@Vinay1991  From the logs, I see connectivity loss and that's precisely what's causing the NN switch. Remember in my earlier posting the importance of  Zk quorum! Your NN and losing Connection to the ZK  so the NN that loses active connection is  causing the ZK to elect a new leader and that's happening in a loop   Caused by : java.net.SocketTimeoutException: 5000 millis timeout while waiting for channel to be ready for read   I would start by checking FW  I see you are on Ubuntu so ensure the FW is disabled across the Cluster. Identifying and Fixing Socket Timeouts The root cause of a Socket Timeout is a connectivity failure between the machines, so try the usual process Check the settings: is this the machine you really wanted to talk to? From the machine that is raising the exception, can you resolve the hostname? Is that resolved hostname the correct one? Can you ping the remote host? Is the target machine running the relevant Hadoop processes? Can you telnet to the target host and port? Can you telnet to the target host and port from any other machine? On the target machine, can you telnet to the port using localhost as the hostname? If this works but external network connections time out, it's usually a firewall issue. If it is a remote object store: is the address correct? Does it go away when you repeat the operation? Does it only happen on bulk operations? If the latter, it's probably due to throttling at the far end.   Check your hostname resolution DNS or /etc/hosts should be in sync and another important thing is all your host time should be in sync. can you share the value of Core-site.xml parameter  ha.zookeeper.quorum ... View more

@Vinay1991  Unfortunately, you haven't described your cluster setup but my assumption is that you have 3 Zk's in your HA implementation. There are two components deployed to Hadoop HDFS for implementing Automatic Failover.   These two components are- ZKFailoverController process(ZKFC) ZooKeeper quorum (3 Zk's)   1. ZKFailoverController(ZKFC) The ZKFC is the ZooKeeper client, who is also responsible for managing and monitoring the NameNode state. ZKFC is a client that runs on all nodes on the Hadoop cluster, which is running NameNode.   These 2 components are responsible for: Health monitoring ZKFC is accountable for health monitoring heart beating the NameNode with health-check commands periodically. As long as the NameNode responds with a healthy status timely, it considers the NameNode as healthy. In this case, if the NameNode got crashed, froze, or entered an unhealthy state, then it marks the NameNode as unhealthy. ZooKeeper session management It is also responsible for the session management with ZooKeeper. The ZKFC maintains a session open in the ZooKeeper when the local Namenode is healthy. Also, if the Local NameNode is the active NameNode, then with the session, it also holds a special lock “znode”. This lock uses ZooKeeper support for the ”ephemeral” nodes. Thus, if the session gets expires, the lock node will be deleted automatically. ZooKeeper-based election When the local Namenode is healthy and ZKFC finds that no other NameNode acquires the lock “znode”, then it will try by itself to acquire the lock. If it gets successful in obtaining the lock, then ZKFC has won the election, and now it is responsible for running the failover to make its local NameNode active. The failover process run by the ZKFC is similar to the failover process run by the manual failover described in the NameNode High Availability article. 2. ZooKeeper quorum A ZK quorum is a highly available service for maintaining little amounts of coordination data. It notifies the clients about the changes in that data. It monitors clients for the failures. The HDFS implementation of automatic failover depends on ZooKeeper for the following things: How does it detect NN Failure each NameNode machine in the Hadoop cluster maintains a persistent session in the ZooKeeper. If any of the machines crashes, then the ZooKeeper session maintained will get expire—zooKeeper than reveal to all the other NameNodes to start the failover process.   To exclusively select the active NameNode, ZooKeeper provides a simple mechanism. In the case of active NameNode failure, another standby NameNode may take the special exclusive lock in the ZooKeeper, stating that it should become the next active NameNode.   After the initialization of Health Monitor is completed, internal threads are started to call the method corresponding to the HASERVICE Protocol RPC interface of NameNode periodically to detect the health status of NameNode. If the Health Monitor detects a change in the health status of NameNode, it calls back the corresponding method registered by ZKFailover Controller for processing. If ZKFailover Controller decides that a primary-standby switch is needed, it will first use Active Standby Elector to conduct an automatic primary election. Active Standby Elector interacts with Zookeeper to complete an automatic backup election. Active Standby Elector calls back the corresponding method of ZKFailover Controller to notify the current NameNode to become the main NameNode or the standby NameNode after the primary election is completed. ZKFailover Controller calls the HASERVICE Protocol RPC interface corresponding to NameNode to convert NameNode to Active or Standby state. Taking all the above into account, the first component logs to check are Zk and NN,   /var/log/hadoop/hdfs /var/log/zookeeper   My suspicion is you have issues with the Namenode heartbeat which makes the zookeeper fail to get the pingback in time and marks the NN as dead and elects a new leader and that keeps happening in a loop. So check those ZK logs to ensure time is set correctly and is in sync! Please revert ... View more

