Repartition Join Concepts in MapReduce
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Questions and Answers

What is the primary assumption made when performing a Repartition Join?

  • |L| < |R| (correct)
  • |L| > |R|
  • |L| = |R|
  • |R| < |L|

    • In a Broadcast Join, the left relation must always be larger than the right relation.

    False

    What is the purpose of local predicates in the Repartition Join process?

    To filter unneeded tuples

    In a Repartition Join, the intermediate key is the value of the join key, _____ an annotation identifying to which relation the tuple belongs.

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

    Match the join types with their descriptions:

    <p>Repartition Join = Assumes the left relation is smaller than the right Broadcast Join = Broadcasts the smaller relation to all mappers Equi-Join = Joins tables based on equality of specified columns MapJoin = Uses a map-side join for smaller datasets</p> Signup and view all the answers

    Which operation is NOT part of the Repartition Join process?

    <p>Sorting tuples by size</p> Signup and view all the answers

    Dynamic join optimization occurs only during the initial stages of query execution.

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

    What happens to tuples in a Reduce phase during a Repartition Join?

    <p>They are joined with matching tuples from the other relation.</p> Signup and view all the answers

    The process of collapsing multiple MapReduce stages in a query plan is known as _____ folding.

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

    Which of the following is a characteristic of the improved Repartition Join?

    <p>Incorporates an annotation for identifying relation tuples</p> Signup and view all the answers

    Equi-Join can only be implemented in MapReduce if both relations are of equal size.

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

    What is a common optimization technique for complex queries in Hive?

    <p>Pruning unused partitions</p> Signup and view all the answers

    In Hive, the system uses a _____ translation of queries into a Directed Acyclic Graph (DAG).

    <p>DAG-based</p> Signup and view all the answers

    Match the MapReduce components with their corresponding tasks:

    <p>Map function = Processes input data and emits key-value pairs Reduce function = Aggregates results based on keys Join process = Combines records from two relations Filter operation = Excludes records based on criteria</p> Signup and view all the answers

    What does TPMMS stand for?

    <p>Tuple Processing Memory Management System</p> Signup and view all the answers

    The reduce phase in MapReduce can start before the map phase is finished.

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

    What is the primary function of the combiner in MapReduce?

    <p>To reduce intermediate results and network traffic.</p> Signup and view all the answers

    In a typical MapReduce process, output data is generated during the ________ stage.

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

    Match the following MapReduce components with their functions:

    <p>Map = Processes input data into k-v pairs Reduce = Aggregates and processes intermediate data Primary = Assigns tasks to workers Worker = Executes the assigned map and reduce tasks</p> Signup and view all the answers

    When can one fill memory and sort in Phase 1 of TPMMS?

    <p>Up to M / B - 1 times</p> Signup and view all the answers

    The straggler problem refers to the situation where all tasks run at the same speed.

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

    How much data can be sorted using M² / (RB) in TPMMS?

    <p>At most (M / R) * ((M / B) - 1) tuples</p> Signup and view all the answers

    The overall runtime for processing a block of data is approximately ________ years for 4.3 PB.

    <p>562</p> Signup and view all the answers

    Match the following terms with their descriptions:

    <p>Scheduling = Assigns workers to tasks Data Distribution = Moves processes to data Synchronization = Gathers and sorts data Error Handling = Manages worker failures</p> Signup and view all the answers

    What is the primary purpose of PageRank?

    <p>To rank web pages based on their importance</p> Signup and view all the answers

    PageRank algorithms only consider webpages with ingoing links to calculate their score.

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

    What are the two main phases of the PageRank algorithm in MapReduce?

    <p>Map phase and Reduce phase</p> Signup and view all the answers

    In the MapReduce algorithm, PageRank is calculated until it __________.

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

    Match the following components with their functions in Hive architecture:

    <p>Metastore = Stores metadata about tables and partitions Driver = Manages HiveQL sessions Query Compiler = Translates HiveQL to MR tasks Execution Engine = Interacts with the MapReduce engine</p> Signup and view all the answers

    Which of the following statements about MapReduce is accurate?

    <p>MapReduce processes large datasets in a distributed manner.</p> Signup and view all the answers

    Hive queries can be used for real-time data processing.

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

    What type of syntax does Hive use for executing queries?

