Hadoop官方文檔翻譯——MapReduce Tutorial

Hadoop MapReduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.

A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks.

Typically the compute nodes and the storage nodes are the same, that is, the MapReduce framework and the Hadoop Distributed File System (see HDFS Architecture Guide) are running on the same set of nodes. This configuration allows the framework to effectively schedule tasks on the nodes where data is already present, resulting in very high aggregate bandwidth across the cluster.

The MapReduce framework consists of a single master ResourceManager, one slave NodeManager per cluster-node, and MRAppMaster per application (see YARN Architecture Guide).

Minimally, applications specify the input/output locations and supply map and reduce functions via implementations of appropriate interfaces and/or abstract-classes. These, and other job parameters, comprise the job configuration.

The Hadoop job client then submits the job (jar/executable etc.) and configuration to the ResourceManager which then assumes the responsibility of distributing the software/configuration to the slaves, scheduling tasks and monitoring them, providing status and diagnostic information to the job-client.

Although the Hadoop framework is implemented in Java™, MapReduce applications need not be written in Java.

- Hadoop Streaming is a utility which allows users to create and run jobs with any executables (e.g. shell utilities) as the mapper and/or the reducer.
- Hadoop Pipes is a SWIG-compatible C++ API to implement MapReduce applications (non JNI™ based).

Hadoop Mapreduce是一個易於編程並且能在大型集群（上千節點）快速地並行得處理大量數據的軟件框架，以可靠，容錯的方式部署在商用機器上。

MapReduce Job通常將獨立大塊數據切片以完全並行的方式在map任務中處理。該框架對maps輸出的做為reduce輸入的數據進行排序，Job的輸入輸出都是存儲在文件系統中。該框架調度任務、監控任務和重啟失效的任務。

一般來說計算節點和存儲節點都是同樣的設置，MapReduce框架和HDFS運行在同組節點。這樣的設定使得MapReduce框架能夠以更高的帶寬來執行任務，當數據已經在節點上時。

MapReduce 框架包含一個主ResourceManager，每個集群節點都有一個從NodeManager和每個應用都有一個MRAppMaster。

應用最少必須指定輸入和輸出的路徑並且通過實現合適的接口或者抽象類來提供map和reduce功能。前面這部分內容和其他Job參數構成了Job的配置。

Hadoop 客戶端提交Job和配置信息給ResourceManger，它將負責把配置信息分配給從屬節點，調度任務並且監控它們，把狀態信息和診斷信息傳輸給客戶端。

　　盡管 MapReduce 框架是用Java實現的，但是 MapReduce 應用卻不一定要用Java編寫。

- Hadoop Streaming 是一個工具允許用戶創建和運行任何可執行文件。
- Hadoop Pipes 是兼容SWIG用來實現 MapReduce 應用的C++ API（不是基於JNI）.

Inputs and Outputs

The MapReduce framework operates exclusively on <key, value> pairs, that is, the framework views the input to the job as a set of <key, value> pairs and produces a set of <key, value>pairs as the output of the job, conceivably of different types.

The key and value classes have to be serializable by the framework and hence need to implement the Writable interface. Additionally, the key classes have to implement theWritableComparable interface to facilitate sorting by the framework.

Input and Output types of a MapReduce job:

(input) <k1, v1> -> map -> <k2, v2> -> combine -> <k2, v2> -> reduce -> <k3, v3> (output)

MapReduce 框架只操作鍵值對，MapReduce 將job的不同類型輸入當做鍵值對來處理並且生成一組鍵值對作為輸出。

Key和Value類必須通過實現Writable接口來實現序列化。此外，Key類必須實現WritableComparable 來使得排序更簡單。

MapRedeuce job 的輸入輸出類型：

(input) ->map-> ->combine-> ->reduce-> (output)

MapReduce - User Interfaces

This section provides a reasonable amount of detail on every user-facing aspect of the MapReduce framework. This should help users implement, configure and tune their jobs in a fine-grained manner. However, please note that the javadoc for each class/interface remains the most comprehensive documentation available; this is only meant to be a tutorial.

Let us first take the Mapper and Reducer interfaces. Applications typically implement them to provide the map and reduce methods.

We will then discuss other core interfaces including Job, Partitioner, InputFormat, OutputFormat, and others.

Finally, we will wrap up by discussing some useful features of the framework such as the DistributedCache, IsolationRunner etc.

這部分將展示 MapReduce 中面向用戶方面的盡可能多的細節。這將會幫助用戶更小粒度地實現、配置和調試它們的Job。然而，請在 Javadoc 中查看每個類和接口的綜合用法，這里僅僅是作為一份指導。

讓我們首先來看看Mapper和Reducer接口。應用通常只實現它們提供的map和reduce方法。

我們將會討論其他接口包括Job、Partitioner、InputFormat和其他的。

最后，我們會討論一些有用的特性像分布式緩存、隔離運行等。

Payload

Applications typically implement the Mapper and Reducer interfaces to provide the map and reduce methods. These form the core of the job.

應用通常實現Mapper和Reducer接口提供map和reduce方法。這是Job的核心代碼。

Mapper

Mapper maps input key/value pairs to a set of intermediate key/value pairs.

Maps are the individual tasks that transform input records into intermediate records. The transformed intermediate records do not need to be of the same type as the input records. A given input pair may map to zero or many output pairs.

The Hadoop MapReduce framework spawns（產卵） one map task for each InputSplit generated by the InputFormat for the job.

Overall, Mapper implementations are passed the Job for the job via the Job.setMapperClass(Class) method. The framework then calls map(WritableComparable, Writable, Context) for each key/value pair in the InputSplit for that task. Applications can then override the cleanup(Context) method to perform any required cleanup.

Output pairs do not need to be of the same types as input pairs. A given input pair may map to zero or many output pairs. Output pairs are collected with calls to context.write(WritableComparable, Writable).

Applications can use the Counter to report its statistics.

All intermediate（中間的） values associated（聯系） with a given output key are subsequently（隨后） grouped by the framework, and passed to the Reducer(s) to determine the final output. Users can control the grouping by specifying a Comparator via Job.setGroupingComparatorClass(Class).

The Mapper outputs are sorted and then partitioned per Reducer. The total number of partitions is the same as the number of reduce tasks for the job. Users can control which keys (and hence records) go to which Reducer by implementing a custom Partitioner.

Users can optionally（隨意） specify a combiner, via Job.setCombinerClass(Class), to perform local aggregation of the intermediate outputs, which helps to cut down the amount of data transferred from the Mapper to the Reducer.

The intermediate, sorted outputs are always stored in a simple (key-len, key, value-len, value) format. Applications can control if, and how, the intermediate outputs are to be compressed and the CompressionCodec to be used via the Configuration.

Mappers將輸入的鍵值對轉換成中間鍵值對。

Maps是多個單獨執行的任務將輸入轉換成中間記錄。那些被轉換的中間記錄不一定要和輸入的記錄為相同類型。輸入鍵值對可以在map后輸出0或者更多的鍵值對。

MapReduce 會根據 InputFormat 切分成的各個 InputSplit 都創建一個map任務

總的來說，通過 job.setMapperClass(Class)來給Job設置Mapper實現類，並且將InputSplit輸入到map方法進行處理。應用可復寫cleanup方法來執行任何需要回收清除的操作。

輸出鍵值對不一定要和輸入鍵值對為相同的類型。一個鍵值對輸入可以輸出0至多個不等的鍵值對。輸出鍵值對將通過context.write(WritableComparable,Writable)方法進行緩存。

應用可以通過Counter進行統計。

所有的中間值都會按照Key進行排序，然后傳輸給一個特定的Reducer做最后確定的輸出。用戶可以通過Job.setGroupingComparatorClass(Class)來控制分組規則。

Mapper輸出會被排序並且分區到每一個Reducer。分區數和Reduce的數目是一致的。用戶可以通過實現一個自定義的Partitioner來控制哪個key對應哪個Reducer。

用戶可以隨意指定一個combiner,Job.setCombinerClass(Class)，來執行局部輸出數據的整合，將有效地降低Mapper和Reducer之間的數據傳輸量。

那些經過排序的中間記錄通常會以（key-len, key, value-len, value）的簡單格式儲存。應用可以通過配置來決定是否需要和怎樣壓縮數據和選擇壓縮方式。

　　How Many Maps?