@USMAN_HAIDER  There is this step below did you perform that? Kerberos must be specified as the security mechanism for Hadoop infrastructure, starting with the HDFS service. Enable Cloudera Manager Server security for the cluster on an HDFS service. After you do so, the Cloudera Manager Server automatically enables Hadoop security on the MapReduce and YARN services associated with that HDFS service. In the Cloudera Manager Admin Console: Select Clusters > HDFS-n. 1.Click the Configuration tab. 2.Select HDFS-n for the Scope filter. 3.Select Security for the Category filter. 4.Scroll (or search) to find the Hadoop Secure Authentication property. 5.Click the Kerberos button to select Kerberos: Please revert             ... View more

@mike_bronson7    Are you using the default capacity schedule settings? No queues/leafs created?  Is what you shared the current seeting? ... View more

@srinivasp    I am wondering whether your Ranger policies are also in place.  Please explicitly give the correct permissions to the group/user in Ranger as the beeline authorization depends now on Ranger 🙂     Happy hadooping.  ... View more

@SparkNewbie  Bingo you are using the derby DB, which is only recommended for testing.   There are three modes for Hive Metastore deployment: Embedded Metastore Local Metastore Remote Metastore In   Hive   by default, metastore service runs in the same JVM as the Hive service. It uses embedded   derby   database stored on the local file system in this mode. Thus both metastore service and hive service runs in the same JVM by using embedded Derby Database. But, this mode also has its limitation that, as only one embedded Derby database can access the database files on disk at any one time, so only one Hive session could be open at a time. 21/07/07 23:07:56 INFO MetaStoreDirectSql: Using direct SQL, underlying DB is DERBY Derby is an embedded relational database in Java program and used for online transaction processing and has a 3.5 MB disk-space footprint. Depending on your software HDP or Cloudera ensure the hive DB is plugged to an external Mysql database For CDH using Mysql  For HDP using mysql  Check your current hive UI backend metadata databases !! After installing MySQL then you should toggle hive config to point to the external Mysql database .. Once done your commands and the refresh should succeed Please let me know if you need help Happy hadooping   ... View more

@SparkNewbie  Can you add the redcline between your 2 commands spark.sql("create table test1_0522 location 's3a://<test-bucket>/data/test1_0522' stored as PARQUET as select * from my_temp_table") REFRESH TABLE test1_0522; spark.sql("SHOW TABLES").show()   That should resolve the problem. Happy hadooping ... View more

