MapReduce Tutorial

Queues are expected to be primarily used by Hadoop Schedulers. Additionaly, the JobClient exposes a set of information about the cluster. By mid, we had successfully deployed Corona across all our production systems. The slaves execute the tasks as directed by the master. Job setup is done by a separate task when the job is in PREP state and after initializing tasks. In such cases, the framework may skip additional records surrounding the bad record.

Terminology and Architecture

What is MapReduce?

If you were using Cloudera Manager to configure these automatically, Cloudera Manager will take care of it in MRv2 as well. If configuring these manually, simply set these to the amount of memory and number of cores on the machine after subtracting out resources needed for other services. Tasks should be requested with vcores equal to the number of cores they can saturate at once.

Currently vcores are very coarse - tasks will rarely want to ask for more than one of them, but a complementary axis that represents processing power may be added in the future to enable finer-grained resource configuration. Also noteworthy are the yarn. If tasks are submitted with resource requests lower than the minimum-allocation values, their requests will be set to these values.

If tasks are submitted with resource requests that are not multiples of the increment-allocation values, their requests will be rounded up to the nearest increments. Each host in the cluster has 24 GB of memory and 6 cores. Other services running on the nodes require 4 GB and 1 core, so we set yarn. If you leave the map and reduce task defaults of MB and 1 virtual core intact, you will have at most 5 tasks running at the same time.

If you want each of your tasks to use 5 GB, set their mapreduce. Cloudera recommends using the Fair Scheduler in MRv2. Fair Scheduler allocation files require changes in light of the new way that resources work.

The minMaps, maxMaps, minReduces, and maxReduces queue properties have been replaced with a minResources property and a maxProperties. By default, the MRv2 Fair Scheduler will attempt to equalize memory allocations in the same way it attempted to equalize slot allocations in MRv1.

The MRv2 Fair Scheduler contains a number of new features including hierarchical queues and fairness based on multiple resources. The jobtracker and tasktracker commands, which start the JobTracker and TaskTracker, are no longer supported because these services no longer exist. They are replaced with yarn resourcemanager and yarn nodemanager , which start the ResourceManager and NodeManager respectively.

Instead, yarn rmadmin should be used. The new admin commands mimic the functionality of the MRv1 names, allowing nodes, queues, and ACLs to be refreshed while the ResourceManager is running. The following section outlines the additional changes needed to migrate a secure cluster.

The mapred principal should still be used for the JobHistory Server. If you are using Cloudera Manager to configure security, this will be taken care of automatically. As in MRv1, a configuration must be set to have the user that submits a job own its task processes.

In a secure setup, NodeManager configurations should set yarn. Properties set in the taskcontroller. In secure setups, configuring hadoop-policy. The following is a table of MRv1 options and their MRv2 equivalents:. The queue administration ACL is no longer supported, but will be in a future release. The following is a list of default ports used by MRv2 and YARN, as well as the configuration properties used to configure them.

In MRv1, the JobTracker Web UI served detailed information about the state of the cluster and the jobs recent and current running on it. It also contained the job history page, which served information from disk about older jobs.

The MRv2 Web UI provides the same information structured in the same way, but has been revamped with a new look and feel. Jobs can be searched and viewed there just as they could in MRv1. Because the ResourceManager is meant to be agnostic to many of the concepts in MapReduce, it cannot host job information directly. Instead, it proxies to a Web UI that can. If the job is running, this proxy is the relevant MapReduce ApplicationMaster; if the job has completed, then this proxy is the JobHistoryServer.

Thus, the user experience is similar to that of MRv1, but the information is now coming from different places. The following tables summarize the changes in configuration parameters between MRv1 and MRv2. The table that follows shows TaskTracker properties and their equivalents in the auxiliary shuffle service that runs inside NodeManagers.

Many of these properties have new names in MRv2, but the MRv1 names will work for all properties except mapred. To read this documentation, you must turn JavaScript on. See the following sections for more information: Source compatibility may be broken for applications that make use of a few obscure APIs that are technically public, but rarely needed and primarily exist for internal use. These APIs are detailed below. Source incompatibility means that code changes will be required to compile.

It is orthogonal to binary compatibility - binaries for an application that is binary-compatible, but not source-compatible, will continue to run fine on the new framework, but code changes will be required to regenerate those binaries.

Several different settings are involved. The table below shows the default settings, as well as the settings that Cloudera recommends, for each configuration option. The following is a table of MRv1 options and their MRv2 equivalents: MRv1 MRv2 Comment security. A major improvement over MRv1 is: Further, the configuration and setup has also been simplified.

The main differences are: So, there is no need to run an additional daemon. Clients, applications, and NodeManagers do not require configuring a proxy-provider to talk to the active ResourceManager.

The other 3 properties can be defined on a job level. However in production clustes the jvm size is marked final to prevent abuses that may lead to OOMs. The jvm size of task jvms are defined by 'mapred. Regards Bejoy KS reply permalink. Michael Segel "However in production clustes the jvm size is marked final to prevent abuses that may lead to OOMs. On Nov 1, , at 6: Kartashov, Andy I checked my MR admin page running on: I did not touch the property of. It seemed that MR set it nicely to 14 automatically, processing 45 of my maps.

However, I increased reduce task from the default 1 to 14 in the mapred-site. Thursday, November 01, 3: This e-mail message and any attachments are confidential, subject to copyright and may be privileged. Any unauthorized use, copying or disclosure is prohibited. If you are not the intended recipient, please delete and contact the sender immediately. Please consider the environment before printing this e-mail.

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