The number of maps is usually driven by the total size of the inputs, that is, the total number of blocks of the input files.

The right level of parallelism（平行） for maps seems to be around 10-100 maps per-node, although it has been set up to 300 maps for very cpu-light map tasks. Task setup takes a while, so it is best if the maps take at least a minute to execute.

Thus, if you expect 10TB of input data and have a blocksize of 128MB, you'll end up with 82,000 maps, unless Configuration.set(MRJobConfig.NUM_MAPS, int) (which only provides a hint to the framework) is used to set it even higher.

　　maps的數據通常依賴於輸入數據的總長度，也就是，輸入文檔的總block數。

每個節點map的正常並行度應該在10-100之間，盡管每個cpu已經設置的上限值為300。任務的配置會花費一些時間，最少需要花費一分鍾來啟動運行。

因此，如果你有10TB的數據輸入和定義blocksize為128M，那么你將需要82000 maps，除非通過Configuration.set(MRJobConfig.NUM_MAPS, int)（設置一個默認值通知框架）來設置更高的值。

Reducer

Reducer reduces a set of intermediate values which share a key to a smaller set of values.

The number of reduces for the job is set by the user via Job.setNumReduceTasks(int).

Overall（總的來說）, Reducer implementations are passed the Job for the job via the Job.setReducerClass(Class) method and can override it to initialize themselves. The framework then callsreduce(WritableComparable, Iterable<Writable>, Context) method for each <key, (list of values)> pair in the grouped inputs. Applications can then override the cleanup(Context)method to perform any required cleanup.

Reducer has 3 primary（主要） phases（階段）: shuffle, sort and reduce.

　　Reduce處理一系列相同key的中間記錄。

用戶可以通過 Job.setNumReduceTasks(int) 來設置reduce的數量。

總的來說，通過 Job.setReducerClass(Class) 可以給 job 設置 recuder 的實現類並且進行初始化。框架將會調用 reduce 方法來處理每一組按照一定規則分好的輸入數據，應用可以通過復寫cleanup 方法執行任何清理工作。

Reducer有3個主要階段：混洗、排序和reduce。

Shuffle

Input to the Reducer is the sorted output of the mappers. In this phase the framework fetches（取得） the relevant（有關的，恰當的） partition of the output of all the mappers, via HTTP.

輸出到Reducer的數據都在Mapper階段經過排序的。在這個階段框架將通過HTTP從恰當的Mapper的分區中取得數據。

Sort

The framework groups Reducer inputs by keys (since different mappers may have output the same key) in this stage（階段）.

The shuffle and sort phases occur simultaneously（同時）; while map-outputs are being fetched they are merged.

這個階段框架將對輸入到的 Reducer 的數據通過key（不同的 Mapper 可能輸出相同的key）進行分組。

混洗和排序階段是同時進行；map的輸出數據被獲取時會進行合並。

Secondary Sort

If equivalence（平等的） 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 Job.setSortComparatorClass(Class). Since Job.setGroupingComparatorClass(Class) can be used to control how intermediate keys are grouped, these can be used in conjunction（協調） to simulate（模擬） secondary sort on values.

如果想要對中間記錄實現與 map 階段不同的排序方式，可以通過Job.setSortComparatorClass(Class) 來設置一個比較器。Job.setGroupingComparatorClass(Class) 被用於控制中間記錄的排序方式，這些能用來進行值的二次排序。

Reduce

In this phase the reduce(WritableComparable, Iterable<Writable>, Context) method is called for each <key, (list of values)> pair in the grouped inputs.

The output of the reduce task is typically written to the FileSystem via Context.write(WritableComparable, Writable).

Applications can use the Counter to report its statistics.

The output of the Reducer is not sorted.

在這個階段reduce方法將會被調用來處理每個已經分好的組鍵值對。

reduce 任務一般通過 Context.write(WritableComparable, Writable) 將數據寫入到FileSystem。

應用可以使用 Counter 進行統計。

Recuder 輸出的數據是不經過排序的。

How Many Reduces?

The right number of reduces seems to be 0.95 or 1.75 multiplied（乘上） by (<no. of nodes> * <no. of maximum containers per node>).

With 0.95 all of the reduces can launch immediately（立刻） and start transferring map outputs as the maps finish. With 1.75 the faster nodes will finish their first round of reduces and launch a second wave（波浪） of reduces doing a much better job of load balancing（均衡）.

Increasing the number of reduces increases the framework overhead（負擔，天花板）, but increases load balancing and lowers the cost of failures.

The scaling（規模） factors above are slightly（輕微的） less than whole numbers to reserve a few reduce slots in the framework for speculative（推測的）-tasks and failed tasks.

　　合適的 reduce 總數應該在節點數*每個節點的容器數*0.95 至節點數*每個節點的容器數*1.75 之間。

當設定值為0.95時，map任務結束后所有的 reduce 將會立刻啟動並且開始轉移數據，當設定值為1.75時，處理更多任務的時候將會快速地一輪又一輪地運行 reduce 達到負載均衡。

reduce 的數目的增加將會增加框架的負擔（天花板），但是會提高負載均衡和降低失敗率。

整體的規模將會略小於總數，因為有一些 reduce slot 用來存儲推測任務和失敗任務。

Reducer NONE

It is legal to set the number of reduce-tasks to zero if no reduction is desired.

In this case the outputs of the map-tasks go directly to the FileSystem, into the output path set by FileOutputFormat.setOutputPath(Job, Path). The framework does not sort the map-outputs before writing them out to the FileSystem.

當沒有 reduction 需求的時候可以將 reduce-task 的數目設置為0，是允許的。

在這種情況當中，map任務將直接輸出到 FileSystem，可通過 FileOutputFormat.setOutputPath(Job, Path) 來設置。該框架不會對輸出的FileSystem 的數據進行排序。

Partitioner

Partitioner partitions the key space.

Partitioner controls the partitioning of the keys of the intermediate map-outputs. The key (or a subset （子集）of the key) is used to derive（取得；源自） the partition, typically by a hash function. The total number of partitions is the same as the number of reduce tasks for the job. Hence this controls which of the m reduce tasks the intermediate key (and hence the record) is sent to for reduction.

HashPartitioner is the default Partitioner.

　　Partitioner對key進行分區。

Partitioner 對 map 輸出的中間值的 key（Recuder之前）進行分區。分區采用的默認方法是對 key 取 hashcode。分區數等於 job 的 reduce 任務數。因此這會根據中間值的key 將數據傳輸到對應的 reduce。

HashPartitioner 是默認的的分區器。

Counter

Counter is a facility for MapReduce applications to report its statistics.

Mapper and Reducer implementations can use the Counter to report statistics.

Hadoop MapReduce comes bundled（捆綁） with a library of generally（普遍的） useful mappers, reducers, and partitioners.

　 計數器是一個工具用於報告 Mapreduce 應用的統計。

Mapper 和 Reducer 實現類可使用計數器來報告統計值。

Hadoop Mapreduce 是普遍的可用的 Mappers、Reducers 和 Partitioners 組成的一個庫。

Job Configuration

Job represents（代表，表示） a MapReduce job configuration.