@t1  I tried out the  sqoop list-databases  and my output looks correct  [root@bern ~]# sqoop list-databases \ > --connect jdbc:mysql://localhost:3306/ \ > --username root \ > --password welcome ......... SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory] 21/07/05 17:28:04 INFO sqoop.Sqoop: Running Sqoop version: 1.4.7.3.1.4.0-315 21/07/05 17:28:05 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead. 21/07/05 17:28:05 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset. information_schema ambari druid harsh8 hive mysql oozie performance_schema ranger rangerkms superset Then I run exactly the same  sqoop import  and it succeeded but I think its the underlying table format [hdfs@bern ~]$ sqoop import --connect jdbc:mysql://localhost:3306/harsh8 --username root --table staff2 --hive-import --fields-terminated-by "," --hive-import --create-hive-table --hive-table staff2_backup --m 1 SLF4J: Class path contains multiple SLF4J bindings. ..... SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory] 21/07/05 18:36:40 INFO sqoop.Sqoop: Running Sqoop version: 1.4.7.3.1.4.0-315 21/07/05 18:36:41 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset. 21/07/05 18:36:41 INFO tool.CodeGenTool: Beginning code generation 21/07/05 18:36:43 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `staff2` AS t LIMIT 1 21/07/05 18:36:43 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `staff2` AS t LIMIT 1 21/07/05 18:36:43 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /usr/hdp/3.1.4.0-315/hadoop-mapreduce 21/07/05 18:36:48 WARN orm.CompilationManager: Could not rename /tmp/sqoop-hdfs/compile/358a7be0c1aae1ac531284e68ae3679e/staff2.java to /home/hdfs/./staff2.java. Error: Destination '/home/hdfs/./staff2.java' already exists 21/07/05 18:36:48 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hdfs/compile/358a7be0c1aae1ac531284e68ae3679e/staff2.jar 21/07/05 18:36:49 WARN manager.MySQLManager: It looks like you are importing from mysql. 21/07/05 18:36:49 WARN manager.MySQLManager: This transfer can be faster! Use the --direct 21/07/05 18:36:49 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path. 21/07/05 18:36:49 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql) 21/07/05 18:36:49 INFO mapreduce.ImportJobBase: Beginning import of staff2 21/07/05 18:36:59 INFO client.RMProxy: Connecting to ResourceManager at bern.swiss.ch/192.168.0.139:8050 21/07/05 18:37:09 INFO client.AHSProxy: Connecting to Application History server at bern.swiss.ch/192.168.0.139:10200 21/07/05 18:38:28 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /user/hdfs/.staging/job_1625500722080_0001 21/07/05 18:40:05 INFO db.DBInputFormat: Using read commited transaction isolation 21/07/05 18:40:23 INFO mapreduce.JobSubmitter: number of splits:1 21/07/05 18:40:32 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1625500722080_0001 21/07/05 18:40:32 INFO mapreduce.JobSubmitter: Executing with tokens: [] 21/07/05 18:40:34 INFO conf.Configuration: found resource resource-types.xml at file:/etc/hadoop/3.1.4.0-315/0/resource-types.xml 21/07/05 18:40:37 INFO impl.YarnClientImpl: Submitted application application_1625500722080_0001 21/07/05 18:40:37 INFO mapreduce.Job: The url to track the job: http://bern.swiss.ch:8088/proxy/application_1625500722080_0001/ 21/07/05 18:40:37 INFO mapreduce.Job: Running job: job_1625500722080_0001 21/07/05 18:46:55 INFO mapreduce.Job: Job job_1625500722080_0001 running in uber mode : false 21/07/05 18:46:55 INFO mapreduce.Job: map 0% reduce 0% 21/07/05 18:50:56 INFO mapreduce.Job: map 100% reduce 0% 21/07/05 18:51:09 INFO mapreduce.Job: Job job_1625500722080_0001 completed successfully 21/07/05 18:51:10 INFO mapreduce.Job: Counters: 32 File System Counters FILE: Number of bytes read=0 ............. HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Launched map tasks=1 Other local map tasks=1 Total time spent by all maps in occupied slots (ms)=385416 Total time spent by all reduces in occupied slots (ms)=0 Total time spent by all map tasks (ms)=192708 Total vcore-milliseconds taken by all map tasks=192708 Total megabyte-milliseconds taken by all map tasks=394665984 Map-Reduce Framework Map input records=6 Map output records=6 ............ Physical memory (bytes) snapshot=152813568 Virtual memory (bytes) snapshot=3237081088 Total committed heap usage (bytes)=81788928 Peak Map Physical memory (bytes)=152813568 Peak Map Virtual memory (bytes)=3237081088 File Input Format Counters Bytes Read=0 File Output Format Counters Bytes Written=223 21/07/05 18:51:10 INFO mapreduce.ImportJobBase: Transferred 223 bytes in 852.4312 seconds (0.2616 bytes/sec) 21/07/05 18:51:10 INFO mapreduce.ImportJobBase: Retrieved 6 records. 21/07/05 18:51:10 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `staff2` AS t LIMIT 1 21/07/05 18:51:10 INFO hive.HiveImport: Loading uploaded data into Hive My table structure MariaDB [harsh8]> describe staff2; +------------+-------------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +------------+-------------+------+-----+---------+-------+ | id | int(11) | NO | PRI | NULL | | | Name | varchar(20) | YES | | NULL | | | Position | varchar(20) | YES | | NULL | | | Salary | int(11) | YES | | NULL | | | Department | varchar(10) | YES | | NULL | | +------------+-------------+------+-----+---------+-------+   My test table contents  MariaDB [harsh8]> select * from staff2; +-----+------------+-------------------+--------+------------+ | id | Name | Position | Salary | Department | +-----+------------+-------------------+--------+------------+ | 100 | Geoffrey | manager | 50000 | Admin | | 101 | Thomas | Oracle Consultant | 15000 | IT | | 102 | Biden | Project Manager | 28000 | PM | | 103 | Carmicheal | Bigdata developer | 30000 | BDS | | 104 | Johnson | Treasurer | 21000 | Accounts | | 105 | Gerald | Director | 30000 | Management | +-----+------------+-------------------+--------+------------+ 6 rows in set (0.09 sec) This is how my comma-delimited [hdfs@bern ~]$ hdfs dfs -cat /tmp/sqoop/hr.txt 100,Geoffrey,manager,50000,Admin 101,Thomas,Oracle Consultant,15000,IT 102,Biden,Project Manager,28000,PM 103,Carmicheal,Bigdata developer,30000,BDS 104,Johnson,Treasurer,21000,Accounts 105,Gerald,Director,30000,Management 106,Paul,Director,30000,Management 105,Mark,CEO,90000,Management 105,Edward,Janitor,30000,Housing 105,Richard,Farm Manager,31000,Agriculture 105,Albert,System Engineer,21000,IT The dataset looks like the above is your format AVRO? Happy hadooping   ... View more

