MapReduce Inverted Index Overview
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Questions and Answers

What is the primary function of the map function in the inverted index?

  • To parse each document and emit hword, document ID pairs (correct)
  • To split input files into smaller chunks
  • To write output files
  • To combine data from multiple files

    • The reduce phase combines intermediate files from all workers into a single output file.

    True

    What are the two main phases described in the execution overview?

    Map phase and Reduce phase

    The workers perform the actions of read, write, and ______.

    <p>remote read</p> Signup and view all the answers

    Match the following actions with their corresponding phases:

    <p>Fork = Master Parse documents = Map Combine intermediate files = Reduce Write output files = Worker</p> Signup and view all the answers

    Which of the following describes the role of 'assign' in the execution overview?

    <p>To allocate tasks to workers</p> Signup and view all the answers

    Workers are responsible for both reading input files and writing output files.

    <p>True</p> Signup and view all the answers

    What kind of hardware setup is indicated for processing large clusters?

    <p>Commodity PCs connected with switched Ethernet</p> Signup and view all the answers

    What was used as the Reduce operator in the MapReduce process?

    <p>Identity function</p> Signup and view all the answers

    The entire computation took 891 seconds to complete.

    <p>True</p> Signup and view all the answers

    How many reduce tasks were executed during the first batch?

    <p>1700</p> Signup and view all the answers

    The final sorted output is written to a set of __________ files.

    <p>2-way replicated GFS</p> Signup and view all the answers

    Match the following terms with their descriptions:

    <p>GFS = Google File System used for storage Reduce operator = Processes intermediate key/value pairs Shuffling = Redistributing data for reduce tasks Partitioning function = Segregates data into pieces based on keys</p> Signup and view all the answers

    At what point in the computation did the shuffling of data begin?

    <p>600 seconds</p> Signup and view all the answers

    Machines execute more than one reduce task at a time.

    <p>False</p> Signup and view all the answers

    What is the estimated writing rate for the output during computation?

    <p>2-4 GB/s</p> Signup and view all the answers

    When was the first version of the MapReduce library written?

    <p>February 2003</p> Signup and view all the answers

    MapReduce is only applicable to large-scale machine learning problems.

    <p>False</p> Signup and view all the answers

    What optimization was significantly enhanced in the MapReduce library in August 2003?

    <p>locality optimization</p> Signup and view all the answers

    MapReduce allows programmers with no experience in ______ to exploit large amounts of resources.

    <p>distributed or parallel systems</p> Signup and view all the answers

    As of late September 2004, how many separate instances of the MapReduce program were checked into the source code management system?

    <p>900</p> Signup and view all the answers

    The development cycle for MapReduce programs is slowed down due to its complexity.

    <p>False</p> Signup and view all the answers

    What type of problems besides large-scale machine learning does MapReduce address?

    <p>clustering problems and data extraction for reports</p> Signup and view all the answers

    Match the following MapReduce functionalities with their descriptions:

    <p>Locality optimization = Improves data locality for efficiency Dynamic load balancing = Distributes tasks dynamically across machines Data extraction = Generates popular query reports Statistical logging = Logs computational resources used by jobs</p> Signup and view all the answers

    What approach does River use to achieve balanced completion times in parallel processing?

    <p>Careful scheduling of disk and network transfers</p> Signup and view all the answers

    MapReduce framework is designed to handle tasks across a single machine only.

    <p>False</p> Signup and view all the answers

    What is one key feature of the MapReduce framework?

    <p>Fault tolerance</p> Signup and view all the answers

    The River system improves performance in the presence of non-uniformities caused by __________ hardware.

    <p>heterogeneous</p> Signup and view all the answers

    What does the restricted programming model in MapReduce allow?

    <p>Partitioning of problems into fine-grained tasks</p> Signup and view all the answers

    Bulk Synchronous Programming has a similar programming model to MapReduce.

    <p>False</p> Signup and view all the answers

    What is the main advantage of dynamic scheduling in MapReduce?