    <p>SQL-like syntax</p> Signup and view all the answers

    Apache __________ is an open-source framework for processing large datasets using a distributed algorithm.

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

    What step is taken in the preprocessing phase of PageRank?

    <p>Removing pages with no ingoing links</p> Signup and view all the answers

    Pages with no outgoing links are referred to as dangling pages in PageRank.

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

    What is the role of the HiveServer in Hive architecture?

    <p>Integration with other applications</p> Signup and view all the answers

    The __________ framework is essential for the execution of MapReduce jobs.

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

    Which phase in PageRank involves computing the new ranks based on ingoing edges?

    <p>Reduce phase</p> Signup and view all the answers

    Study Notes

    Big Data Systems

    • Slides cover Big Data Systems, specifically MapReduce I.
    • The presenter is Martin Boissier, Data Engineering Systems, Hasso Plattner Institute.

    Announcements

    • One week remaining for the first exercise.
    • Check Moodle for discussions.

    Timeline I

    • Dates and topics for the course are outlined.
    • Topics include Intro/Organizational, Performance Management, MapReduce I, Map Reduce III, Data Centers, File Systems, Key Value Stores I, Key Value Stores III, Stream Processing I, ML Systems I, and related exercises/topics.
    • Wednesday sessions also included, covering topics like Intro to GitHub Classroom and Use Case Search Engines.

    This Lecture

    • Overview of MapReduce Paradigm, Sorting Large Amounts of Data, and MR Architecture.
    • Various sources are listed for further reading.

    Where are we?

    • First introduction to big data processing.
    • MapReduce is the ancestor of several subsequent big data processing frameworks.
    • MapReduce can be used independently from the full big data framework.
    • This is illustrated with a diagram showing the relationship between Application, Big Data Systems, and Infrastructure.

    Basic Web Search Interaction

    • Big data involves billions of web pages.
    • Non-interactive operations like web search tasks can handle minute to hour delays.
    • The systems rely on distributed processing using tens to thousands of servers.

    Large Scale Data Analysis

    • Initial issue regarding analyzing large datasets (20+ billion web pages).
    • A single computer can read up to 30-35 MB of data/second.
    • Analyzing 1,000 machines' data takes less than 3 hours in certain situations.
    • Distributed and parallel programming is challenging with clusters often breaking down at scale (e.g., many node and drive failures).
    • This was the problem that MapReduce was meant to solve.

    MapReduce

    • A programming model for large-scale distributed data processing.
    • Inspired by functions in functional languages like map and reduce.
    • A framework with a simple parallelization model and shared nothing architecture built around commodity hardware.
    • Developed by Google (Jeff Dean and Sanjay Ghemawat) and presented in 2004 at OSDI'04
    • Implemented extensively for web searches and other data processes, including as Apache Hadoop.

    MapReduce Framework

    • Part of Google's Big Data stack consisting of components like Sawzall, Index, GMail, Apps, BigTable, Scheduling System, GFS (Google File System) and Chubby.
    • This diagram shows the various components and data flow.

    Open Source Implementation

    • Apache Hadoop is an open source implementation and clone of the Google platform.
    • Developed at Yahoo!.
    • The various components of Hadoop are highlighted.

    Map – Shuffle/Sort – Reduce

    • An outline of basic MapReduce operations.

    Simple Example

    • An illustration with the task of counting word frequencies in a petabyte of text.
    • The 3-step process involved splitting text, grouping words, and counting in groups is explained.
    • The concept has a surprising level of applicability to many different operations.

    MapReduce - Conceptual Data Flow

    • This shows the basic flow of data through the MapReduce system.

    Map Example

    • Shows code for the 'map' part of the MapReduce paradigm, demonstrating how to input and output data.

    Reduce Example

    • Shows code for the 'reduce' part, which merges the output from the 'map' step. This is meant to combine all the intermediate values for a given key.

    Full Code Example (Hadoop)

    • Includes code snippets of a MapReduce program in Hadoop.

    Map Phase - Example Word Count

    • Demonstrates the 'map' phase with example input, showing the process of splitting, and creating key-value pairs such as (word, 1).

    Sort Phase - Example Word Count

    • Shows the 'sort' phase where the intermediate results are sorted for efficient combining.

    Reduce Phase - Example Word Count

    • Shows how the reduce step efficiently gathers and combines the values.