@t1  Is there a way I can re-create your tables?  I could try the same commands I also have MySQL/MariaDB. And keep you posted! ... View more

@t1  How is the root user authentication against the databases?  If username password then I don't see the prompt for the password. Can you run the below and re-share the out put   Added sqoop import \ --connect jdbc:mysql://localhost:3306/sample \ --username root -p \ --table test \ --hive-import \ --fields-terminated-by "," --hive-import --create-hive-table --hive-table sample.tesr --m 4 ... View more

@Guarupe  Can you share your steps, please? Are you suing HUE to run your cmds? Did you use the Impala editor? ... View more

@tomm  A quick thought can you check and share your /etc/hosts ... View more

@ask_bill_brooks  Thanks for the  addendum and official context. Happy hadooping ... View more

@t1  Can you share the whole stack command +plus output? ... View more

@Guarupe  I responded to a similar question Warm up Impala You will need to run the  INVALIDATE METADATA [[db_name.]table_name] The error is precise Impala uses the Hive Metastore  [HMS] to build efficient queries     CAUSED BY: MetaException: Column mycolumn doesn't exist in table mytable in database myschema     In your case INVALIDATE METADATA [[myschema.]mytable]  The INVALIDATE METADATA   is an asynchronous operation that simply discards the loaded metadata from the catalog and coordinator caches. After that operation, the catalog and all the Impala coordinators only know about the existence of databases and tables and nothing more. Metadata loading for tables is triggered by any subsequent queries.   After running this in the impala-shell you should compute statistics successfully Happy hadooping ... View more