    <p>Improved task assignment to faster workers</p> Signup and view all the answers

    Match the following concepts from parallel processing with their definitions:

    <p>MapReduce = Framework that separately schedules tasks for distributed execution River = System that balances completion times through careful scheduling Bulk Synchronous Programming = Programming model designed for wide-area network jobs Fault-tolerance = Ability to continue functioning despite failure of a component</p> Signup and view all the answers

    What is the purpose of the 'set_filebase' method in the code?

    <p>To configure the output file path</p> Signup and view all the answers

    The 'Adder' class is used as the reducer class in this MapReduce program.

    <p>True</p> Signup and view all the answers

    What does the function 'set_num_tasks' specify?

    <p>The number of tasks to be run in parallel</p> Signup and view all the answers

    The primary purpose of the WordCounter class is to count the ______ of each unique word.

    <p>occurrences</p> Signup and view all the answers

    Match the following methods with their purpose:

    <p>set_machines = Configure the number of machines to be used set_map_megabytes = Set memory allocation for map tasks set_reduce_megabytes = Set memory allocation for reduce tasks set_combiner_class = Define the combiner function for optimization</p> Signup and view all the answers

    What type of input does the 'Map' function in the WordCounter class process?

    <p>Text data</p> Signup and view all the answers

    The 'result' structure contains information about the memory used during the MapReduce process.

    <p>True</p> Signup and view all the answers

    What happens if the MapReduce function fails to execute?

    <p>The program aborts</p> Signup and view all the answers

    What type of input does the MapReduceInput handle in the provided code?

    <p>Text input files</p> Signup and view all the answers

    The 'Adder' class is referenced as the input class in the MapReduce program.

    <p>False</p> Signup and view all the answers

    What is the function of the 'set_filepattern' method in the code?

    <p>It sets the pattern for input files to be processed.</p> Signup and view all the answers

    The programming model of MapReduce allows programmers to exploit large amounts of __________.

    <p>resources</p> Signup and view all the answers

    Match the following components with their roles in the MapReduce framework:

    <p>Mapper = Processes input data and produces intermediate key-value pairs Reducer = Combines intermediate key-value pairs to produce final outputs Input Format = Defines the format of input data Output Format = Defines the format of output data</p> Signup and view all the answers

    What is the primary purpose of the counter facility in the MapReduce library?

    <p>To count occurrences of various events</p> Signup and view all the answers

    The MapReduce library allows for the skipping of records that cause deterministic crashes.

    <p>True</p> Signup and view all the answers

    What do workers process to determine which records may cause crashes?

    <p>Segmentation violations and bus errors</p> Signup and view all the answers

    User code creates a named counter object to count the number of ________ processed.

    <p>words</p> Signup and view all the answers

    Match the following signals with their corresponding actions in the MapReduce library:

    <p>Segmentation violation = Indicates an invalid memory access Bus error = Indicates a hardware access issue Signal handler = Catches errors during processing Counter = Counts occurrences of events</p> Signup and view all the answers

    What is the main goal of the MapReduce programming model?

    <p>To process and generate large data sets</p> Signup and view all the answers

    MapReduce abstracts away the complexity of parallelization, fault tolerance, and data distribution.

    <p>True</p> Signup and view all the answers

    What function does the reduce phase serve in MapReduce?

    <p>It merges all intermediate values associated with the same intermediate key.</p> Signup and view all the answers

    The MapReduce model was inspired by the map and reduce primitives found in ______.

    <p>Lisp</p> Signup and view all the answers

    Match the following parts of the MapReduce model with their descriptions:

    <p>Map = Processes key/value pairs and generates intermediate data Reduce = Merges intermediate values with the same key Runtime System = Manages execution across the machines Fault-tolerance = Handles failures during computation</p> Signup and view all the answers

    Which of the following tasks does the runtime system NOT handle?

    <p>Coding the map and reduce functions</p> Signup and view all the answers

    All computations in the MapReduce model require experience with parallel and distributed systems.

