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Q1. Name the most common InputFormats defined in Hadoop? Which one is default?
Following 2 are most common InputFormats defined in Hadoop
- TextInputFormat
- KeyValueInputFormat
- SequenceFileInputFormat
Q2. What is the difference between TextInputFormatand KeyValueInputFormat class?
TextInputFormat: It reads lines of text files and provides the offset of the line as key to the Mapper and actual line as Value to the mapper
KeyValueInputFormat: Reads text file and parses lines into key, val pairs. Everything up to the first tab character is sent as key to the Mapper and the remainder of the line is sent as value to the mapper.
Q3. What is InputSplit in Hadoop?
When a hadoop job is run, it splits input files into chunks and assign each split to a mapper to process. This is called Input Split
Q4. How is the splitting of file invoked in Hadoop Framework?
It is invoked by the Hadoop framework by running getInputSplit()method of the Input format class (like FileInputFormat) defined by the user
Q5. Consider case scenario: In M/R system,
- HDFS block size is 64 MB
- Input format is FileInputFormat
- We have 3 files of size 64K, 65Mb and 127Mb
then how many input splits will be made by Hadoop framework?
Hadoop will make 5 splits as follows
- 1 split for 64K files
- 2 splits for 65Mb files
- 2 splits for 127Mb file
Q6. What is the purpose of RecordReader in Hadoop?
The InputSplithas defined a slice of work, but does not describe how to access it. The RecordReaderclass actually loads the data from its source and converts it into (key, value) pairs suitable for reading by the Mapper. The RecordReader instance is defined by the InputFormat
Q7. After the Map phase finishes, the hadoop framework does "Partitioning, Shuffle and sort". Explain what happens in this phase?
- Partitioning
Partitioning is the process of determining which reducer instance will receive which intermediate keys and values. Each mapper must determine for all of its output (key, value) pairs which reducer will receive them. It is necessary that for any key, regardless of which mapper instance generated it, the destination partition is the same
- Shuffle
After the first map tasks have completed, the nodes may still be performing several more map tasks each. But they also begin exchanging the intermediate outputs from the map tasks to where they are required by the reducers. This process of moving map outputs to the reducers is known as shuffling.
- Sort
Each reduce task is responsible for reducing the values associated with several intermediate keys. The set of intermediate keys on a single node is automatically sorted by Hadoop before they are presented to the Reducer
Q9. If no custom partitioner is defined in the hadoop then how is data partitioned before its sent to the reducer?
The default partitioner computes a hash value for the key and assigns the partition based on this result
Q10. What is a Combiner?
The Combiner is a "mini-reduce" process which operates only on data generated by a mapper. The Combiner will receive as input all data emitted by the Mapper instances on a given node. The output from the Combiner is then sent to the Reducers, instead of the output from the Mappers.
Q11. Give an example scenario where a cobiner can be used and where it cannot be used.
There can be several examples following are the most common ones
- Scenario where you can use combiner
Getting list of distinct words in a file
- Scenario where you cannot use a combiner
Calculating mean of a list of numbers
Q12. What is job tracker?
Job Tracker is the service within Hadoop that runs Map Reduce jobs on the cluster
Q13. What are some typical functions of Job Tracker?
The following are some typical tasks of Job Tracker
- Accepts jobs from clients
- It talks to the NameNode to determine the location of the data
- It locates TaskTracker nodes with available slots at or near the data
- It submits the work to the chosen Task Tracker nodes and monitors progress of each task by receiving heartbeat signals from Task tracker
Q14. What is task tracker?
Task Tracker is a node in the cluster that accepts tasks like Map, Reduce and Shuffle operations - from a JobTracker
Q15. Whats the relationship between Jobs and Tasks in Hadoop?
One job is broken down into one or many tasks in Hadoop.
Q16. Suppose Hadoop spawned 100 tasks for a job and one of the task failed. What willhadoop do?
It will restart the task again on some other task tracker and only if the task fails more than 4 (default setting and can be changed) times will it kill the job
Q17. Hadoop achieves parallelism by dividing the tasks across many nodes, it is possible for a few slow nodes to rate-limit the rest of the program and slow down the program. What mechanism Hadoop provides to combat this
Speculative Execution?
Q18. How does speculative execution works in Hadoop?