Job is the primary interface for a user to describe a MapReduce job to the Hadoop framework for execution. The framework tries to faithfully（如實的） execute the job as described by Job, however:

- Some configuration parameters may have been marked as final by administrators (see Final Parameters) and hence cannot be altered（改變）.
- While some job parameters are straight-forward to set (e.g. Job.setNumReduceTasks(int)), other parameters interact（互相影響） subtly（微妙的） with the rest of the framework and/or job configuration and are more complex to set (e.g. Configuration.set(JobContext.NUM_MAPS, int)).

Job is typically used to specify the Mapper, combiner (if any), Partitioner, Reducer, InputFormat, OutputFormat implementations. FileInputFormat indicates（指定，表明） the set of input files (FileInputFormat.setInputPaths(Job, Path...)/ FileInputFormat.addInputPath(Job, Path)) and ( FileInputFormat.setInputPaths(Job, String...)/ FileInputFormat.addInputPaths(Job, String))and where the output files should be written ( FileOutputFormat.setOutputPath(Path)).

Optionally, Job is used to specify other advanced facets of the job such as the Comparator to be used, files to be put in the DistributedCache, whether intermediate and/or job outputs are to be compressed (and how), whether job tasks can be executed in a speculative manner ( setMapSpeculativeExecution(boolean))/ setReduceSpeculativeExecution(boolean)), maximum number of attempts per task (setMaxMapAttempts(int)/ setMaxReduceAttempts(int)) etc.

Of course, users can use Configuration.set(String, String)/ Configuration.get(String) to set/get arbitrary parameters needed by applications. However, use the DistributedCache for large amounts of (read-only) data.

　　Job類用來表示MapReduce作業的配置。Job是用戶用來描述MapReduce job在Hadoop框架運行的主要接口。Hadoop將盡量如實地按照job所描述的來執行。然而：

- 一些配置參數已經被管理員標注為不可更改的因此不能被改變。
- 一些參數是直接設置的（如Job.setNumReduceTasks(int)），有一些參數是跟框架或者任務配置之間有微妙的互相影響並且復雜的設置。

Job 典型地用於指定Mapper、Combiner、Partitioner、Reducer、InputFormat、OutputFormat實現類。 FileInputFormat指定輸入文檔的設定(FileInputFormat.setInputPaths(Job, Path...)/FileInputFormat.addInputPath(Job, Path))和(FileInputFormat.setInputPaths(Job, String...)/FileInputFormat.addInputPaths(Job, String))和輸出文件應該寫入通過(FileOutputFormat.setOutputPath(Path)).

隨意地,Job也常用來指定job的其他高級配置，例如比較器、文檔置於分布式緩存、中間記錄是否壓縮和怎樣壓縮, job任務是否已預測的方式去執行，每個任務的最大處理量等等。

當然，用戶可以使用來設置或者獲得應用所需要的任何參數。然而，使用分布式緩存來存儲大量的可讀數據。

Task Execution & Environment

The MRAppMaster executes the Mapper/Reducer task as a child process in a separate jvm.

The child-task inherits the environment of the parent MRAppMaster. The user can specify additional options to the child-jvm via the mapreduce.{map|reduce}.java.opts and configuration parameter in the Job such as non-standard paths for the run-time linker to search shared libraries via -Djava.library.path=<> etc. If the mapreduce.{map|reduce}.java.opts parameters contains the symbol @taskid@ it is interpolated with value of taskid of the MapReduce task.

Here is an example with multiple arguments and substitutions, showing jvm GC logging, and start of a passwordless（無密碼） JVM JMX agent so that it can connect with jconsole and the likes to watch child memory, threads and get thread dumps. It also sets the maximum heap-size of the map and reduce child jvm to 512MB & 1024MB respectively. It also adds an additional path to the java.library.path of the child-jvm.

 1 <property>
 2 
 3   <name>mapreduce.map.java.opts</name>
 4 
 5   <value>
 6 
 7     -Xmx512M -Djava.library.path=/home/mycompany/lib -verbose:gc -Xloggc:/tmp/@taskid@.gc
 8 
 9     -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.ssl=false
10 
11   </value>
12 
13 </property>
14 
15  
16 
17 <property>
18 
19   <name>mapreduce.reduce.java.opts</name>
20 
21   <value>
22 
23     -Xmx1024M -Djava.library.path=/home/mycompany/lib -verbose:gc -Xloggc:/tmp/@taskid@.gc
24 
25     -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.ssl=false
26 
27   </value>
28 
29 </property>

MRAppMaster 在一個單獨的jvm中運行Mapper/Reducer任務做為一個子進程。

子任務繼承父MRAppMaster的運行環境。用戶可以通過（mapreduce.{map|reduce}.java.opts和配置參數例如通過 Djava.library.path=<>可以設置非標准的路徑用於運行時搜索庫）指定額外的設置。如果mapreduce.{map|reduce}.java.opts參數包含@taskid@ 符號那么Mapreduce任務將會被修改為taskid的值。

下面有個例子;配置多個參數和代替，展示jvm gc 日志，和 JVM JMX 代理用於無密碼登錄以致可以連接JConsole來監控子程序的內存、線程和線程垃圾回收。也分別設置了map和reduce的最大堆內存為512M和1024M。它也給子jvm添加了額外的路徑通過java.library.path參數。

Memory Management

Users/admins can also specify the maximum virtual memory of the launched child-task, and any sub-process it launches recursively, using mapreduce.{map|reduce}.memory.mb. Note that the value set here is a per process limit. The value for mapreduce.{map|reduce}.memory.mb should be specified in mega bytes (MB). And also the value must be greater than or equal to the -Xmx passed to JavaVM, else the VM might not start.

Note: mapreduce.{map|reduce}.java.opts are used only for configuring the launched child tasks from MRAppMaster. Configuring the memory options for daemons is documented inConfiguring the Environment of the Hadoop Daemons.

The memory available to some parts of the framework is also configurable. In map and reduce tasks, performance may be influenced by adjusting parameters influencing the concurrency of operations and the frequency with which data will hit disk. Monitoring the filesystem counters for a job- particularly relative to byte counts from the map and into the reduce- is invaluable to the tuning of these parameters.

用戶或者管理員可以使用mapreduce.{map|reduce}.memory.mb指定子任務或者任何子進程運行的最大虛擬內存。需要注意的這里的值是針對每個進程的限制。{map|reduce}.memory.mb的值是以MB為單位的。並且這個值應該大於等於傳給JavaVM的-Xmx的值，要不VM可能會無法啟動。

　　說明：mapreduce.{map|reduce}.java.opts只用來設置MRAppMaster發出的子任務。守護線程的內存選項配置在Configuring the Environment of the Hadoop Daemons.

　　框架的一些組成部分的內存也是可配置的。在map和reduce任務中，性能可能會受到並發數的調整和寫入到磁盤的頻率的影響。文件系統計數器監控作業的map輸出和輸入到reduce的字節數對於調整這些參數是寶貴的。

Map Parameters

A record emitted（發射） from a map will be serialized into a buffer and metadata will be stored into accounting buffers. As described in the following options, when either the serialization buffer or the metadata exceed（超過） a threshold（入口）, the contents of the buffers will be sorted and written to disk in the background while the map continues to output records. If either buffer fills completely while the spill is in progress, the map thread will block. When the map is finished, any remaining records are written to disk and all on-disk segments are merged into a single file. Minimizing the number of spills to disk can decrease map time, but a larger buffer also decreases the memory available to the mapper.

Name	Type	Description
mapreduce.task.io.sort.mb	int	The cumulative（累積） size of the serialization and accounting buffers storing records emitted from the map, in megabytes.
mapreduce.map.sort.spill.percent	float	The soft limit in the serialization buffer. Once reached, a thread will begin to spill the contents to disk in the background.

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 mapreduce.map.sort.spill.percent is set to 0.33, and the remainder（剩余） of the buffer is filled while the spill runs, the next spill will include all the collected records, or 0.66 of the buffer, and will not generate additional spills. 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.