@harsh8  Happy to help with that question. The simple answer is YES below I am demonstrating with the table staff created in the previous post! Before the import [hdfs@bern ~]$ hdfs dfs -ls /tmp Found 5 items drwxrwxr-x - druid hadoop 0 2020-07-06 02:04 /tmp/druid-indexing drwxr-xr-x - hdfs hdfs 0 2020-07-06 01:50 /tmp/entity-file-history drwx-wx-wx - hive hdfs 0 2020-07-06 01:59 /tmp/hive -rw-r--r-- 3 hdfs hdfs 1024 2020-07-06 01:57 /tmp/ida8c04300_date570620 drwxr-xr-x - hdfs hdfs 0 2021-06-29 10:14 /tmp/sqoop When you run the sqoop import to ensure the destination directory sqoop_harsh8 doesn't already exist in HDFS $ sqoop import --connect jdbc:mysql://localhost/harsh8 --table staff --username root -P --target-dir /tmp/sqoop_harsh8 -m 1 Here I am importing the table harsh8.staff I created in the previous session. The sqoop export will create 2 files _SUCCESS and part-m-0000 in the HDFS directory as shown below. After the export the directory /tmp/sqoop_harsh8 is newly created [hdfs@bern ~]$ hdfs dfs -ls /tmp Found 5 items drwxrwxr-x - druid hadoop 0 2020-07-06 02:04 /tmp/druid-indexing drwxr-xr-x - hdfs hdfs 0 2020-07-06 01:50 /tmp/entity-file-history drwx-wx-wx - hive hdfs 0 2020-07-06 01:59 /tmp/hive -rw-r--r-- 3 hdfs hdfs 1024 2020-07-06 01:57 /tmp/ida8c04300_date570620 drwxr-xr-x - hdfs hdfs 0 2021-06-29 10:14 /tmp/sqoop -rw-r--r-- 3 hdfs hdfs 0 2021-07-03 22:04 /tmp/sqoop_harsh8 Check the contents of /tmp/sqoop_harsh8 [hdfs@bern ~]$ hdfs dfs -ls /tmp/sqoop_harsh8 Found 2 items -rw-r--r-- 3 hdfs hdfs 0 2021-07-03 22:04 /tmp/sqoop_harsh8/_SUCCESS -rw-r--r-- 3 hdfs hdfs 223 2021-07-03 22:04 /tmp/sqoop_harsh8/part-m-00000 The _SUCCESS is just a log file so cat the contents of part-m-00000 this is the data from our table harsh8.staff [hdfs@bern ~]$ hdfs dfs -cat /tmp/sqoop_harsh8/part-m-00000 100,Geoffrey,manager,50000,Admin 101,Thomas,Oracle Consultant,15000,IT 102,Biden,Project Manager,28000,PM 103,Carmicheal,Bigdata developer,30000,BDS 104,Johnson,Treasurer,21000,Accounts 105,Gerald,Director,30000,Management I piped the contents to a text file hr2.txt in my local tmp directory to enable me run a sqoop import with an acceptable format [hdfs@bern ~]$ hdfs dfs -cat /tmp/sqoop_harsh8/part-m-00000 > /tmp/hr2.txt Validate the hr2.txt contents [hdfs@bern ~]$ cat /tmp/hr2.txt 100,Geoffrey,manager,50000,Admin 101,Thomas,Oracle Consultant,15000,IT 102,Biden,Project Manager,28000,PM 103,Carmicheal,Bigdata developer,30000,BDS 104,Johnson,Treasurer,21000,Accounts 105,Gerald,Director,30000,Management Copied the hr2.txt to HDFS and validated the file was copied [hdfs@bern ~]$ hdfs dfs -copyFromLocal /tmp/hr2.txt /tmp Validation [hdfs@bern ~]$ hdfs dfs -ls /tmp Found 7 items drwxrwxr-x - druid hadoop 0 2020-07-06 02:04 /tmp/druid-indexing drwxr-xr-x - hdfs hdfs 0 2020-07-06 01:50 /tmp/entity-file-history drwx-wx-wx - hive hdfs 0 2020-07-06 01:59 /tmp/hive -rw-r--r-- 3 hdfs hdfs 223 2021-07-03 22:41 /tmp/hr2.txt -rw-r--r-- 3 hdfs hdfs 1024 2020-07-06 01:57 /tmp/ida8c04300_date570620 drwxr-xr-x - hdfs hdfs 0 2021-06-29 10:14 /tmp/sqoop drwxr-xr-x - hdfs hdfs 0 2021-07-03 22:04 /tmp/sqoop_harsh8 Connected to MySQL and switch to harsh8 database [root@bern ~]# mysql -uroot -p Enter password: Welcome to the MariaDB monitor. Commands end with ; or \g. Your MariaDB connection id is 179 Server version: 5.5.65-MariaDB MariaDB Server Copyright (c) 2000, 2018, Oracle, MariaDB Corporation Ab and others. Type 'help;' or '\h' for help. Type '\c' to clear the current input statement. MariaDB [(none)]> use harsh8; Reading table information for completion of table and column names You can turn off this feature to get a quicker startup with -A Database changed Check the existing tables in the harsh8 database before the export MariaDB [harsh8]> show tables; +------------------+ | Tables_in_harsh8 | +------------------+ | staff | +------------------+ 1 row in set (0.00 sec) Pre-create a table staff2 to receive the hr2.txt data MariaDB [harsh8]> CREATE TABLE staff2 ( id INT NOT NULL PRIMARY KEY, Name VARCHAR(20), Position VARCHAR(20),Salary INT,Department VARCHAR(10)); Query OK, 0 rows affected (0.57 sec) MariaDB [harsh8]> show tables; +------------------+ | Tables_in_harsh8 | +------------------+ | staff | | staff2 | +------------------+ 2 rows in set (0.00 sec) Load data into staff2 from a sqoop export ! sqoop]$ sqoop export --connect jdbc:mysql://localhost/harsh8 --username root --password 'w3lc0m31' --table staff2 --export-dir /tmp/hr2.txt [hdfs@bern ~]$ sqoop export --connect jdbc:mysql://localhost/harsh8 --username root --password 'w3lc0m31' --table staff2 --export-dir /tmp/hr2.txt ... 