Applications should implement tool for the same hadoop

Ask Question. Asked 6 years, 10 months ago. Active 6 years, 10 months ago. Viewed 1k times. Applications should implement Tool for the same How can I fix this? I am using CDH 4. Improve this question. Add a comment. Active Oldest Votes. Improve this answer. Sign up or log in Sign up using Google. Toggle navigation Apache Hadoop. Download Documentation Latest Stable 3. Latest news Release 3. Ozone 1. Release 3. Release 2. Modules The project includes these modules: Hadoop Common : The common utilities that support the other Hadoop modules.

Who Uses Hadoop? Ambari also provides a dashboard for viewing cluster health such as heatmaps and ability to view MapReduce, Pig and Hive applications visually alongwith features to diagnose their performance characteristics in a user-friendly manner. The location can be changed through SkipBadRecords. Here is a more complete WordCount which uses many of the features provided by the MapReduce framework we discussed so far. Hence it only works with a pseudo-distributed or fully-distributed Hadoop installation.

Notice that the inputs differ from the first version we looked at, and how they affect the outputs. Now, lets plug-in a pattern-file which lists the word-patterns to be ignored, via the DistributedCache. WordCount -Dwordcount. The second version of WordCount improves upon the previous one by using some features offered by the MapReduce framework:.

Getting Started. MapReduce Tutorial. HDFS Users. Deployment Layout. Secure Impersonation. Ensure that Hadoop is installed, configured and is running. More details: Single Node Setup for first-time users. Cluster Setup for large, distributed clusters.

Hadoop Streaming is a utility which allows users to create and run jobs with any executables e. Source Code WordCount. IOException; 4. Path; 7. Walk-through The WordCount application is quite straight-forward. Payload Applications typically implement the Mapper and Reducer interfaces to provide the map and reduce methods. How Many Maps? Reducer Reducer reduces a set of intermediate values which share a key to a smaller set of values. Reducer has 3 primary phases: shuffle, sort and reduce. Shuffle Input to the Reducer is the sorted output of the mappers.

Sort The framework groups Reducer inputs by keys since different mappers may have output the same key in this stage. Secondary Sort If equivalence rules for grouping the intermediate keys are required to be different from those for grouping keys before reduction, then one may specify a Comparator via JobConf.

The output of the Reducer is not sorted. How Many Reduces? Partitioner Partitioner partitions the key space. Reporter Reporter is a facility for MapReduce applications to report progress, set application-level status messages and update Counters.

OutputCollector OutputCollector is a generalization of the facility provided by the MapReduce framework to collect data output by the Mapper or the Reducer either the intermediate outputs or the output of the job. The framework tries to faithfully execute the job as described by JobConf , however: f Some configuration parameters may have been marked as final by administrators and hence cannot be altered.

While some job parameters are straight-forward to set e. Users can set the following parameter per job: Name Type Description mapred. A task will be killed if it consumes more Virtual Memory than this number. This number can be optionally used by Schedulers to prevent over-scheduling of tasks on a node based on RAM needs. Map Parameters A record emitted from a map will be serialized into a buffer and metadata will be stored into accounting buffers.

Name Type Description io. Each serialized record requires 16 bytes of accounting information in addition to its serialized size to effect the sort. This percentage of space allocated from io. Clearly, for a map outputting small records, a higher value than the default will likely decrease the number of spills to disk. When this percentage of either buffer has filled, their contents will be spilled to disk in the background.

Let io. Note that a higher value may decrease the number of- or even eliminate- merges, but will also increase the probability of the map task getting blocked. The lowest average map times are usually obtained by accurately estimating the size of the map output and preventing multiple spills. Other notes If either spill threshold is exceeded while a spill is in progress, collection will continue until the spill is finished. For example, if io. In other words, the thresholds are defining triggers, not blocking.

A record larger than the serialization buffer will first trigger a spill, then be spilled to a separate file. It is undefined whether or not this record will first pass through the combiner. It limits the number of open files and compression codecs during the merge.

If the number of files exceeds this limit, the merge will proceed in several passes. Though this limit also applies to the map, most jobs should be configured so that hitting this limit is unlikely there. Like the spill thresholds in the preceding note, this is not defining a unit of partition, but a trigger. In practice, this is usually set very high or disabled 0 , since merging in-memory segments is often less expensive than merging from disk see notes following this table.

This threshold influences only the frequency of in-memory merges during the shuffle. Since map outputs that can't fit in memory can be stalled, setting this high may decrease parallelism between the fetch and merge. Conversely, values as high as 1.

This parameter influences only the frequency of in-memory merges during the shuffle. Though some memory should be set aside for the framework, in general it is advantageous to set this high enough to store large and numerous map outputs. When the reduce begins, map outputs will be merged to disk until those that remain are under the resource limit this defines. By default, all map outputs are merged to disk before the reduce begins to maximize the memory available to the reduce.

For less memory-intensive reduces, this should be increased to avoid trips to disk. Other notes If a map output is larger than 25 percent of the memory allocated to copying map outputs, it will be written directly to disk without first staging through memory. When running with a combiner, the reasoning about high merge thresholds and large buffers may not hold. For merges started before all map outputs have been fetched, the combiner is run while spilling to disk.