Map發出的數據將會被序列化在緩存中和源數據將會儲存在統計緩存。正如接下來的配置所描述的，當序列化緩存和元數據超過設定的臨界值，緩存中的內容將會后台中寫入到磁盤中而map將會繼續輸出記錄。當緩存完全滿了溢出之后，map線程將會阻塞。當map任務結束，所有剩下的記錄都會被寫到磁盤中並且磁盤中所有文件塊會被合並到一個單獨的文件。減小溢出值將減少map的時間，但更大的緩存會減少mapper的內存消耗。

其他說明：

- 當任何一個spill超出的臨界值，收集還會持續進行直到結束。例如，當mapreduce.map.sort.spill.percent 設置為0.33，那么剩余的緩存將會繼續填充而spill會繼續運行，而下一個spill將會包含所有的收集的記錄，而當值為0.66，將不會產生另一個spills。也就是說，臨界值會被觸發，但不會阻塞。
- 一個記錄大於序列化緩存將會第一時間觸發溢出，並且會被寫到一個單獨的文件。無論是否有定義都會第一時間通過combiner進行傳輸。

Shuffle/Reduce Parameters

As described previously, each reduce fetches the output assigned to it by the Partitioner via HTTP into memory and periodically merges these outputs to disk. If intermediate compression of map outputs is turned on, each output is decompressed into memory. The following options affect the frequency of these merges to disk prior to the reduce and the memory allocated to map output during the reduce.

Name	Type	Description
mapreduce.task.io.soft.factor	int	Specifies the number of segments on disk to be merged at the same time. It limits the number of open files and compression codecs during 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.
mapreduce.reduce.merge.inmem.thresholds	int	The number of sorted map outputs fetched into memory before being merged to disk. 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 (1000) 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.
mapreduce.reduce.shuffle.merge.percent	float	The memory threshold for fetched map outputs before an in-memory merge is started, expressed as a percentage of memory allocated to storing map outputs in memory. 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 1.0 have been effective for reduces whose input can fit entirely in memory. This parameter influences only the frequency of in-memory merges during the shuffle.
mapreduce.reduce.shuffle.input.buffer.percent	float	The percentage of memory- relative to the maximum heapsize as typically specified in mapreduce.reduce.java.opts- that can be allocated to storing map outputs 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.
mapreduce.reduce.input.buffer.percent	float	The percentage of memory relative to the maximum heapsize in which map outputs may be retained during the reduce. 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 leastmapreduce.task.io.sort.factor segments already on disk, the in-memory map outputs will be part of the intermediate merge.

正如前面提到的，每個reduce都會通過HTTP在內存中拿到Partitioner分配好的數據並且定期地合並數據寫到磁盤中。如果map輸出的中間值都進行壓縮，那么每個輸出都會減少內存的壓力。下面這些設置將會影響reduce之前的數據合並到磁盤的頻率和reduce過程中分配給map輸出的內存。

其他說明：

- 如果一個map輸出大於分配給用於復制map輸出的內存的25%，那么將會直接寫到磁盤不會通過內存進行臨時緩存。
- 當運行一個combiner，高的臨界值和大的緩存的理由將沒有效果。在map輸出進行合並之前，combiner將會進行溢出寫到磁盤的操作。在一些例子當中，耗費資源combine map輸出數據獲得更小的溢出會比粗暴地增加緩存大小使得recuder的時間更少。
- 當合並內存中的map數據到磁盤來開始recuder時，如果磁盤中已經存在部分切片數據的話，那么必須將內存中的數據作為磁盤中間數據的一部分來進行合並操作。

Configured Parameters

The following properties are localized in the job configuration for each task's execution:

Name	Type	Description
mapreduce.job.id	String	The job id
mapreduce.job.jar	String	job.jar location in job directory
mapreduce.job.local.dir	String	The job specific shared scratch space
mapreduce.task.id	String	The task id
mapreduce.task.attempt.id	String	The task attempt id
mapreduce.task.is.map	boolean	Is this a map task
mapreduce.task.partition	int	The id of the task within the job
mapreduce.map.input.file	String	The filename that the map is reading from
mapreduce.map.input.start	long	The offset of the start of the map input split
mapreduce.map.input.length	long	The number of bytes in the map input split
mapreduce.task.output.dir	String	The task's temporary output directory

Note: During the execution of a streaming job, the names of the "mapreduce" parameters are transformed. The dots ( . ) become underscores ( _ ). For example, mapreduce.job.id becomes mapreduce_job_id and mapreduce.job.jar becomes mapreduce_job_jar. To get the values in a streaming job's mapper/reducer use the parameter names with the underscores.

說明：流式任務的執行過程中，名字以mapreduce開頭的參數會被改變。符號（.）會變成（_）。例如，mapreduce.job.id會變成mapreduce_job_id和mapreduce.job.jar會變成mapreduce_job_jar。在Mapper/Reducer中使用帶下划線的參數名來獲得對應的值。

Task Logs

The standard output (stdout) and error (stderr) streams and the syslog of the task are read by the NodeManager and logged to ${HADOOP_LOG_DIR}/userlogs.

NodeManager 會讀取stdout、sterr和任務的syslog並寫到${HADOOP_LOG_DIR}/userlogs。

Distributing Libraries

The DistributedCache can also be used to distribute both jars and native libraries for use in the map and/or reduce tasks. The child-jvm always has its current working directory added to the java.library.path and LD_LIBRARY_PATH. And hence the cached libraries can be loaded via System.loadLibrary or System.load. More details on how to load shared libraries through distributed cache are documented at Native Libraries.

分布是緩存也可以在map/reduce任務中用來分不是存儲jars和本地庫。子JVM經常將它的工作路徑添加到java.librarypath和LD_LIBRARY_PATH.因此緩存的庫能通過System.loadLibrary 或者 System.load 來加載。更多關於如何通過分布式緩存來加載第三方庫參考Native Libraries.

Job Submission and Monitoring

Job is the primary interface by which user-job interacts with the ResourceManager.

Job provides facilities to submit jobs, track their progress, access component-tasks' reports and logs, get the MapReduce cluster's status information and so on.

The job submission process involves:

1. Checking the input and output specifications of the job.
2. Computing the InputSplit values for the job.
3. Setting up the requisite accounting information for the DistributedCache of the job, if necessary.
4. Copying the job's jar and configuration to the MapReduce system directory on the FileSystem.
5. Submitting the job to the ResourceManager and optionally monitoring it's status.

Job history files are also logged to user specified directory mapreduce.jobhistory.intermediate-done-dir and mapreduce.jobhistory.done-dir, which defaults to job output directory.

User can view the history logs summary in specified directory using the following command
$ mapred job -history output.jhist
This command will print job details, failed and killed tip details.
More details about the job such as successful tasks and task attempts made for each task can be viewed using the following command
$ mapred job -history all output.jhist

Normally the user uses Job to create the application, describe various facets of the job, submit the job, and monitor its progress.

Job 是用戶Job與ResourceManager交互的主要接口。

Job 提供工具去提交jobs、跟蹤他們的進程、使用組成任務的報告和日志，獲得MapReduce集群的狀態信息和其他。

Job的提交包含以下內容：

檢查Job的輸入輸出指定
計算Job的InputSplit的值
如果必要的話，設置分布式緩存的需求信息。
將Job的jar和configuration復制到Mapreduce系統的文件系統路徑下。
將Job提交到ResourceManger並且隨時監控它的狀態。

Job的歷史文件也被記錄到用戶通過mapreduce.jobhistory.intermediate-done-dir and mapreduce.jobhistory.done-dir指定的路徑下，默認是Job的輸出路徑。

用戶可以通過下面的指令來查看指定路徑下的所有的歷史記錄。

$ mapred job -history output.jhist

這個命令可以打印job的細節，失敗和殺死Job的技巧。用以下的命令可以考到更多關於Job例如成功任務和每個任務的目的細節。

$ mapred job -history all output.jhist

Normally the user uses Job to create the application, describe various facets of the job, submit the job, and monitor its progress.

