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What is the fundamental purpose of the Map/Reduce principle in cloud computing?
What is the fundamental purpose of the Map/Reduce principle in cloud computing?
Which of the following is NOT one of the '4 V's of Big Data?
Which of the following is NOT one of the '4 V's of Big Data?
What major trend has influenced the volume of data generated?
What major trend has influenced the volume of data generated?
Which service leverages both public data and user-generated data?
Which service leverages both public data and user-generated data?
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What does the term 'data deluge' refer to?
What does the term 'data deluge' refer to?
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How do applications like Netflix and Spotify utilize data?
How do applications like Netflix and Spotify utilize data?
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What is a ZetaByte equivalent to in bytes?
What is a ZetaByte equivalent to in bytes?
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Why have many businesses transitioned to being data-driven?
Why have many businesses transitioned to being data-driven?
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What is a limitation of using global top-k as a local reducer?
What is a limitation of using global top-k as a local reducer?
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What is the output of the map function in the context of the Reverse Web-link graph?
What is the output of the map function in the context of the Reverse Web-link graph?
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Which of the following best describes the reduce function in the inverted index?
Which of the following best describes the reduce function in the inverted index?
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What is the primary goal of the k-Means clustering method?
What is the primary goal of the k-Means clustering method?
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In the context of the PageRank algorithm, what does the output pair from the map function represent?
In the context of the PageRank algorithm, what does the output pair from the map function represent?
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Which scenario exemplifies the issue with local reducers in the global top-k problem?
Which scenario exemplifies the issue with local reducers in the global top-k problem?
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What does the final pair emitted by the reduce function in the inverted index contain?
What does the final pair emitted by the reduce function in the inverted index contain?
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What characteristic is essential for the clusters formed by the k-Means method?
What characteristic is essential for the clusters formed by the k-Means method?
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What is the main challenge addressed when dealing with large amounts of data?
What is the main challenge addressed when dealing with large amounts of data?
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Which framework is mentioned for handling large static data?
Which framework is mentioned for handling large static data?
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What types of data will not be covered in this course?
What types of data will not be covered in this course?
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What format are log entries stored in within CouchDB?
What format are log entries stored in within CouchDB?
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What is the goal of processing logs in the provided example?
What is the goal of processing logs in the provided example?
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What does the term ‘Velocity’ refer to in the context of data handling?
What does the term ‘Velocity’ refer to in the context of data handling?
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In the example provided, which page types are mentioned for average generation time?
In the example provided, which page types are mentioned for average generation time?
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Which of the following statements is true regarding the content of the course?
Which of the following statements is true regarding the content of the course?
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What was one of the main reasons for the development of MapReduce?
What was one of the main reasons for the development of MapReduce?
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Which feature of MapReduce makes it accessible to non-computer science majors?
Which feature of MapReduce makes it accessible to non-computer science majors?
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In the context of MapReduce, what do users specify to process key/value pairs?
In the context of MapReduce, what do users specify to process key/value pairs?
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What kind of computations does MapReduce primarily deal with?
What kind of computations does MapReduce primarily deal with?
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What is the first step in the k-Means algorithm using Map/Reduce?
What is the first step in the k-Means algorithm using Map/Reduce?
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What challenge does MapReduce address that obscures simple computations?
What challenge does MapReduce address that obscures simple computations?
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Which application was primarily mentioned as a use case for MapReduce?
Which application was primarily mentioned as a use case for MapReduce?
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During the Map phase of k-Means, how is the nearest centroid determined?
During the Map phase of k-Means, how is the nearest centroid determined?
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What happens in the Reduce phase of the k-Means algorithm?
What happens in the Reduce phase of the k-Means algorithm?
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What type of model is MapReduce classified as?
What type of model is MapReduce classified as?
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What aspect of implementations did MapReduce simplify for users?
What aspect of implementations did MapReduce simplify for users?
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What condition indicates that the k-Means algorithm has finished iterating?
What condition indicates that the k-Means algorithm has finished iterating?
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What is a major limitation of the Map/Reduce model in relation to k-Means?
What is a major limitation of the Map/Reduce model in relation to k-Means?
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What does the cleanup step in the Reduce phase of k-Means accomplish?
What does the cleanup step in the Reduce phase of k-Means accomplish?
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What initial conditions are set for the centroids in the k-Means algorithm?
