location) \ .enableHiveSupport() \ .getOrCreate() # spark is an existing SparkSession spark.sql("CREATE TABLE IF NOT EXISTS totti (key INT, value STRING)") # Load some data here spark.sql("LOAD DATA LOCAL INPATH 'path/to/the/table/totti.txt' INTO TABLE totti") # Refresh the HMS metastore // spark is an existing SparkSession spark.catalog.refreshTable("totti") # Queries are expressed in HiveQL spark.sql("SELECT * FROM totti").show()   In the above example, you will need to connect to the database to create the table totti. Notice I run the refresh before the select so that the Metadata is invalidated and fetched from the databases else I will get no  table found etc   ... View more

@emeric  Can you copy and paste the new flume. conf for clarity I have split the different parts  Flow Diagram   Configuring the flume.conf   # Naming the components on the current agent. TwitterAgent.sources = Twitter # Added TwitterAgent.channels = MemChannel TwitterAgent.sinks = HDFS # Configuring the source TwitterAgent.sources.Twitter.type = org.apache.flume.source.twitter.TwitterSource TwitterAgent.sources.Twitter.consumerKey = <consumerKey> TwitterAgent.sources.Twitter.consumerSecret = <consumerSecret> TwitterAgent.sources.Twitter.accessToken = <accessToken> TwitterAgent.sources.Twitter.accessTokenSecret = <accessTokenSecret> TwitterAgent.sources.Twitter.keywords = <keyword> # Configuring the sink TwitterAgent.sinks.HDFS.type = hdfs TwitterAgent.sinks.HDFS.hdfs.path = hdfs://quickstart.cloudera:8020/user/flume/tweets/ TwitterAgent.sinks.HDFS.hdfs.fileType = DataStream TwitterAgent.sinks.HDFS.hdfs.writeFormat = text TwitterAgent.sinks.HDFS.hdfs.batchSize = 1000 TwitterAgent.sinks.HDFS.hdfs.rollSize = 0 TwitterAgent.sinks.HDFS.hdfs.rollCount = 10000 TwitterAgent.sinks.HDFS.hdfs.rollInterval = 600 # Configuring the channel TwitterAgent.channels.MemChannel.type = memory TwitterAgent.channels.MemChannel.capacity = 10000 TwitterAgent.channels.MemChannel.transitionCapacity = 100 # Binding the source and sink to the channel TwitterAgent.sources.Twitter.channels = MemChannel TwitterAgent.sinks.HDFS.channel = MemChannel $ bin/flume-ng agent --conf ./conf/ -f /home/cloudera/flume.conf -n TwitterAgent   Please let me know if it runs  successfully         ... View more

@sandipkumar  Think about it Impala uses HMS so remember that the  Hive metastore  database is required for  Impala  to  function. So if HMS is not running then no Impala query/job  should be launched.   Hope that helps     ... View more

@ryu  How is your cluster setup?  The number of nodes and the HDP versions? Are you running your HQL from the edge node?   Give as much information as possible.     ... View more

@Jay2021  Impala and hive share metadata catalog ie Hive MetaStore , when a database/table is created in HIVE it's readily available for hive users but not Impala! To successfully query a table or database created in HIVE there is a caveat you need to run the INVALIDATE METADATA from the impala-shell before the table is available for Impala queries. INVALIDATE METADATA reloads all the metadata for the table needed for a subsequent query  The next time the current Impala node performs a query against a table whose metadata is invalidated you definitely will run into errors you could use the REFRESH in the common case where you add new data files for an existing table it reloads the metadata immediately, but only loads the block location data for newly added data files, making it a less expensive operation overall. INVALIDATE METADATA [[db_name.]table_name] Example   $ impala-shell > INVALIDATE METADATA new_db_from_hive.new_table_from_hive; > SHOW TABLES IN new_db_from_hive; +---------------------+ | new_table_from_hive | +---------------------+   That should resolve your issue Happy hadooping ... View more

@SnehasishRSC  REFRESH in the common case where you add new data files for an existing table it reloads the metadata immediately, but only loads the block location data for newly added data files, making it a less expensive operation overall. It is recommended to run COMPUTE STATS when 30 % of data is altered in a table, where altered means the addition or deletion of files/data. INVALIDATE METADATA is a relatively expensive operation compared to the incremental metadata update done by the REFRESH statement, so in the common scenario of adding new data files to an existing table, prefer REFRESH rather than INVALIDATE METADATA which marks the metadata for one or all tables as stale. The next time the Impala service performs a query against a table whose metadata is invalidated, Impala reloads the associated metadata before the query proceed. Hope that helps ... View more

@rohit_sharma  Any updates? Do you still need help? ... View more

@raghurok  Bad news    As of February 1, 2021, all downloads of CDH and Cloudera Manager require a username and password and use a modified URL. You must use the modified URL, including the username and password when downloading the cloudera repository contents    Hope that helps ... View more

@Alex_IT  From my Oracle knowledge, there are 2 options for migrating the same Oracle_home [DB] from 12C to 19C  if you are running 12.1.0.2 then you have the direct path see the attached matrix. With this option, you won't need to change the hostname.  The other option is to export your current schema CM ,oozie,hive,hue,Ranger etc schemas   install a fresh Oracle 19c box with an empty database, and import the old schemas this could be a challenge as you might have to rebuild indexes or recompile some database packages etc but bot are doable. Hope that helps ... View more

@rohit_sharma    Can you change your syntax as below, note the zookeeper ensemble /bin/kafka-topics.sh --create \ --zookeeper zk1:2181,zk2:2181,zk3:2181 \ --topic "topic_name" \ --partitions 1> \ --replication-factor 2 Hope that helps   ... View more

@zetta4ever  In a Hadoop cluster, three types of nodes exist Master, Worker and edge nodes. The distinction of roles helps maintain efficiency. Master nodes control which nodes perform which tasks and what processes run on what nodes. The majority of work is assigned to worker nodes. Worker node store most of the data and perform most of the calculations Edge nodes aka gateway facilitate communications from end users to master and worker nodes. The 3 masternodes should have the Namenode[Active & Standby],YARN [Active & Standby], Zookeeper Quorum [3 masters] and the other component you intend to install and on the 6 worker node aka slave nodes you will install the Nodemanager,Datanodes and the all the clients. There is no need to install the client on the master nodes, Some nodes have important tasks, which may impact performance if interrupted. Edge nodes allow end-users to contact worker nodes when necessary, providing a network interface for the cluster without leaving the entire cluster open to communication. That limitation improves reliability and security. As work is evenly distributed between work nodes, the edge node’s role helps avoid data skewing and performance issues. See my document on edge node https://community.cloudera.com/t5/Support-Questions/Edge-node-or-utility-node-packages/td-p/202164# Hope that helps ... View more