    Shuffling / Sorting Stage

    • Explains how the shuffle-sort process works on the Map side, involving buffering, partitioning, sorting and spilling to disk, and results.

    Shuffling / Sorting Stage II

    • Explains how the shuffle-sort works on the Reduce side, with fetching, buffering, merging and reducing.

    Sorting in Detail

    • Explores the concept of merge sort, a common in-memory sorting algorithm.
    • Provides an example to illustrate how merge sort works on two sorted input lists.

    Merge Sort

    • Details on the divide-and-conquer algorithm with its recursion, partitioning into lists, and merging into a large sorted list.
    • Provides the time complexity.
    • The algorithm's lower bound and its applicability to comparison-based sorting is illustrated.

    Two-Phase, Multiway Merge-Sort (TPMMS)

    • Handling large datasets that do not fit in memory (disk-based).
    • Two phases for loading and sorting of data, processing sorted parts in the second phase, and merging to a single list.

    2-Way Sort

    • Shows the process of sort operations and buffer management on the disk and in memory.

    TPMMS - Phase 1

    • Describes how recursion works for larger datasets and disk-based sorting with the TPMMS method.
    • Provides an illustration with an example of the costs involved in sorting larger datasets and an estimate of the required time.

    TPMMS – Phase 2

    • Explains how to manage multiple list partitions efficiently in the second phase and how to handle scenarios where blocks in main memory is greater than the number of partitions or blocks.

    Limitations of TPMMS

    • Discusses limitations based on size of input based on data, memory size and speed.
    • Example calculation is provided for estimating time required, which illustrates that sorting a large dataset using this method can be slow.

    Multi-Phase MMS

    • Describes the multi-phase version of the method when working with very large datasets that may not all fit in memory, and the impact of this on overall runtime.

    Short Break

    • Indicates a break in the presentation.

    MapReduce Architecture

    • A diagram illustrating the basic architecture of MapReduce.

    Simplified Architecture

    • Illustration of the architecture depicting the roles of Primary and Worker.
    • Input Data is separated into chunks.
    • Primary assigns map tasks to the worker nodes.
    • The worker nodes perform map tasks, creating intermediate data.
    • This intermediate data is then locally sorted, stored and the Primary Node decides the reduce node to process.
    • The result is stored in the global output data.

    MapReduce Execution Stages

    • Highlights the stages involved in MapReduce execution, including scheduling, data distribution, synchronization, and error handling.

    Parallelism

    • Discusses how Map and Reduce tasks run in parallel, which are dependent on input data.
    • Mentions a performance bottleneck in which the reduce phase has to wait until the map phase is completed.
    • The issue of single keys not being parallelized, and straggler problems are noted.

    Parallelism II

    • A flow diagram showing the further details about the parallel process.

    Additional Optimizations

    • Combiner: reduce on mapping nodes.
    • Reduces intermediate result size and network traffic.
    • Partitioning: splitting the data into partitions to allow for faster parallel reduce processing.
    • Locality: optimize task assignment to data locations on the cluster to lessen network traffic.

    Combiners

    • Explains the use of Combiners in MapReduce to perform partial reductions on mapper nodes, to decrease the volume of data transferred.
    • Shows a reduction in the size of intermediate data.

    Partitioning Function

    • Input to map is created using contiguous splits of input files.
    • Reduce uses an intermediate key to ensure that records with the same intermediate key end up at the same worker.
    • It uses a default partition function (hash(key) mod R) to distribute intermediate values in a "random" fashion.
    • Shows circumstances under which the partition function needs to be manually overwritten for load balancing.

    Locality

    • Primary assigns map tasks locally to input data locations.
    • This is designed to decrease network transfer time.
    • Map operations are further split into chunks (e.g., 64 MB).
    • The goal is to allow for thousands of machines to read input from local disk speeds, instead of relying on network transfers for data.

    Other Optimizations

    • Replicating tasks can reduce latency for slow worker nodes.
    • Skipping "bad" records in input can provide fault tolerance.
    • Compression of intermediate data can decrease the memory footprint, transfer time, and potential for issues.

    Fault Tolerance

    • Describes how heartbeat messages and materialized intermediate results on disk aid recovery.

    Summary

    • Overview of the MapReduce paradigm and framework.
    • Details on the basic framework functions.
    • Fault tolerance is highlighted.
    • Scalability of the MapReduce method is noted.

