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Hadoop operations basic


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  • 1. Hadoop Operations - Basic Hafizur Rahman April 4, 2013
  • 2. Agenda ● Why Hadoop ● Hadoop Architecture ● Hadoop Installation ● Hadoop Configuration ● Hadoop DFS Command ● What's next
  • 3. Challenges at Large Scale ● Single node can't handle due to limited resource ○ Processor time, Memory, Hard drive space, Network bandwidth ○ Individual hard drives can only sustain read speeds between 60-100 MB/second, so multicore does not help that much ● Multiple nodes needed, but probability of failure increases ○ Network failure, Data transfer failure, Node failure ○ Desynchronized clock, Lock ○ Partial failure in distributed atomic transaction
  • 4. Hadoop Approach (1/4) ● Data Distribution ○ Distributed to all the nodes in the cluster ○ Replicated to several nodes
  • 5. Hadoop Approach (2/4) ● Move computation to the data ○ Whenever possible, rather than moving data for processing, computation is moved to the node that contains the data ○ Most data is read from local disk straight into the CPU, alleviating strain on network bandwidth and preventing unnecessary network transfers ○ This data locality results in high performance
  • 6. Hadoop Approach (3/4) ● MapReduce programming model ○ Run as isolated process
  • 7. Hadoop Approach (4/4) ● Isolated execution ○ Communication between nodes is limited and done implicitly ○ Individual node failures can be worked around by restarting tasks on other nodes ■ No message exchange needed by user task ■ No roll back to pre-arranged checkpoints to partially restart the computation ■ Other workers continue to operate as though nothing went wrong
  • 8. Hadoop Environment
  • 9. High-level Hadoop architecture
  • 10. HDFS (1/2) ● Storage component of Hadoop ● Distributed file system modeled after GFS ● Optimized for high throughput ● Works best when reading and writing large files (gigabytes and larger) ● To support this throughput HDFS leverages unusually large (for a filesystem) block sizes and data locality optimizations to reduce network input/output (I/O)
  • 11. HDFS (2/2) ● Scalability and availability are also key traits of HDFS, achieved in part due to data replication and fault tolerance ● HDFS replicates files for a configured number of times, is tolerant of both software and hardware failure, and automatically re-replicates data blocks on nodes that have failed
  • 12. HDFS Architecture
  • 13. MapReduce (1/2) ● MapReduce is a batch-based, distributed computing framework modeled ● Simplifies parallel processing by abstracting away the complexities involved in working with distributed systems ○ computational parallelization ○ work distribution ○ dealing with unreliable hardware and software
  • 14. MapReduce (2/2)
  • 15. MapReduce Logical Architecture ● Name Node ● Secondary Name Node ● Data Node ● Job Tracker ● Task Tracker
  • 16. Hadoop Installation ● Local mode ○ No need to communicate with other nodes, so it does not use HDFS, nor will it launch any of the Hadoop daemons ○ Used for developing and debugging the application logic of a MapReduce program ● Pseudo Distributed Mode ○ All daemons running on a single machine ○ Helps to examine memory usage, HDFS input/output issues, and other daemon interactions ● Fully Distributed Mode
  • 17. Hadoop Configuration File name Description ● Environment-specific settings go here. ● If a current JDK is not in the system path you’ll want to come here to configure your JAVA_HOME core-site.xml ● Contains system-level Hadoop configuration items ○ HDFS URL ○ Hadoop temporary directory ○ script locations for rack-aware Hadoop clusters ● Override settings in core-default.xml: 0.0/core-default.html. hdfs-site.xml ● Contains HDFS settings ○ default file replication count ○ block size ○ whether permissions are enforced ● Override settings in hdfs-default.xml: 0.0/hdfs-default.html mapred-site.xml ● Contains HDFS settings ○ default number of reduce tasks ○ default min/max task memory sizes ○ speculative execution ● Override settings in mapred-default.xml: http://hadoop.apache. org/common/docs/r1.0.0/mapred-default.html
  • 18. Installation Pseudo Distributed Mode ● Setup public key based login ○ ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa ○ cat ~/.ssh/ >> ~/.ssh/authorized_keys ● Update the following configuration ○ hadoop.tmp.dir and at core-site. xml ○ dfs.replication at hdfs-site.xml ○ mapred.job.tracker at mapred-site.xml ● Format NameNode ○ bin/hadoop namenode -format ● Start all daemons ○ bin/
  • 19. Hands On ● HDFS Commands ○ /hdfs_shell.html ● Execute example ○ Wordcount ● Web Interface ○ NameNode daemon: http://localhost:50070/ ○ JobTracker daemon: http://localhost:50030/ ○ TaskTracker daemon: http://localhost:50060/ ● Hadoop Job Command