一般來說用戶使用Job來創建應用，描述Job的各個方面，提交Job和監控它的進程。

Job Control

Users may need to chain MapReduce jobs to accomplish（實現） complex tasks which cannot be done via a single MapReduce job. This is fairly easy since the output of the job typically goes to distributed file-system, and the output, in turn（依次）, can be used as the input for the next job.

However, this also means that the onus on ensuring jobs are complete (success/failure) lies squarely on the clients. In such cases, the various job-control options are:

- Job.submit() : Submit the job to the cluster and return immediately.
- Job.waitForCompletion(boolean) : Submit the job to the cluster and wait for it to finish.

用戶可能需要將多個任務串行實現復雜任務而沒辦法通過一個MapReduce任務實現。這是相當容易，job的output通常是輸出到分布式緩存，而輸出，依次作為下一個任務的輸入。

然而，這也意味確保任務的完成（成功/失敗）的義務是完全建立在客戶端上。在這種情況下，各種作業的控制選項有：

- Job.submit() :提交作業給集群並立刻回復
- Job.waitForCompletion(boolean) :提交作業給集群並且等待它完成。

Job Input

InputFormat describes the input-specification for a MapReduce job.

The MapReduce framework relies on the InputFormat of the job to:

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

The default behavior of file-based InputFormat implementations, typically sub-classes of FileInputFormat, is to split the input into logical InputSplit instances based on the total size, in bytes, of the input files. However, the FileSystem blocksize of the input files is treated as an upper bound for input splits. A lower bound on the split size can be set viamapreduce.input.fileinputformat.split.minsize.

Clearly, logical splits based on input-size is insufficient for many applications since record boundaries must be respected. In such cases, the application should implement a RecordReader, who is responsible for respecting record-boundaries and presents a record-oriented view of the logical InputSplit to the individual task.

TextInputFormat is the default InputFormat.

If TextInputFormat is the InputFormat for a given job, the framework detects input-files with the .gz extensions and automatically decompresses them using the appropriate CompressionCodec. However, it must be noted that compressed files with the above extensions cannot be split and each compressed file is processed in its entirety by a single mapper.

InputFormat描述MapReduce Job的輸入規定。

MapReduce框架依賴Job的InputFormat：

1. 使Job的輸入設定生效。
2. 將輸入文件分割成邏輯上的輸入塊實例，並將每一輸入塊分配給單獨的Mapper。
3. 提供RecordReader實現用於收集從邏輯輸入塊的記錄輸入到Mapper中。

那些默認的基於InputFormat的實現，通常來說FileInputForamt的子類，基於總字節數將輸入基於字節數分成邏輯輸入塊實例，然而，FileSystem的塊大小將是inputSplits的上限值，下限值可以通過mapreduce.input.fileinputformat.split.minsize來設置。

很明顯，很多應用必須重視記錄的邊界，因存在着輸入大小不足以邏輯分割。在這種情況，應用應當實現一個RecordReader，負責在單獨任務中處理記錄邊界和顯示，面向記錄的邏輯視圖。

TextInputForamt是默認的InputForamt。

如果job的InputForamt是TextInputFormat，框架會對輸入文件進行檢測，如果擴展名為.gz那么會自動用合適的壓縮編碼器進行解壓。然而，必須說明的是經過壓縮的文件將不能被切割並且每一個壓縮文件都必須完全在一個Mapper單獨處理。

InputSplit

InputSplit represents the data to be processed by an individual Mapper.

Typically InputSplit presents a byte-oriented view of the input, and it is the responsibility of RecordReader to process and present a record-oriented view.

FileSplit is the default InputSplit. It sets mapreduce.map.input.file to the path of the input file for the logical split.

輸入塊表示每個單獨Mapper處理的數據。

通常來說，輸入塊代表輸入的面向字節視圖，而RecordReader代表的是面向記錄視圖。

FileSplit是默認的InputSplit。mapreduce.map.input.file設置用於邏輯分割的輸入路徑。

RecordReader

RecordReader reads <key, value> pairs from an InputSplit.

Typically the RecordReader converts the byte-oriented view of the input, provided by the InputSplit, and presents a record-oriented to the Mapper implementations for processing.RecordReader thus assumes the responsibility of processing record boundaries and presents the tasks with keys and values.

RecordReader從InputSplit讀取鍵值對。

通常來說，RecordReader將輸入的面向字節視圖轉換成面向記錄視圖並輸入到Mapper的實現類進行處理。RecordReader因此承擔起處理記錄邊界和顯示任務的Keys和Values的責任。

Job Output

OutputFormat describes the output-specification for a MapReduce job.

The MapReduce framework relies on the OutputFormat of the job to:

1. Validate the output-specification of the job; for example, check that the output directory doesn't already exist.
2. Provide the RecordWriter implementation used to write the output files of the job. Output files are stored in a FileSystem.

TextOutputFormat is the default OutputFormat.

OutputFormat 描述MapReduce Job的輸出規定。

MapReduce 框架依賴於Job的OutputFormat：

1. 使job的輸出設置生效；例如，檢查輸出路徑是否已經存在。
2. 提供RecordWriter實現用於輸出文件。輸出文件儲存在FileSystem。

OutputCommitter

OutputCommitter describes the commit of task output for a MapReduce job.

The MapReduce framework relies on the OutputCommitter of the job to:

1. 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. Once the setup task completes, the job will be moved to RUNNING state.
2. 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. Job is declared SUCCEDED/FAILED/KILLED after the cleanup task completes.
3. Setup the task temporary output. Task setup is done as part of the same task, during task initialization.
4. Check whether a task needs a commit. This is to avoid the commit procedure if a task does not need commit.
5. Commit of the task output. Once task is done, the task will commit it's output if required.
6. Discard the task commit. If the task has been failed/killed, the output will be cleaned-up. If task could not cleanup (in exception block), a separate task will be launched with same attempt-id to do the cleanup.

FileOutputCommitter is the default OutputCommitter. Job setup/cleanup tasks occupy map or reduce containers, whichever is available on the NodeManager. And JobCleanup task, TaskCleanup tasks and JobSetup task have the highest priority, and in that order.

OutputCommitter 描述着MapReduce的任務輸出的提交。

MapReduce依賴於Job的輸出提交器:

1. 初始化時設置Job。例如，job的初始化過程中創建臨時輸出路徑。當Job處於准備階段和初始化任務之后，Job通過一個單獨的任務完成創建。，一旦任務的創建完成之后，job將會轉成運行狀態。
2. Job完成之后清除Job。例如，Job完成后移除臨時輸出路徑。Job結束之時用一個單獨的任務完成Job的清除。在完成對任務的清除之后Job會聲明SUCCEDED/FAILED/KILLED.
3. 設置任務臨時輸出。在任務的初始化過程中，任務設置作為任務的一部分來完成。
4. 檢查一個任務是否需要提交。這將避免一個不需要提交的任務執行提交程序。
5. 提交任務的輸出。一旦任務完成，任務將會提交它的輸出如果需要的話。
6. 放棄任務提交。如果任務已經失敗或者被殺死，那么輸出將會被清除掉。如果任務因為意外沒有被清除掉，那么一個單獨的任務將會被運行來執行清除工作。

FileOutputCommitter是默認的OutputCommitter。Job 創建/清除任務占有map或者reduce容器，無論NodeManager是否可用。Job的清除任務，任務的清除任務和Job的創建任務擁有最高的優先級。

Task Side-Effect Files

In some applications, component tasks need to create and/or write to side-files, which differ from the actual job-output files.