    <p>False</p> Signup and view all the answers

    What is one of the primary benefits of the MapReduce abstraction?

    <p>It simplifies the process of writing programs for large-scale data processing.</p> Signup and view all the answers

    What is the primary role of the 'WordCounter' class?

    <p>To count the number of occurrences of each unique word</p> Signup and view all the answers

    The 'set_combiner_class' method is optional in the provided MapReduce code.

    <p>True</p> Signup and view all the answers

    What is the significance of 'out->set_num_tasks(100);' in the code?

    <p>It specifies the number of tasks that the MapReduce job will execute.</p> Signup and view all the answers

    The __________ specifies the input parameters such as machine count and memory limits in the MapReduce code.

    <p>spec</p> Signup and view all the answers

    Match the following methods with their purposes:

    <p>set_filebase = Sets the base path for output files set_format = Defines the output file format set_machines = Specifies the number of machines to use set_map_megabytes = Configures memory limit for map tasks</p> Signup and view all the answers

    Which method is used to specify the reducer class in the code?

    <p>set_reducer_class</p> Signup and view all the answers

    The output of the MapReduce job includes information about the number of machines used.

    <p>True</p> Signup and view all the answers

    What command finishes the execution of the MapReduce job in the given code?

    <p>abort()</p> Signup and view all the answers

    What is the approximate size of the data being sorted in the sort program?

    <p>1 terabyte</p> Signup and view all the answers

    The sort program consists of more than 100 lines of user code.

    <p>False</p> Signup and view all the answers

    What is the relationship between the sorting key and the output in the sort program?

    <p>The sorting key is emitted along with the original text line as a key/value pair.</p> Signup and view all the answers

    Each machine in the cluster had __________ Intel Xeon processors.

    <p>two</p> Signup and view all the answers

    Match the following components with their descriptions:

    <p>Machine = Runs the sorting program 2GHz Intel Xeon = Type of processor used 4GB = Amount of memory per machine 160GB IDE = Type of storage used</p> Signup and view all the answers

    What is the main purpose of the Map function in the sort program?

    <p>To extract a sorting key from a text line</p> Signup and view all the answers

    The sort program can be executed on a single machine efficiently.

    <p>False</p> Signup and view all the answers

    What was the benchmark that the sort program is modeled after?

    <p>TeraSort</p> Signup and view all the answers

    The total number of records sorted by the sort program was __________ records.

    <p>10^10</p> Signup and view all the answers

    What issue is noted in scenario (c) of Figure 3 regarding the sort program's execution?

    <p>200 tasks were killed</p> Signup and view all the answers

    Data transfer rates remained constant during all execution scenarios.

    <p>False</p> Signup and view all the answers

    How much memory did each machine in the cluster have?

    <p>4GB</p> Signup and view all the answers

    The sorting program processes data in a __________ manner.

    <p>parallel</p> Signup and view all the answers

    Match the following data transfer rates with their descriptions:

    <p>Input (MB/s) = Rate at which input data is read Shuffle (MB/s) = Rate at which data is shuffled between tasks Output (MB/s) = Rate at which output data is written Done = Indicates completion of the sorting process</p> Signup and view all the answers

    Study Notes

    MapReduce Inverted Index

    • MapReduce is used to create an inverted index from parsed documents by emitting (keyword, document ID) pairs.
    • The Reduce function is an Identity function.
    • This function passes the intermediate key/value pair unchanged to the output.
    • The sorted output is stored in replicated Google File System (GFS) files.

    Data Partitioning In The Inverted Index

    • The input for the MapReduce program is split into 64MB pieces.
    • The sorted output (inverted index) is partitioned into 4000 files.
    • This partitioning is done using the initial bytes of the keyword.
    • The partitioning function has built-in knowledge of the distribution of keywords.

    Runtime Overview

    • The MapReduce computation is performed in a cluster using 1700 machines.
    • Each machine executes at most one reduce task at a time.
    • The shuffling phase for the reduce tasks starts around 300 seconds after the computation begins.
    • The shuffling phase ends around 600 seconds after the computation starts.
    • The writing of sorted data starts around 600 seconds and lasts for about 250 seconds.
    • The total computation time is 891 seconds, including startup overhead.