  • 20. Hadoop FileSystem File System URI Scheme Java Impl. (all under org. apache.hadoop) Description Local file fs.LocalFileSystem Filesystem for a locally connected disk with client-side checksums HDFS hdfs hdfs.DistributedFileSystem Hadoop’s distributed filesystem WebHDFS webhdfs hdfs.web. WebHdfsFileSystem Filesystem providing secure read-write access to HDFS over HTTP S3 (native) s3n fs.s3native. NativeS3FileSystem Filesystem backed by Amazon S3 S3 (block based) s3 fs.s3.S3FileSystem Filesystem backed by Amazon S3, which stores files in blocks (much like HDFS) to overcome S3’s 5 GB file size limit. GlusterFS glusterfs fs.glusterfs. GlusterFileSystem Still in beta https://github. com/gluster/glusterfs/tree/master /glusterfs-hadoop
  • 21. Installation Fully Distributed Mode Three different kind of hosts: ● master ○ master node of the cluster ○ hosts NameNode and JobTracker daemons ● backup ○ hosts Secondary NameNode daemon ● slave1, slave2, ... ○ slave boxes running both DataNode and TaskTracker daemons
  • 22. Hadoop Configuration File Name Description masters ● Name is misleading and should have been called secondary-masters ● When you start Hadoop it will launch NameNode and JobTracker on the local host from which you issued the start command and then SSH to all the nodes in this file to launch the SecondaryNameNode. slaves ● Contains a list of hosts that are Hadoop slaves ● When you start Hadoop it will SSH to each host in this file and launch the DataNode and TaskTracker daemons
  • 23. Recipes ● S3 Configuration ● Using multiple disks/volumes and limiting HDFS disk usage ● Setting HDFS block size ● Setting the file replication factor
  • 24. Recipes: S3 Configuration ● Config file: conf/hadoop-site.xml ● To access S3 data using DFS command <property> <name>fs.s3.awsAccessKeyId</name> <value>ID</value> </property> <property> <name>fs.s3.awsSecretAccessKey</name> <value>SECRET</value> </property> ● To use S3 as a replacement for HDFS <property> <name></name> <value>s3://BUCKET</value> </property>
  • 25. Recipes: Disk Configuration ● Config file: $HADOOP_HOME/conf/hdfs-site.xml ● For multiple locations: <property> <name></name> <value>/u1/hadoop/data,/u2/hadoop/data</value> </property> ● For limiting the HDFS disk usage, specify reserved space for non-DFS (bytes per volume) <property> <name>dfs.datanode.du.reserved</name> <value>6000000000</value> </property>
  • 26. Recipes: HDFS Block Size (1/3) ● HDFS stores files across the cluster by breaking them down into coarser grained, fixed-size blocks ● Default HDFS block size is 64 MB ● Affects performance of ○ filesystem operations where larger block sizes would be more effective, if you are storing and processing very large files ○ MapReduce computations, as the default behavior of Hadoop is to create one map task for each data block of the input files
  • 27. Recipes: HDFS Block Size (2/3) ● Option 1: NameNode configuration ○ Add/modify dfs.block.size parameter at conf/hdfs- site.xml ○ Block size in number of bytes ○ Only the files copied after the change will have the new block size ○ Existing files in HDFS will not be affected <property> <name>dfs.block.size</name> <value>134217728</value> </property>
  • 28. Recipes: HDFS Block Size (2/3) ● Option 2: During file upload ○ Applies only to the specific file paths > bin/hadoop fs -Ddfs.blocksize=134217728 -put /user/foo ● Use fsck command > bin/hadoop fsck /user/foo/ -blocks -files -locations /user/foo/ 215227246 bytes, 2 block(s): .... 0. blk_6981535920477261584_1059len=134217728 repl=1 [hostname:50010] 1. blk_-8238102374790373371_1059 len=81009518 repl=1 [hostname:50010]
  • 29. Recipes: File Replication Factor (1/3) ● Replication done for fault tolerance ○ Pros: Improves data locality and data access bandwidth ○ Cons: Needs more storage ● HDFS replication factor is a file-level property that can be set per file basis
  • 30. Recipes: File Replication Factor (2/3) ● Set default replication factor ○ Add/Modify dfs.replication property in conf/hdfs- site.xml ○ Old files will be unaffected ○ Only the files copied after the change will have the new replication factor <property> <name>dfs.replication</name> <value>2</value> </property>
  • 31. Recipes: File Replication Factor (3/3) ● Set replication factor during file upload > bin/hadoop fs -D dfs.replication=1 -copyFromLocal non-criticalfile.txt /user/foo ● Change the replication factor of files or file paths that are already in the HDFS ○ Use setrep command ○ Syntax: hadoop fs -setrep [-R] <path> > bin/hadoop fs -setrep 2 non-critical-file.txt Replication 3 set: hdfs://myhost:9000/user/foo/non-critical-file.txt
  • 32. Recipes: Merging files in HDFS ● Use HDFS getmerge command ● Syntax: hadoop fs -getmerge <src> <localdst> [addnl] ● Copies files in a given path in HDFS to a single concatenated file in the local filesystem > bin/hadoop fs -getmerge /user/foo/demofiles merged.txt
  • 33. Hadoop Operations - Advanced
  • 34. Example: Advanced Operations ● HDFS ○ Adding new data node ○ Decommissioning data node ○ Checking FileSystem Integrity with fsck ○ Balancing HDFS Block Data ○ Dealing with a Failed Disk ● MapReduce ○ Adding a Tasktracker ○ Decommissioning a Tasktracker ○ Killing a MapReduce Job ○ Killing a MapReduce Task ○ Dealing with a Blacklisted Tasktracker
  • 35. Links ● http://www.michael-noll. com/tutorials/running-hadoop-on-ubuntu- linux-single-node-cluster/ ● http://www.michael-noll. com/tutorials/running-hadoop-on-ubuntu- linux-multi-node-cluster/ ● com/hadoop/tutorial/ ● /mapred_tutorial.html
  • 36. Q/A