In such cases there could be issues with two instances of the same Mapper or Reducer running simultaneously (for example, speculative tasks) trying to open and/or write to the same file (path) on the FileSystem. Hence the application-writer will have to pick unique names per task-attempt (using the attemptid, say attempt_200709221812_0001_m_000000_0), not just per task.

To avoid these issues the MapReduce framework, when the OutputCommitter is FileOutputCommitter, maintains a special${mapreduce.output.fileoutputformat.outputdir}/_temporary/_${taskid} sub-directory accessible via ${mapreduce.task.output.dir} for each task-attempt on the FileSystem where the output of the task-attempt is stored. On successful completion of the task-attempt, the files in the ${mapreduce.output.fileoutputformat.outputdir}/_temporary/_${taskid} (only) are promoted to${mapreduce.output.fileoutputformat.outputdir}. Of course, the framework discards the sub-directory of unsuccessful task-attempts. This process is completely transparent to the application.

The application-writer can take advantage of this feature by creating any side-files required in ${mapreduce.task.output.dir} during execution of a task viaFileOutputFormat.getWorkOutputPath(Conext), and the framework will promote them similarly for succesful task-attempts, thus eliminating the need to pick unique paths per task-attempt.

Note: The value of ${mapreduce.task.output.dir} during execution of a particular task-attempt is actually ${mapreduce.output.fileoutputformat.outputdir}/_temporary/_{$taskid}, and this value is set by the MapReduce framework. So, just create any side-files in the path returned by FileOutputFormat.getWorkOutputPath(Conext) from MapReduce task to take advantage of this feature.

The entire discussion holds true for maps of jobs with reducer=NONE (i.e. 0 reduces) since output of the map, in that case, goes directly to HDFS.

在一些應用當中，組成的任務必須創建一些其他文檔，跟實際輸出不同的文檔。

在這些情況當中將會同時存在兩個Mapper或者Reducer實例去打開或者寫到FileSystem中相同的文檔。因此應用開發者將會獲取獨一無二的任務目的（使用目的ID，假如say attempt_200709221812_0001_m_000000_0），不僅是每個任務。

說明：${mapreduce.task.output.dir}的值在一個特定任務執行過程中實際上是${mapreduce.output.fileoutputformat.outputdir}/_temporary/_{$taskid}的值，而這個值是由MapReduce框架設定的。所以，MapReduce任務利用這個特性從FileOutputForamt.getWorkOutPath(Context)返回的路徑創建副文檔。

整個討論適用於作業有map但沒有reduce的情況，因此map的output直接寫到HDFS.

RecordWriter

RecordWriter writes the output <key, value> pairs to an output file.

RecordWriter implementations write the job outputs to the FileSystem.

RecordWriter將鍵值對的輸出寫到輸出文件中。

RecordWriter實現類將job的輸出寫到FileSytem。

Other Useful Features

Submitting Jobs to Queues

Users submit jobs to Queues. Queues, as collection of jobs, allow the system to provide specific functionality. For example, queues use ACLs to control which users who can submit jobs to them. Queues are expected to be primarily used by Hadoop Schedulers.

Hadoop comes configured with a single mandatory queue, called 'default'. Queue names are defined in the mapreduce.job.queuename> property of the Hadoop site configuration. Some job schedulers, such as the Capacity Scheduler, support multiple queues.

A job defines the queue it needs to be submitted to through the mapreduce.job.queuename property, or through the Configuration.set(MRJobConfig.QUEUE_NAME, String) API. Setting the queue name is optional. If a job is submitted without an associated queue name, it is submitted to the 'default' queue.

用戶提交job到隊列中。隊列，也就是job的集合，允許系統提供特定的功能。例如，隊列使用ACLS來控制哪些用戶可以提交隊列。Hadoop Schedulers是隊列的主要使用者。

Hadoop設置一個單獨的強制的隊列，稱之為“默認”。隊列的名稱是在Hadoop-site配置文件中的mapreduce.job.queuename>屬性決定的。一些作業調度器支持多個的隊列，例如容量調度器。

一個作業通過mapreduce.job.queuename屬性或者Configuration.set(MRJobConfig.QUEUE_NAME, String)API來定義一個隊列。設置隊列的名字是可選的。如果一個作業被提交時並沒有設置隊列名稱，那么隊列名稱為“默認”。

Counters

Counters represent global counters, defined either by the MapReduce framework or applications. Each Counter can be of any Enum type. Counters of a particular Enum are bunched into groups of type Counters.Group.

Applications can define arbitrary Counters (of type Enum) and update them via Counters.incrCounter(Enum, long) or Counters.incrCounter(String, String, long) in the map and/or reducemethods. These counters are then globally aggregated by the framework.

計數器是全局計數器，由MapReduce框架或者應用定義。每一個計數器都可以是任何枚舉類型。Counters of a particular Enum are bunched into groups of type Counters.Group。

應用可以定義任意計數器和通過 Counters.incrCounter(Enum, long) 或者Counters.incrCounter(String, String, long)來更新在map/reduce方法中。這些計數器是通過框架進行全局計算的。

DistributedCache

DistributedCache distributes application-specific, large, read-only files efficiently.

DistributedCache is a facility provided by the MapReduce framework to cache files (text, archives, jars and so on) needed by applications.

Applications specify the files to be cached via urls (hdfs://) in the Job. The DistributedCache assumes that the files specified via hdfs:// urls are already present on the FileSystem.

The framework will copy the necessary files to the slave node before any tasks for the job are executed on that node. Its efficiency stems from the fact that the files are only copied once per job and the ability to cache archives which are un-archived on the slaves.

DistributedCache tracks the modification timestamps of the cached files. Clearly the cache files should not be modified by the application or externally while the job is executing.

DistributedCache can be used to distribute simple, read-only data/text files and more complex types such as archives and jars. Archives (zip, tar, tgz and tar.gz files) are un-archived at the slave nodes. Files have execution permissions set.

The files/archives can be distributed by setting the property mapreduce.job.cache.{files|archives}. If more than one file/archive has to be distributed, they can be added as comma separated paths. The properties can also be set by APIs Job.addCacheFile(URI)/ Job.addCacheArchive(URI) and Job.setCacheFiles(URI[])/ Job.setCacheArchives(URI[]) where URI is of the form hdfs://host:port/absolute-path#link-name. In Streaming, the files can be distributed through command line option -cacheFile/-cacheArchive.

The DistributedCache can also be used as a rudimentary software distribution mechanism for use in the map and/or reduce tasks. It can be used to distribute both jars and native libraries. The Job.addArchiveToClassPath(Path) or Job.addFileToClassPath(Path) api can be used to cache files/jars and also add them to the classpath of child-jvm. The same can be done by setting the configuration properties mapreduce.job.classpath.{files|archives}. Similarly the cached files that are symlinked into the working directory of the task can be used to distribute native libraries and load them.

分布式緩存有效地分布存儲應用專用的、大的、只讀的文件。

分布是緩存是MapReduce框架提供給應用用於緩存文件（文本，檔案、jar包和其他）。

應用可以通過urls (hdfs://)在Job中指定文件的緩存路徑。分布式緩存假設通過hdfs:// urls指定的文件已經存在現在的FileSystem。

這個框架將在某個從屬節點執行任何任務之前復制必要的文件到該節點上。它的高效源於這樣的事實：每個作業只復制一次到那些能夠存檔但是還沒存檔的節點上。

分布式緩存跟蹤緩存文件的修改時間戳。顯然當作業在執行時緩存文件不應該被應用或者外部修改。

分布式緩存可以用來分布緩存簡單的、只讀的的數據或者文本文檔和更復雜類型例如檔案和Jar包。檔案（zip, tar, tgz and tar.gz files）指的是未存檔到從屬節點的。文檔是有執行權限的。

文件可以通過設置mapreduce.job.cache.{files|archives}屬性來分配存儲。如果有更多的文件需要存儲，那么在用逗號隔開路徑即可。該屬性還可以通過Job.addCacheFile(URI)/ Job.addCacheArchive(URI) and Job.setCacheFiles(URI[])/ Job.setCacheArchives(URI[]) 來設置，URL的格式為hdfs://host:port/absolute-path#link-name。文件可以通過命令-cacheFile/-cacheArchive來實現分配存儲。

分布式緩存也可以用作一個基本的軟件分發機制用於map/reduce任務。它也可以用來分布存儲jar包和本地庫。Job.addArchiveToClassPath(Path) or Job.addFileToClassPath(Path) api可以用來緩存文件/jars並且子Jvm也會將它們添加到類路徑下。通過設置mapreduce.job.classpath.{files|archives}屬性也可以達到同樣效果。同樣地緩存文件通過符號鏈接到任務的工作路徑來分布緩存本地庫和加載它們。

Private and Public DistributedCache Files

DistributedCache files can be private or public, that determines how they can be shared on the slave nodes.