What initial conditions are set for the centroids in the k-Means algorithm?
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What is the purpose of emitting the nearest centroid and point during the Map phase?
What is the purpose of emitting the nearest centroid and point during the Map phase?
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What is the first step for the map workers in the execution process?
What is the first step for the map workers in the execution process?
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During the map phase, what do workers do with the input files?
During the map phase, what do workers do with the input files?
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Which phase follows the map phase in the execution overview?
Which phase follows the map phase in the execution overview?
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What action do the workers perform after reading the splits in the map phase?
What action do the workers perform after reading the splits in the map phase?
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How do map workers communicate the location of fresh data?
How do map workers communicate the location of fresh data?
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What do the intermediate files generated during the map phase store?
What do the intermediate files generated during the map phase store?
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What is the role of the Master in the map and reduce phases?
What is the role of the Master in the map and reduce phases?
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What is the end result of the reduce phase?
What is the end result of the reduce phase?
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Study Notes
Cloud Computing - Lesson 10: Map/Reduce
- Lecture Objectives: Introduce the need for large-scale data processing in cloud environments, present the Map/Reduce principle and supporting frameworks, and detail representative applications.
Introduction
- Cloud computing allows large-scale applications.
- Large-scale applications attract more users.
- Users generate more data.
- Applications generate more data (e.g., logs).
- How can data be leveraged to improve applications?
- "Big Data" phenomenon: a significant increase in data volume, velocity, variety, and veracity.
The 4 V's of Big Data
- Volume: Data volumes are increasing exponentially, and will continue to increase at a fast rate creating unprecedented amounts of data in the future.
- Velocity: Data is being generated and processed at a rapid pace.
- Variety: Data comes in various formats (e.g., text, images, videos).
- Veracity: Data quality and accuracy vary significantly.
The Data Deluge
- Data volume is growing exponentially worldwide.
- The COVID-19 crisis has accelerated this trend.
- Increasingly more human activities are digitalized.
Data-Supported Services
- Applications processing large volumes of public data (e.g., web search and indexing, large language models).
- Applications using user-generated data (e.g., social networks, recommendation engines, taxi hailing).
- Applications using both types of data.
Dealing with the Two First "V"s (Volume and Velocity)
- Need specific tools and models for handling large amounts of data.
- Complex environments (replication, distribution) and potential for faults or slow machines need to be considered.
- Volume: Map/Reduce framework for large static data.
- Velocity: Stream processing frameworks for dynamic data.
- Variety and Veracity are not covered.
Motivating Example: Logging
- Application front-end (FE) components generate logs.
- Client information, errors, page generation time, and accessed/purchased items are logged.
- Log entries are stored as JSON documents in a distributed CouchDB database which is a NoSQL database that is optimized for managing and scaling large volumes of data.
Processing Logs
- Objective: calculating the average generation time for each page type (e.g., home, product, cart, checkout).
- Log processing can provide useful insights.
Centralized Processing?
- Collecting all log documents in one process is ineffective.
- Logs are often too large to fit into memory or have too much bandwidth consumption.
- Not all logs are useful for operations resulting in wasted bandwidth.
- Centralized systems often lead to slow processing times.
Processing Logs In Parallel
- Parallel processing of logs across multiple processors/machines is more efficient than centralized processing.
- Logs are divided into partitions to be processed independently on various computing resources.
- Processing results from each partition are then combined.
Handling Volume
- Split data into partitions; process independently; then merge outputs.
- Manual handling is difficult (deploying worker processes close to data, coordinating workers, handling faults, and collecting all outputs).
Map/Reduce
- Big Data often uses a Map/Reduce pattern:
- Partition: Iterate over a large number of records in parallel.
- Map: Extract information of interest.
- Shuffle: Regroup information into sets, one for each category.
- Reduce: Aggregate the sets to get final results.
- Map/Reduce is a programming model enabling parallel processing on many machines.
Programming Model
- Programmer specifies two functions:
- map(record): Processes data from a partition and generates key/value pairs.
- reduce(key, {values}): Receives all values for same keys to generate aggregated results.
Map Phase Example:
- Process each log entry (record) through a map function to determine the page type and generate
pairs. - Key is the page type.
- Value is generation time and count.
Shuffle Phase
- Shuffle output of each mapper to group value pairs having the same keys.
