LOAD DATA just copies the files to hive datafiles. Hive does not do any transformation while loading data into tables. So, in this case the input file /home/user/test_details.txt needs to be in ORC format if you are loading it into an ORC table. A possible workaround is to create a temporary table with STORED AS … Read more
The InputFormat is responsible to provide the splits. In general, if you have n nodes, the HDFS will distribute the file over all these n nodes. If you start a job, there will be n mappers by default. Thanks to Hadoop, the mapper on a machine will process the part of the data that is … Read more
The default Hadoop ports are as follows: (HTTP ports, they have WEB UI): Daemon Default Port Configuration Parameter ———————– ———— ———————————- Namenode 50070 dfs.http.address Datanodes 50075 dfs.datanode.http.address Secondarynamenode 50090 dfs.secondary.http.address Backup/Checkpoint node? 50105 dfs.backup.http.address Jobracker 50030 mapred.job.tracker.http.address Tasktrackers 50060 mapred.task.tracker.http.address Internally, Hadoop mostly uses Hadoop IPC, which stands for Inter Process Communicator, to communicate amongst … Read more
You can use org.apache.hadoop.fs.FileSystem. Specifically, FileSystem.listFiles([path], true) And with Spark… FileSystem.get(sc.hadoopConfiguration).listFiles(…, true) Edit It’s worth noting that good practice is to get the FileSystem that is associated with the Path‘s scheme. path.getFileSystem(sc.hadoopConfiguration).listFiles(path, true)
from your question I assume that you already have your data in hdfs. So you don’t need to LOAD DATA, which moves the files to the default hive location /user/hive/warehouse. You can simply define the table using the externalkeyword, which leaves the files in place, but creates the table definition in the hive metastore. See … Read more
Actually stdout only shows the System.out.println() of the non-map reduce classes. The System.out.println() for map and reduce phases can be seen in the logs. Easy way to access the logs is http://localhost:50030/jobtracker.jsp->click on the completed job->click on map or reduce task->click on tasknumber->task logs->stdout logs. Hope this helps
Use msck repair table command: MSCK [REPAIR] TABLE tablename; or ALTER TABLE tablename RECOVER PARTITIONS; if you are running Hive on EMR. Read more details about both commands here: RECOVER PARTITIONS
Here’s an algorithm that is built on mapping and reducing (folding). It expresses the sieve of Eratosthenes P = {3,5,7, …} \ U {{p2, p2+2p, p2+4p, …} | p in P} for the odd primes (i.e without the 2). The folding tree is indefinitely deepening to the right, like this: where each prime number … Read more
Actually having actually tried it, I think the link I posted as a comment doesn’t do exactly what you want with dependencies. What you are quite reasonably asking for is a way to have Spark play nicely with setuptools and pip regarding installing dependencies. It blows my mind that this isn’t supported better in Spark. … Read more
I was facing the issue of namenode not starting. I found a solution using following: first delete all contents from temporary folder: rm -Rf <tmp dir> (my was /usr/local/hadoop/tmp) format the namenode: bin/hadoop namenode -format start all processes again:bin/start-all.sh You may consider rolling back as well using checkpoint (if you had it enabled).