We are using hadoop 1.4 and it doesn’t have listFiles method so we use listStatus to get directories. It doesn’t have recursive option but it is easy to manage recursive lookup. val fs = FileSystem.get(new Configuration()) val status = fs.listStatus(new Path(YOUR_HDFS_PATH)) status.foreach(x=> println(x.getPath))
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
Short answer: To force Spark to execute a transformation, you’ll need to require a result. Sometimes a simple count action is sufficient. TL;DR: Ok, let’s review the RDD operations. RDDs support two types of operations: transformations – which create a new dataset from an existing one. actions – which return a value to the driver … Read more