Job tracker makes different task trackers process same input. When tasks complete, they announce this fact to the Job Tracker. Whichever copy of a task finishes first becomes the definitive copy. If other copies were executing speculatively, Hadoop tells the Task Trackers to abandon the tasks and discard their outputs. The Reducers then receive their inputs from whichever Mapper completed successfully, first.
Q19. Using command line in Linux, how will you
- see all jobs running in the hadoop cluster
- kill a job
- hadoop job -list
- hadoop job -kill jobid
Q20. What is Hadoop Streaming?
Streaming is a generic API that allows programs written in virtually any language to be used asHadoop Mapper and Reducer implementations
Q21. What is the characteristic of streaming API that makes it flexible run map reduce jobs in languages like perl, ruby, awk etc.
Hadoop Streaming allows to use arbitrary programs for the Mapper and Reducer phases of a Map Reduce job by having both Mappers and Reducers receive their input on stdin and emit output (key, value) pairs on stdout.
Q22. Whats is Distributed Cache in Hadoop
Distributed Cache is a facility provided by the Map/Reduce framework to cache files (text, archives, jars and so on) needed by applications during execution of the job. The framework will copy the necessary files to the slave node before any tasks for the job are executed on that node.
Q23. What is the benifit of Distributed cache, why can we just have the file in HDFS and have the application read it?
This is because distributed cache is much faster. It copies the file to all trackers at the start of the job. Now if the task tracker runs 10 or 100 mappers or reducer, it will use the same copy of distributed cache. On the other hand, if you put code in file to read it from HDFS in the MR job then every mapper will try to access it from HDFS hence if a task tracker run 100 map jobs then it will try to read this file 100 times from HDFS. Also HDFS is not very efficient when used like this.
Q.24 What mechanism does Hadoop framework provides to synchronize changes made in Distribution Cache during runtime of the application?
This is a trick questions. There is no such mechanism. Distributed Cache by design is read only during the time of Job execution
Q25. Have you ever used Counters in Hadoop. Give us an example scenario.
Anybody who claims to have worked on a Hadoop project is expected to use counters
Q26. Is it possible to provide multiple input to Hadoop? If yes then how can you give multiple directories as input to the Hadoop job?
Yes, The input format class provides methods to add multiple directories as input to a Hadoop job
Q27. Is it possible to have Hadoop job output in multiple directories. If yes then how?
Yes, by using Multiple Outputs class
Q28. What will a hadoop job do if you try to run it with an output directory that is already present? Will it
- overwrite it
- warn you and continue
- throw an exception and exit
The hadoop job will throw an exception and exit.
Q29. How can you set an arbitary number of mappers to be created for a job in Hadoop?
This is a trick question. You cannot set it
Q30. How can you set an arbitary number of reducers to be created for a job in Hadoop?
You can either do it progamatically by using method setNumReduceTasksin the JobConfclass or set it up as a configuration setting
32、设计一套系统，使之能够从不断增加的不同的数据源中，提取指定格式的数据。
要求：1、运行结果要能大致得知提取效果，并可据此持续改进提取方法；
2、由于数据来源的差异性，请给出可弹性配置的程序框架；
3、数据来源可能有Mysql,sqlserver等；
4、该系统具备持续挖掘的能力，即，可重复提取更多信息；
33. 经典的一道题：
现有1亿个整数均匀分布，如果要得到前1K个最大的数，求最优的算法。（先不考虑内存的限制，也不考虑读写外存，时间复杂度最少的算法即为最优算法）
我先说下我的想法:分块，比如分1W块，每块1W个，然后分别找出每块最大值，从这最大的1W个值中找最大1K个，那么其他的9K个最大值所在的块即可扔掉，从剩下的最大的1K个值所在的块中找前1K个即可。那么原问题的规模就缩小到了1/10。
问题：
1.这种分块方法的最优时间复杂度。
2.如何分块达到最优。比如也可分10W块，每块1000个数。则问题规模可降到原来1/100。但事实上复杂度并没降低。
3.还有没更好更优的方法解决这个问题。
34. MapReduce大致流程？
35. combiner, partition作用？
36.用mapreduce实现sql语句 select count(x) from a group by b？
36. 用mapreduce如何实现两张表连接，有哪些方法？
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原文地址：http://www.cnblogs.com/ilovexiao77/p/4841750.html