    Next Part

    • Indicates the next topic for discussion which is in this case on map reduce II.

    Questions?

    • Provides contact information (Moodle forum and email address) for students to ask questions.
    • Indicates the availability of Q&A sessions.

    Big Data Systems (new slide)

    • Title: Big Data Systems
    • Subtitle: Map Reduce II
    • The presenter is Prof. Tilmann Rabl.

    Announcements (new slide)

    • First exercise is due today.
    • TPMMS question is provided in Moodle.
    • Additional seminar details are presented.

    Multi-Phase MMS (new slide)

    • Discusses aspects of multi-layered MapReduce-style data processing methods and provides overall runtime calculations.

    Timeline I (new slide)

    • Course schedule, with dates and topics, similar to earlier slide(s).

    This Lecture (new slide)

    • Course topics covered: MR Algorithms, PageRank, Hive, SQL on MR, sources (web links).

    Where are we? (new slide)

    • General MapReduce processing.

    MR Algorithms (new slide)

    • Discussion of general MapReduce algorithms.

    MapReduce (new slide)

    • Overview of map-reduce programming.

    MapReduce Program Design (new slide)

    • Overview of the MapReduce program design.
    • Types of tasks (map, reduce) and program phases.

    Application - Inverted Index (new slide)

    • Explains an application of MapReduce programming in terms of structured data access, for web search.
    • Discusses the map process (parser), and reduce process (inverter).

    Page Rank (new slide)

    • Description about ranking pages based on their importance.

    Page Rank in MR (new slide)

    • Description on algorithm design for ranking pages with calculation formula.

    Page Rank: Basic Example (new slide)

    • Basic demonstration of calculating page ranking.

    Page Rank: Basic Example – Second iteration (new slide)

    • Illustration about continuing to calculate PageRank values until the results converge, that is, no further relevant changes in the calculated PageRank values

    Page Rank in MapReduce – Phase 1 (new slide)

    • Description of MapReduce algorithm for PageRank calculation, showing input, output and the core ideas behind the process.

    Page Rank in MapReduce – Data Distribution Phase I (new slide)

    • Diagram demonstrating the process of data distribution in the first phase of the PageRank MapReduce algorithm.

    Page Rank in MapReduce – Phase II (new slide)

    • Explanation of the second phase of the PageRank calculation using MapReduce, showing how data is processed by MapReduce.

    Page Rank cont'd (new slide)

    • Describes data processing steps of pre-processing, iterative calculations, and post-processing, including details like removing dangling pages, updating PageRanks and sorting.

    Hive (new slide)

    • Introduces Hive, a data warehousing system built on top of Hadoop's MapReduce.
    • Highlights DWH queries, no online queries, additional features, metadata in Derby database.

    Hive Architecture (new slide)

    • Describes the internal architecture of Hive.

    Data Types and Data Access (new slide)

    • Overview of different data types and access methods supported by Hive.

    Data Storage (new slide)

    • How data is stored in Hive, using HDFS, partitions, and buckets.
    • Data pruning mechanism is illustrated.

    HiveQL (new slide)

    • Explains how Hive uses SQL-like language for data querying.
    • Additional details like sub-queries, joins, group by, and cartesian products.

    Query Compilation (new slide)

    • Details on how Hive translates SQL queries into a directed acyclic graph (DAG) of tasks for execution by MapReduce.

    Query Compilation Flow (new slide)

    • Information on the complete process for translating an SQL Query into the execution system

    Complex Queries (new slide)

    • Details on optimization processes for complex queries, and common techniques such as removing unnecessary columns, filters, and partitions. The dynamic query optimization during run-time using MapJoin vs Repartition Join optimization is also described.

    Hive on other Backends (new slide)

    • Describes using other backends (e.g., Tez, Spark) when Hive suffers from MapReduce performance limitations.
    • Key components are identified that should be retained, such as metadata and optimizations.

    Short Break (new slide)

    SQL on MR (new slide)

    • Highlights SQL operations that are readily translated to MapReduce.

    Translating SQL to MR (new slide)

    • Processes for translating SQL into MapReduce

    Relational Operators in Map Reduce (new slide)

    • Details on common SQL operations in relational algebra that can be expressed within the MapReduce paradigm.