21/07/03 22:44:36 INFO sqoop.Sqoop: Running Sqoop version: 1.4.7.3.1.4.0-315 21/07/03 22:44:37 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead. 21/07/03 22:44:37 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset. 21/07/03 22:44:37 INFO tool.CodeGenTool: Beginning code generation 21/07/03 22:44:39 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `staff2` AS t LIMIT 1 21/07/03 22:44:40 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `staff2` AS t LIMIT 1 21/07/03 22:44:40 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /usr/hdp/3.1.4.0-315/hadoop-mapreduce 21/07/03 22:45:48 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hdfs/compile/a53bb813b88ab155201196658f3ee001/staff2.jar 21/07/03 22:45:48 INFO mapreduce.ExportJobBase: Beginning export of staff2 21/07/03 22:47:59 INFO client.RMProxy: Connecting to ResourceManager at bern.swiss.ch/192.168.0.139:8050 21/07/03 22:48:07 INFO client.AHSProxy: Connecting to Application History server at bern.swiss.ch/192.168.0.139:10200 21/07/03 22:48:18 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /user/hdfs/.staging/job_1625340048377_0003 21/07/03 22:49:05 INFO input.FileInputFormat: Total input files to process : 1 21/07/03 22:49:05 INFO input.FileInputFormat: Total input files to process : 1 21/07/03 22:49:12 INFO mapreduce.JobSubmitter: number of splits:4 21/07/03 22:49:24 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1625340048377_0003 21/07/03 22:49:24 INFO mapreduce.JobSubmitter: Executing with tokens: [] 21/07/03 22:49:26 INFO conf.Configuration: found resource resource-types.xml at file:/etc/hadoop/3.1.4.0-315/0/resource-types.xml 21/07/03 22:49:32 INFO impl.YarnClientImpl: Submitted application application_1625340048377_0003 21/07/03 22:49:33 INFO mapreduce.Job: The url to track the job: http://bern.swiss.ch:8088/proxy/application_1625340048377_0003/ 21/07/03 22:49:33 INFO mapreduce.Job: Running job: job_1625340048377_0003 21/07/03 22:52:15 INFO mapreduce.Job: Job job_1625340048377_0003 running in uber mode : false 21/07/03 22:52:15 INFO mapreduce.Job: map 0% reduce 0% 21/07/03 22:56:45 INFO mapreduce.Job: map 75% reduce 0% 21/07/03 22:58:10 INFO mapreduce.Job: map 100% reduce 0% 21/07/03 22:58:13 INFO mapreduce.Job: Job job_1625340048377_0003 completed successfully 21/07/03 22:58:14 INFO mapreduce.Job: Counters: 32 File System Counters FILE: Number of bytes read=0 FILE: Number of bytes written=971832 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=1132 HDFS: Number of bytes written=0 HDFS: Number of read operations=19 HDFS: Number of large read operations=0 HDFS: Number of write operations=0 Job Counters Launched map tasks=4 Data-local map tasks=4 Total time spent by all maps in occupied slots (ms)=1733674 Total time spent by all reduces in occupied slots (ms)=0 Total time spent by all map tasks (ms)=866837 Total vcore-milliseconds taken by all map tasks=866837 Total megabyte-milliseconds taken by all map tasks=1775282176 Map-Reduce Framework Map input records=6 Map output records=6 Input split bytes=526 Spilled Records=0 Failed Shuffles=0 Merged Map outputs=0 GC time elapsed (ms)=1565 CPU time spent (ms)=6710 Physical memory (bytes) snapshot=661999616 Virtual memory (bytes) snapshot=12958916608 Total committed heap usage (bytes)=462422016 Peak Map Physical memory (bytes)=202506240 Peak Map Virtual memory (bytes)=3244965888 File Input Format Counters Bytes Read=0 File Output Format Counters Bytes Written=0 21/07/03 22:58:14 INFO mapreduce.ExportJobBase: Transferred 1.1055 KB in 630.3928 seconds (1.7957 bytes/sec) 21/07/03 22:58:14 INFO mapreduce.ExportJobBase: Exported 6 records. Switch and log onto MariaDB, switch to harsh8 database, and query for the new table staff2 [root@bern ~]# mysql -uroot -pwelcome1 Welcome to the MariaDB monitor. Commands end with ; or \g. Your MariaDB connection id is 266 Server version: 5.5.65-MariaDB MariaDB Server Copyright (c) 2000, 2018, Oracle, MariaDB Corporation Ab and others. Type 'help;' or '\h' for help. Type '\c' to clear the current input statement. MariaDB [(none)]> use harsh8; Reading table information for completion of table and column names You can turn off this feature to get a quicker startup with -A Database changed MariaDB [harsh8]> show tables; +------------------+ | Tables_in_harsh8 | +------------------+ | staff | | staff2 | +------------------+ 2 rows in set (2.64 sec) MariaDB [harsh8]> SELECT NOW(); +---------------------+ | NOW() | +---------------------+ | 2021-07-03 23:02:38 | +---------------------+ 1 row in set (0.00 sec) After the import MariaDB [harsh8]> select * from staff2; +-----+------------+-------------------+--------+------------+ | id | Name | Position | Salary | Department | +-----+------------+-------------------+--------+------------+ | 100 | Geoffrey | manager | 50000 | Admin | | 101 | Thomas | Oracle Consultant | 15000 | IT | | 102 | Biden | Project Manager | 28000 | PM | | 103 | Carmicheal | Bigdata developer | 30000 | BDS | | 104 | Johnson | Treasurer | 21000 | Accounts | | 105 | Gerald | Director | 30000 | Management | +-----+------------+-------------------+--------+------------+ 6 rows in set (0.00 sec) Check the source table used in the export see the timestamp ! MariaDB [harsh8]> SELECT NOW(); +---------------------+ | NOW() | +---------------------+ | 2021-07-03 23:04:50 | +---------------------+ 1 row in set (0.00 sec) Comparison MariaDB [harsh8]> select * from staff; +-----+------------+-------------------+--------+------------+ | id | Name | Position | Salary | Department | +-----+------------+-------------------+--------+------------+ | 100 | Geoffrey | manager | 50000 | Admin | | 101 | Thomas | Oracle Consultant | 15000 | IT | | 102 | Biden | Project Manager | 28000 | PM | | 103 | Carmicheal | Bigdata developer | 30000 | BDS | | 104 | Johnson | Treasurer | 21000 | Accounts | | 105 | Gerald | Director | 30000 | Management | +-----+------------+-------------------+--------+------------+ You have successfully created a table from a Sqoop export! Et Voila The conversion from part-m-00000 to txt did the trick, so this proves  it's doable  so you question is answered  🙂 You can revalidate by following my steps Happy hadooping ! ... View more

