What is MapReduce ?
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MapReduce is one of the most important and major component in Hadoop Ecosystem. Whenever we are having a large set of data then in the case of the huge data set will be divided into smaller pieces and processing will be done on them in parallel in MapReduce.
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5) Once all the meaningful samples are extracted from the dataset, it will sort those keys, and pick N-1 keys from those sorted keys where N is number of reducers and it places in a Partition file which is used for Total order sorting.
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1) This is a code Sample for Total Order Sorting, in this we have specified the sampler object as RandomSample class. And we also set the Number of reducers using setNumReduceTasks().
And also we specified the Partionfile location unsing setPartionfile() of TotalOrderPartitioner class.
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2) Also HashPartitioner ensures that all records with the same map output key goes to the same reducer, but it doesn’t perform total sorting of the map output keys across all the reducers.
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Lecture 2 part 3Jazan University The document discusses key concepts related to Hadoop including its components like HDFS, MapReduce, Pig, Hive, and HBase. It provides explanations of HDFS architecture and functions, how MapReduce works through map and reduce phases, and how higher-level tools like Pig and Hive allow for more simplified programming compared to raw MapReduce. The summary also mentions that HBase is a NoSQL database that provides fast random access to large datasets on Hadoop, while HCatalog provides a relational abstraction layer for HDFS data.
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Unit 2vishal choudhary Apache MapReduce is a programming model and software framework for processing vast amounts of data in parallel. It works by breaking jobs into map and reduce tasks that can be executed in parallel on large clusters. The map tasks take input data and convert it into intermediate key-value pairs, and the reduce tasks combine these intermediate outputs to produce the final results. As an example, a MapReduce job is presented that analyzes weather data to find the maximum recorded temperature for each year, by having mappers extract the year and temperature from records and reducers find the maximum temperature for each year.
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- 3. The MapReduce is one of the main components of the Hadoop Ecosystem. MapReduce is designed to process a large amount of data in parallel by dividing the work into some smaller and independent tasks. MapReduce programs take input as a list and convert to the output as a list also.
- 4. The Map takes a set of keys and values as input. It may be in a structured or unstructured form The Keys are the reference of input files and Values are the dataset The task is applied on every input value
- 5. The Reducer takes the key-value pair which is created by the mapper as input The key-value pairs are sorted by the key elements In the Reducer, we perform the sorting, aggregation or summation type jobs
- 6. The given inputs are processed by the user-defined methods. All different business logics are working on the mapper section. Mapper generates intermediate data and Reducer takes them as input. The data are processed by user-defined function in the Reducer section. The final output is stored in HDFS (Hadoop Distributed File System).
- 7. When Mapper output is collected it is partitioned which means that it will be written to the output specified by the partitioner Partitioning is responsible for dividing up the intermediate key space and assigning intermediate key-value pairs to reducers It assigns approximately the same number of keys to each reducer
- 8. Combiners are an optimization in MapReduce that allow for local aggregation before the shuffle and sort phase If a Combiner is used then the map key-value pairs are not immediately written to the output. Instead they will be collected in lists, one list per each key-value
- 9. Let us take a real-world example to comprehend the power of MapReduce Twitter receives around 500 million tweets per day which is nearly 3000 tweets per second