    The Overall MapReduce Implementation

    • The MapReduce system utilizes a distributed queue for inter-process communication.
    • The MapReduce library keeps track of resources used by each job, including counters, completion time, and the number of machines used.
    • The framework partitions the problem into fine-grained tasks and dynamically schedules them on available workers.
    • Redundant task execution is implemented to improve completion time in the presence of non-uniform hardware.

    The Word Frequency Example

    • The WordCounter class implements the Mapper interface.
    • The Map() function iterates through the input text, identifying words separated by whitespace.
    • The Reducer is defined as Adder.
    • The MapReduce() function takes a specification object and a MapReduceResult object as parameters.
    • The set_filebase(), set_num_tasks(), set_format(), set_reducer_class(), set_combiner_class(), set_machines(), set_map_megabytes(), and set_reduce_megabytes() methods are used to configure the MapReduce job.
    • The MapReduceResult object stores information about counters, execution time, machines used, etc.
    • The example demonstrates the basic usage of the MapReduce framework for counting word frequencies in a set of input files.

    MapReduce

    • MapReduce is a programming model for processing datasets with the use of "map" and "reduce" functions.
    • It automatically parallelizes computations across a distributed system, minimizing the need for users to write code for distributed computing.
    • The model incorporates fault tolerance, data distribution, and load balancing.
    • It leverages map and reduce primitives from Lisp and other functional languages.

    Implementation & Execution

    • Map function processes input key/value pairs.
    • Reduce function processes intermediate key/value pairs.
    • The runtime system handles partitioning data, scheduling tasks across machines, handling failures, and managing communication.
    • User programs written in MapReduce are automatically parallelized and executed on a large cluster of machines.

    Example Program

    • Word Counter: A program for counting the occurrence of unique words within input files.
    • The program is implemented with a Map function (WordCounter) which processes the input files and emits key/value pairs representing word occurrences.
    • The program uses a Reduce function (Adder) that merges the intermediate key/value pairs and outputs the final count for each word.
    • The program utilizes the Counter facility, which enables tracking key events like the total number of words processed.
    • The program has a user map function (WordCounter) and a user reduce function (Adder) which are written in the MapReduce library.

    Cluster Configuration

    • The cluster for these programs consisted of approximately 1800 machines.
    • Each machine had two 2GHz Intel Xeon processors with Hyper-Threading enabled.
    • Each machine had 4GB of memory and two 160GB IDE disks.

    Performance

    • Data transfer rates over time for different executions of the sort program were analyzed.
    • The performance analysis considered executions with both backup tasks and tasks killed.

    Data Handling & Error Detection

    • The system allows for the optional mode of execution which detects records causing deterministic crashes and skips them, enabling the program to continue its task despite errors.

    Reliability

    • Each worker node has a signal handler to catch segmentation violations and bus errors.
    • The MapReduce library stores the sequence number of the argument in a global variable before invoking a Map or Reduce function.
    • If a signal is generated by the user code, it is useful for identifying errors during debugging.

    Counters

    • Users can create named counter objects and increment them in either the Map or Reduce function.
    • Counters are used to track events during the program's execution.
    • An example is counting the total number of words processed or the number of documents indexed.

    Sort Program

    • The Sort program sorts 1010 100-byte records (approximately 1 Terabyte of data), based on the TeraSort benchmark.
    • The sorting program consists of less than 50 lines of user code and uses a three-line Map function.
    • The Map function extracts a 10-byte sorting key and emits it with the original text line as the intermediate key/value pair.

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    MapReduce: Simplified Data Processing on Large Clusters PDF

    Description

    This quiz covers the key concepts and processes involved in creating an inverted index using MapReduce. It includes data partitioning techniques, runtime overview, and the use of GFS for output storage. Test your understanding of how MapReduce can efficiently manage large datasets and keyword distribution.

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