In some cases, one can obtain better reduce times by spending resources combining map outputs- making disk spills small and parallelizing spilling and fetching- rather than aggressively increasing buffer sizes. When merging in-memory map outputs to disk to begin the reduce, if an intermediate merge is necessary because there are segments to spill and at least io. This directory holds the localized public distributed cache. Thus localized public distributed cache is shared among all the tasks and jobs of all users.

This directory holds the localized private distributed cache. Thus localized private distributed cache is shared among all the tasks and jobs of the specific user only.

It is not accessible to jobs of other users. The tasks can use this space as scratch space and share files among them. This directory is exposed to the users through the configuration property job. It is available as System property also. So, users streaming etc. The job. It is expanded in jars directory before the tasks for the job start. To access the unjarred directory, JobConf. The properties localized for each task are described below. This contains the temporary map reduce data generated by the framework such as map output files etc.

User can specify the property mapred. This defaults to. If the value is not an absolute path, it is prepended with task's working directory. Otherwise, it is directly assigned.

The directory will be created if it doesn't exist. Then, the child java tasks are executed with option -Djava. This directory is created, if mapred. The job submission process involves: Checking the input and output specifications of the job.

Computing the InputSplit values for the job. Setting up the requisite accounting information for the DistributedCache of the job, if necessary. Copying the job's jar and configuration to the MapReduce system directory on the FileSystem.

Submitting the job to the JobTracker and optionally monitoring it's status. Job Authorization Job level authorization and queue level authorization are enabled on the cluster, if the configuration mapred. Job Control Users may need to chain MapReduce jobs to accomplish complex tasks which cannot be done via a single MapReduce job. In such cases, the various job-control options are: runJob JobConf : Submits the job and returns only after the job has completed.

Job Credentials In a secure cluster, the user is authenticated via Kerberos' kinit command. The MapReduce framework relies on the InputFormat of the job to: Validate the input-specification of the job.

Split-up the input file s into logical InputSplit instances, each of which is then assigned to an individual Mapper. Provide the RecordReader implementation used to glean input records from the logical InputSplit for processing by the Mapper.

InputSplit InputSplit represents the data to be processed by an individual Mapper. The MapReduce framework relies on the OutputFormat of the job to: Validate the output-specification of the job; for example, check that the output directory doesn't already exist. Provide the RecordWriter implementation used to write the output files of the job.

Output files are stored in a FileSystem. The MapReduce framework relies on the OutputCommitter of the job to: Setup the job during initialization. For example, create the temporary output directory for the job during the initialization of the job. Job setup is done by a separate task when the job is in PREP state and after initializing tasks. Cleanup the job after the job completion. For example, remove the temporary output directory after the job completion. Job cleanup is done by a separate task at the end of the job.

Setup the task temporary output. Task setup is done as part of the same task, during task initialization. Check whether a task needs a commit. This is to avoid the commit procedure if a task does not need commit. Commit of the task output. Once task is done, the task will commit it's output if required. Discard the task commit. If task could not cleanup in exception block , a separate task will be launched with same attempt-id to do the cleanup.

Counters Counters represent global counters, defined either by the MapReduce framework or applications. DistributedCache DistributedCache distributes application-specific, large, read-only files efficiently. Private and Public DistributedCache Files DistributedCache files can be private or public, that determines how they can be shared on the slave nodes. These files are shared by all tasks and jobs of the specific user only and cannot be accessed by jobs of other users on the slaves.

A DistributedCache file becomes private by virtue of its permissions on the file system where the files are uploaded, typically HDFS. If the file has no world readable access, or if the directory path leading to the file has no world executable access for lookup, then the file becomes private. These files can be shared by tasks and jobs of all users on the slaves.

A DistributedCache file becomes public by virtue of its permissions on the file system where the files are uploaded, typically HDFS. If the file has world readable access, AND if the directory path leading to the file has world executable access for lookup, then the file becomes public.

In other words, if the user intends to make a file publicly available to all users, the file permissions must be set to be world readable, and the directory permissions on the path leading to the file must be world executable. Tool The Tool interface supports the handling of generic Hadoop command-line options. Note that currently IsolationRunner will only re-run map tasks.

Profiling Profiling is a utility to get a representative 2 or 3 sample of built-in java profiler for a sample of maps and reduces. How to distribute the script file: The user needs to use DistributedCache to distribute and symlink the script file. How to submit the script: A quick way to submit the debug script is to set values for the properties mapred.

JobControl JobControl is a utility which encapsulates a set of MapReduce jobs and their dependencies. Data Compression Hadoop MapReduce provides facilities for the application-writer to specify compression for both intermediate map-outputs and the job-outputs i.

Intermediate Outputs Applications can control compression of intermediate map-outputs via the JobConf. Skipping Bad Records Hadoop provides an option where a certain set of bad input records can be skipped when processing map inputs. DistributedCache; 8. Demonstrates how the DistributedCache can be used to distribute read-only data needed by the jobs. Here it allows the user to specify word-patterns to skip while counting line Demonstrates the utility of the Tool interface and the GenericOptionsParser to handle generic Hadoop command-line options lines , Demonstrates how applications can use Counters line 68 and how they can set application-specific status information via the Reporter instance passed to the map and reduce method line A number, in bytes, that represents the maximum Virtual Memory task-limit for each task of the job.

A number, in bytes, that represents the maximum RAM task-limit for each task of the job.