@mike_bronson7  Waiting for your response with the logs. ... View more

@dooby  There is a Jira out theret see the solution  https://issues.apache.org/jira/browse/SPARK-32536   ... View more

@Faizan123  Namenode [Master] and Datanode [Slave] are part of HDFS, which is the storage layer, and ResourceManager[Master] and NodeManager [Slave] are part of YARN, which is a Resource Negotiator. So HDFS and YARN work together usually but are quite independent at design and architecture but their slave processes run together on the compute nodes i.e DataNode and a NodeManager process.   This a high-level architecture of  RM and NM the 2 master processes and the latter being the brain of Hadoop  Below is a standard layout of a Hadoop cluster though we could have easily added a second RM for HA On the 12 compute nodes the NM and DN and co-located for the localized processing    It's illogical to separate the DN and NM on different nodes. The NodeManager is YARN’s per-node agent and takes care of the individual compute nodes in a Hadoop cluster. Updating the ResourceManager (RM) with the status of running jobs on the DN, overseeing containers’ life-cycle management; monitoring resource usage (memory, CPU) of individual containers, tracking node-health, log’s management, and auxiliary services which may be exploited by different YARN applications. DataNodes store data in a Hadoop cluster and is the name of the daemon that manages the data. File data is replicated on multiple DataNodes for reliability and so that localized computation can be executed near the data. That's the reason DN and NM are co-located on the same VM/host. It could be very interesting to see a screenshot of the roles co-located with your data nodes. Hope that gives you a clearer picture  ... View more

@harsh8  Any updates please let me know if you still need help Happy hadooping  ... View more