- "Private" DistributedCache files are cached in a local directory private to the user whose jobs need these files. 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.
- "Public" DistributedCache files are cached in a global directory and the file access is setup such that they are publicly visible to all users. 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.

分布式緩存文件可以是私有的或者公有的，以確定它們是否可以被分享到從屬節點。

- 私有分布式緩存文件被緩存在局部路徑屬於那些作業需要這些文件的用戶。這些文件只可以被指定用戶的所有任務和Job使用，而這些節點的其他用戶就不能使用。分布式緩存文檔在它所上傳的文件系統中通過他的權限變成私有的，文件系統通常為HDFS.如果這些文檔沒有全局讀取權限，或者它的路徑沒有全局的可執行查找權限，那么這些文檔就是私有的。
- 公有分布式緩存文檔被緩存在一個全局路徑並且文件被設置為對所有用戶都可見。這些文件可以被所有節點上的所有用戶分享。分布式緩存文件在它所上傳的文件系統上通過他的權限變成公有的，文件系統通常為HDFS。如果文件具有全局可讀權限，並且他的路徑具有全局的可執行查找權限，那么它就是公有的。也就是說，如果用戶想要使文件對所有用戶可見可操作，那么文件權限必須是全局可讀和他的路徑權限必須是全局可執行。

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.

User can specify whether the system should collect profiler information for some of the tasks in the job by setting the configuration property mapreduce.task.profile. The value can be set using the api Configuration.set(MRJobConfig.TASK_PROFILE, boolean). If the value is set true, the task profiling is enabled. The profiler information is stored in the user log directory. By default, profiling is not enabled for the job.

Once user configures that profiling is needed, she/he can use the configuration property mapreduce.task.profile.{maps|reduces} to set the ranges of MapReduce tasks to profile. The value can be set using the api Configuration.set(MRJobConfig.NUM_{MAP|REDUCE}_PROFILES, String). By default, the specified range is 0-2.

User can also specify the profiler configuration arguments by setting the configuration property mapreduce.task.profile.params. The value can be specified using the api Configuration.set(MRJobConfig.TASK_PROFILE_PARAMS, String). If the string contains a %s, it will be replaced with the name of the profiling output file when the task runs. These parameters are passed to the task child JVM on the command line. The default value for the profiling parameters is -agentlib:hprof=cpu=samples,heap=sites,force=n,thread=y,verbose=n,file=%s.

分析器是一個工具可以用來獲取2到3個Java內置分析器關於map和reduce的分析樣本。

用戶可以通過 mapreduce.task.profile來指定系統是否要收集某個作業的一些任務分析信息。這個值也可以通過Configuration.set(MRJobConfig.TASK_PROFILE, boolean) api來設置。如果這個值為真，那么任務分析將會生效。分析器的信息將儲存在用戶的log路徑下。該屬性默認是不生效的。

一旦用戶配置了該屬性，那么他/她就可以通過 mapreduce.task.profile.{maps|reduces} 來設置MapReduce任務的范圍。這個值也可以通過Configuration.set(MRJobConfig.NUM_{MAP|REDUCE}_PROFILES, String) api來設置。默認的值為0-2。

用戶也可以通過配置mapreduce.task.profile.params屬性來指定分析器的的參數。這個值也可以通過api Configuration.set(MRJobConfig.TASK_PROFILE_PARAMS, String)來設置。假如字符串里面包含%s，那么將會在任務執行時被替換成分析輸出文件的名字。這些參數將會在命令行中傳輸給任務所在的子JVM。默認的參數的值為-agentlib:hprof=cpu=samples,heap=sites,force=n,thread=y,verbose=n,file=%s。

Debugging

The MapReduce framework provides a facility to run user-provided scripts for debugging. When a MapReduce task fails, a user can run a debug script, to process task logs for example. The script is given access to the task's stdout and stderr outputs, syslog and jobconf. The output from the debug script's stdout and stderr is displayed on the console diagnostics and also as part of the job UI.

In the following sections we discuss how to submit a debug script with a job. The script file needs to be distributed and submitted to the framework.

MapReduce框架提供一個工具用來運行用戶提供的腳本用於調試。當一個MapReduce任務失敗，用戶可以運行調試腳本，去處理任務log。腳本可以讀取任務的stdout、stderr輸出、syslog和jobconf。調試腳本的stdout和sterr輸出將會作為Job UI的一部分顯示出來。

在接下來的部分我們將討論如何提交一個調試腳本到作業中。腳本文件需要提交和存儲在框架中。

How to distribute the script file:

The user needs to use DistributedCache to distribute and symlink the script file.

用戶需要使用分布式緩存來分發和符號鏈接腳本文件。

How to submit the script:

A quick way to submit the debug script is to set values for the properties mapreduce.map.debug.script and mapreduce.reduce.debug.script, for debugging map and reduce tasks respectively. These properties can also be set by using APIs Configuration.set(MRJobConfig.MAP_DEBUG_SCRIPT, String) and Configuration.set(MRJobConfig.REDUCE_DEBUG_SCRIPT, String). In streaming mode, a debug script can be submitted with the command-line options -mapdebug and -reducedebug, for debugging map and reduce tasks respectively.

The arguments to the script are the task's stdout, stderr, syslog and jobconf files. The debug command, run on the node where the MapReduce task failed, is:
$script $stdout $stderr $syslog $jobconf

Pipes programs have the c++ program name as a fifth argument for the command. Thus for the pipes programs the command is
$script $stdout $stderr $syslog $jobconf $program

通過mapreduce.map.debug.script 和nd mapreduce.reduce.debug.script屬性來分別設置map和reduce任務的調試腳本是一個快速的提交調試腳本的方法。這些屬性可以通過APIs Configuration.set(MRJobConfig.MAP_DEBUG_SCRIPT, String) 和 Configuration.set(MRJobConfig.REDUCE_DEBUG_SCRIPT, String)來設置。在流式編程模式，可以通過命令行選項 -mapdebug 和 –reducedebug來分別設置map和reduce的調試腳本用於調試。

腳本的參數是任務的標准輸出、標准錯誤、系統日志和作業配置文檔。調試命令，運行在某個Mapreduce任務失敗的節點上，是$script $stdout $stderr $syslog $jobconf $program。

擁有C++程度的Pipes項目在命令中增加第五個參數。因此命令如下：$script $stdout $stderr $syslog $jobconf $program

Default Behavior:

For pipes, a default script is run to process core dumps under gdb, prints stack trace and gives info about running threads.

在Pipes中，默認的腳本是運行在GDP的核心轉儲，打印堆跟蹤和運行線程的信息。

Data Compression

Hadoop MapReduce provides facilities for the application-writer to specify compression for both intermediate map-outputs and the job-outputs i.e. output of the reduces. It also comes bundled with CompressionCodec implementation for the zlib compression algorithm. The gzip, bzip2, snappy, and lz4 file format are also supported.