    Repartition Join (new slide)

    • Detailed exploration of how data is joined using a repartition process based on the join key. It discusses aspects such as assumption, processing logic, partitioning and sorting.

    Improved Repartition Join (new slide)

    • Detailed exploration of an improved, more efficient approach to repartitioning joins compared to the basic method.

    Broadcast Join (new slide)

    • Expands understanding of broadcast joins - which are useful in cases where one of the relations to be joined is significantly smaller that the other.

    Multi-Dimensional Partitioned Join (new slide)

    • Description that expands the understanding of joins within a relational database that are based on joins within a star schema structure.

    Join Performance in Hadoop (new slide)

    • Examination of the performance of joins based on execution time, varying record size and different join techniques in the context of distributed data processing using Hadoop.

    Problem with Hadoop/MR (new slide)

    • Highlights limitations of MapReduce and Hadoop, particularly in relation to memory use, and inefficiency for certain applications

    First Improvement: Tez (new slide)

    • Description of Tez, a framework that enhances MapReduce's capabilities by improving the execution process via a graph structure.

    Summary (new slide)

    • Summary of the topics covered, including PageRank on MR, Hive, SQL, and relational operators in MapReduce.

    Next Part (new slide)

    • Introduction to Spark.

    Spark (new slide)

    • Introduction to Spark, a faster and more versatile distributed data processing framework that addresses several common issues with MapReduce.

    Spark Architecture (new slide)

    • Detailed explanation on architecture and various elements like logical data structures, programming language bindings, and use of different core components for processing.

    Spark Basics (new slide)

    • Introduction to Resilient Distributed Datasets and their structure which helps in handling large dataset operations.

    Spark Components (new slide)

    • Explains different parts of a functional spark application process, including client, workers, and the underlying data access.

    Example Job (new slide)

    • Shows an illustrative job example in which data from an RDD of files is loaded and processed. The process and how to handle data caching and handling when data is unavailable in main memory. The HadoopRDD's mechanism for handling data localizations is also noted.

    RDD Abstraction (new slide)

    • Detailed description of the key characteristics of Resilient Distributed Datasets, including their transformation structure.

    Partitions and Implicit/Explicit Partitioning (new slide)

    • Detailed explanation of how partitions are used in Spark and how you can have implicit partition management (on every data shuffling) or explicit control over this using repartitioning.

    Transformation Types (new slide)

    • Overview of the different types of processing operations within Spark, highlighting transformations like map, flatMap, filter, sample, union, and joins.

    Scheduling Process(new slide)

    • Details on how the scheduling process takes place in Spark.

    Lazy Evaluation, Caching, Lineage (new slide)

    • Discusses the principles of lazy execution and caching in Spark to improve processing performance and details how pipeline operations can be performed

    Task Details (new slide)

    • Explains how each task operated in the Spark system to execute map-reduce operations.

    Event Flow (new slide)

    • Describes the event flow starting execution to finish from an application request to how the whole process is handled by the components in the system.

    RDD Recovery (new slide)

    • Details on how Spark RDDs are recovered in the case that failures happen during a distributed processing job.

    Short Break (new slide)

    Spark SQL (new slide)

    • Discussion of Spark SQL and its functionality for working with data in a declarative SQL style.

    Structured Data Access (new slide)

    • Description of different types of querying methods supported by Spark and how this compares with regular programming.

    Query Compilation in Spark SQL (new slide)

    • Explanation on the process to compile a query in Spark SQL.

    Spark SQL Example (new slide)

    • Example of how to join tables and apply filters on output data.

    Spark vs Hadoop (new slide)

    • Comparison of different processing methods, showing how Spark methods are faster using less machines, compared with standard MapReduce approaches.

    Next Steps (new slide)

    • Exploration of the direction for future processing engines in the big data domain. This includes incremental improvements of existing models.

    Summary (new slide)

    • Overview on Spark functionality, emphasizing speed improvements over MapReduce

    Next Part (new slide)

    • Introduction to the idea of big data processing in a datacenter / cloud environment.

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    Big Data Systems Map Reduce I PDF

    Description

    Test your knowledge on the assumptions and processes involved in Repartition Joins within MapReduce. This quiz covers various aspects such as intermediate keys, local predicates, and optimization techniques in Hive. Challenge yourself with questions about join types and process characteristics.

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