Reduce Function and Phase
- Combine values associated to each unique key to get the final result.
Bandwidth Inefficiency
- Several <key,value> pairs are generated per mapper, and each is shuffled and sent independently to the corresponding reducer.
Local Reduction
- Could aggregations be performed on the mapper instead of shuffling all results?
- This avoids unnecessary network traffic and potentially allows for more efficient processing.
- The reducer can reapply the reduce function to the locally reduced data.
Link with Functional Programming
- Map: Function applied to every element in a list.
- Fold/Reduce: Accumulator with initial value to perform calculations on elements within the list.
- Map/Reduce is similar to these functional programming concepts.
Examples of Applications
- Word Count: Counts how often each word appears in a corpus.
- Word Count Local Reducer: Pre-reducing results to save bandwidth.
Distributed Grep
- Searching for lines matching a pattern in a distributed file system.
- Map function reads input and emits matching lines with a fixed key.
- Reduce function concatenates intermediate results.
Top-k Page Frequency
- Identify the top
kmost frequently accessed web pages. - Map function creates <URL, 1> pairs.
- Reduce function aggregates, sorts, and outputs.
Top-k Efficiency
- Combining all the data into a single reducer is inefficient.
k-Means Computation
- A typical data mining or database problem that organizes items into
kclusters. - Objective is to minimize distance between points in the same cluster.
k-Means Principle
- Get representative points (
m1,m2, etc.) of the clusters, which are called centroids. - Randomly initialize centroids.
- Assign each point to the closest centroid.
- Recalculate centroids.
Simple Example (k-Means)
- Illustrates the process with example data points.
k-Means in Map/Reduce
- Initialization: Randomly choose centroids.
- Map: Assign each point to the nearest centroid
- Reduce: Recalculate centroids based on assigned points.
Classification Step as Map
- Read in global variables with centroids from a file (initially k randomized points).
- Map each point to the closest centroid.
- Emit <nearest centroid, point>.
- Determine how to identify centroids.
Recentering Step as Reduce
- Initialize global variable
centroids. - Recompute centroids from the points assigned to each centroid during the map phase.
- For each point assigned to a centroid, emit the point and its centroid.
- Add centroid to global centroids.
- Save global centroids into a file.
- Repeated calculation until centroids have converged (no change).
Origin and Implementation of MapReduce
- MapReduce was originally proposed by Google in 2008.
- It was necessary due to the unprecedented scale of their data.
- It is important for various data processing tasks, such as log parsing and network monitoring.
Original Map/Reduce
- Job submitted to a master process.
- Master orchestrates execution.
- Each node supporting one or more workers.
- Workers handle map or reduce jobs.
- Map jobs get data from the file system.
- Communication between components using key-value pairs.
- Output written to the file system.
Execution Overview
- Detailed diagram for overall execution of the Map/Reduce framework.
Implementation Challenges
- Failing Workers: Detection and reassignment to another worker (partitioning helps by discarding partially processed data).
- Slow Workers: Monitor work, assign faster workers to handle work of slow ones (redundant computation/keep first finish).
- Failing Master: Snapshot and retry mechanisms.
Original Performance Measurements
- Configuration details (machines, memory, disks, ethernet)
- Aggregate bandwidth.
Distributed Grep
- Scan massive amounts of data for specific character patterns.
Sort
- Illustrative sort performance measures.
Map/Reduce Frameworks: Hadoop
- Open-source implementations of Map/Reduce.
- Specific tools and workflows are involved.
Map/Reduce in NoSQL Databases
- Tools like Hadoop are used for vast unstructured data and also in NoSQL databases.
- Map/Reduce calls are now supported by many NoSQL databases.
Map/Reduce as a Service
- Pre-configured versions of frameworks, like Hadoop, can be accessed as a service; this reduces infrastructure complexity.
- Cloud providers like Amazon offer managed Amazon EMR (Elastic MapReduce).
Conclusions
- Big data processing is crucial for cloud scale.
- Cloud-based applications use it to collect vast information
- This aids in deriving information useful for business.
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Description
This quiz explores fundamental concepts in cloud computing and big data, focusing on principles such as Map/Reduce, the characteristics of big data, and data-driven business strategies. Test your knowledge on various trends and terminologies associated with these technologies.