Hadoop also provides native implementations of the above compression codecs for reasons of both performance (zlib) and non-availability of Java libraries. More details on their usage and availability are available here.

Hadoop MapReduce提供一個功能讓應用開發指定壓縮方式用於map輸出的中間數據和job-outputs也就是reduce的輸出。它也捆綁着實現zlib壓縮算法的壓縮編碼器。支持gzip、bzip2、snappy和lz4文件格式的文檔。

Hadoop也提供上述編碼器的本地實現，因為性能和Java庫不支持的原因。更多關於它們的使用細節和可用性可參考官方文檔。

Intermediate Outputs

Applications can control compression of intermediate map-outputs via the Configuration.set(MRJobConfig.MAP_OUTPUT_COMPRESS, boolean) api and the CompressionCodec to be used via the Configuration.set(MRJobConfig.MAP_OUTPUT_COMPRESS_CODEC, Class) api.

應用可以通過Configuration.set(MRJobConfig.MAP_OUTPUT_COMPRESS, boolean) api來設置是否對map的輸出進行壓縮和Configuration.set(MRJobConfig.MAP_OUTPUT_COMPRESS_CODEC, Class) api指定壓縮編碼器。

Job Outputs

Applications can control compression of job-outputs via the FileOutputFormat.setCompressOutput(Job, boolean) api and the CompressionCodec to be used can be specified via the FileOutputFormat.setOutputCompressorClass(Job, Class) api.

If the job outputs are to be stored in the SequenceFileOutputFormat, the required SequenceFile.CompressionType (i.e. RECORD / BLOCK - defaults to RECORD) can be specified via the SequenceFileOutputFormat.setOutputCompressionType(Job, SequenceFile.CompressionType) api.

應用可以通過FileOutputFormat.setCompressOutput(Job, boolean) api來控制是否對作業輸出進行壓縮和通過FileOutputFormat.setOutputCompressorClass(Job, Class)api來設置壓縮編碼器。

如果作業的輸出是以SequenceFileOutputFormat格式存儲的，那么需要序列化。壓縮類型通過SequenceFileOutputFormat.setOutputCompressionType(Job, SequenceFile.CompressionType) api來指定。

Skipping Bad Records

Hadoop provides an option where a certain set of bad input records can be skipped when processing map inputs. Applications can control this feature through the SkipBadRecords class.

This feature can be used when map tasks crash deterministically on certain input. This usually happens due to bugs in the map function. Usually, the user would have to fix these bugs. This is, however, not possible sometimes. The bug may be in third party libraries, for example, for which the source code is not available. In such cases, the task never completes successfully even after multiple attempts, and the job fails. With this feature, only a small portion of data surrounding the bad records is lost, which may be acceptable for some applications (those performing statistical analysis on very large data, for example).

By default this feature is disabled. For enabling it, refer to SkipBadRecords.setMapperMaxSkipRecords(Configuration, long) and SkipBadRecords.setReducerMaxSkipGroups(Configuration, long).

With this feature enabled, the framework gets into 'skipping mode' after a certain number of map failures. For more details, seeSkipBadRecords.setAttemptsToStartSkipping(Configuration, int). In 'skipping mode', map tasks maintain the range of records being processed. To do this, the framework relies on the processed record counter. See SkipBadRecords.COUNTER_MAP_PROCESSED_RECORDS and SkipBadRecords.COUNTER_REDUCE_PROCESSED_GROUPS. This counter enables the framework to know how many records have been processed successfully, and hence, what record range caused a task to crash. On further attempts, this range of records is skipped.

The number of records skipped depends on how frequently the processed record counter is incremented by the application. It is recommended that this counter be incremented after every record is processed. This may not be possible in some applications that typically batch their processing. In such cases, the framework may skip additional records surrounding the bad record. Users can control the number of skipped records through SkipBadRecords.setMapperMaxSkipRecords(Configuration, long) andSkipBadRecords.setReducerMaxSkipGroups(Configuration, long). The framework tries to narrow the range of skipped records using a binary search-like approach. The skipped range is divided into two halves and only one half gets executed. On subsequent failures, the framework figures out which half contains bad records. A task will be re-executed till the acceptable skipped value is met or all task attempts are exhausted. To increase the number of task attempts, use Job.setMaxMapAttempts(int) and Job.setMaxReduceAttempts(int)

Skipped records are written to HDFS in the sequence file format, for later analysis. The location can be changed through SkipBadRecords.setSkipOutputPath(JobConf, Path).

Hadoop提供一個選項當執行map輸入時可以跳過某一組確定的壞數據。應用可以通過SkipBadRecords 類來控制特性。

當map任務中某些輸入一定會導致崩潰時可以使用這個屬性。這通常發生在map函數中的bug。通常地，用戶會修復這些bug。然而，某些時候不一定有用。這個bug可能是第三方庫導致的，例如那些源代碼看不了的。在這些情況當中，盡管經過多次嘗試都沒有辦法完成任務，作業也會失敗。通過這個屬性，只有一小部分的壞數據周邊數據會丟失，這對於某些應用是可以接受的（那么數據量非常的統計分析）

這個屬性默認是失效的。可以通過SkipBadRecords.setMapperMaxSkipRecords(Configuration, long) 和SkipBadRecords.setReducerMaxSkipGroups(Configuration, long)。來使它生效。

當這個屬性生效，框架在一定數量的map失敗后會進入“跳過模式”。在跳過模式中，map任務維持被處理數據的范圍，看看SkipBadRecords.setAttemptsToStartSkipping(Configuration, int)。為了達到這個目標，框架依賴於記錄計數器。看看SkipBadRecords.COUNTER_MAP_PROCESSED_RECORDS and SkipBadRecords.COUNTER_REDUCE_PROCESSED_GROUPS的說明。這個計數器是的框架可以知道有多少條記錄被成功處理了，因此來找出哪些記錄范圍會引起任務崩潰。在進一步的嘗試中，這些范圍的記錄會被跳過。

跳過記錄的數目取決於運行的記錄計數器的增長頻率。建議這個計數器在每天記錄處理增加。這在批量處理中可已不太可能實現。在這些情況當中，框架會跳過不良記錄附近的額外數據。用戶可以通過SkipBadRecords.setMapperMaxSkipRecords(Configuration, long) andSkipBadRecords.setReducerMaxSkipGroups(Configuration, long)來控制跳過記錄的數量。框架會試圖使用二進制搜索方式來縮窄跳過記錄的范圍。跳過范圍被分成兩部分並且只有其中一半會被拿來執行。在接下來的錯誤當中，框架將會指出哪一半范圍包含不良數據。一個任務將會重新執行直到跳過記錄或者嘗試次數用完。可以通過Job.setMaxMapAttempts(int) and Job.setMaxReduceAttempts(int).來增加嘗試次數。

跳過的記錄將會以序列化的形式寫到HDFS中。可以通過 SkipBadRecords.setSkipOutputPath(JobConf, Path)來修改路徑。

*由於譯者本身能力有限，所以譯文中肯定會出現表述不正確的地方，請大家多多包涵，也希望大家能夠指出文中翻譯得不對或者不准確的地方，共同探討進步，謝謝。

免責聲明！

本站轉載的文章為個人學習借鑒使用，本站對版權不負任何法律責任。如果侵犯了您的隱私權益，請聯系本站郵箱yoyou2525@163.com刪除。

猜您在找 Hadoop官方文檔翻譯—— YARN ResourceManager High Availability 2.7.3 Hadoop官方文檔翻譯——HDFS Architecture 2.7.3 Postman官方文檔翻譯 Mammoth官方文檔翻譯 FlowCanvas官方文檔翻譯（一） Jetty官方文檔翻譯 Spring官方文檔翻譯 FlowCanvas官方文檔翻譯（二） FullCalendar 官方文檔翻譯 AVFoundation 官方文檔翻譯