Practice - MapReduce Paradigm: Decomposing Large-Scale Computation
Interactive Quizzes
Quick quizzes to reinforce your learning
For what type of machine learning task is MapReduce generally well-suited?
* **Type**: mcq
* **Options**: Real-time model prediction, Interactive model tuning, Batch training of certain models, Online learning
* **Correct Answer**: Batch training of certain models
* **Explanation**: MapReduce is effective for machine learning models where training data can be processed in large batches and updates applied iteratively using chained jobs.
* **Hint**: Consider the processing model MapReduce excels at.
Challenge Problems
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Problem: Design a high-level MapReduce job to count the frequency of unique URLs visited from a very large web server log file. Specify the input for the Mapper and Reducer, and their respective outputs.
- Solution:
- Mapper Input:
(line_offset, log_line_string) - Mapper Output:
(URL, 1)for each URL extracted from the log line. - Reducer Input:
(URL, list_of_ones)(e.g., ('www.example.com/page1', [1, 1, 1])) - Reducer Output:
(URL, total_count)(e.g., ('www.example.com/page1', 3))
- Mapper Input:
- Hint: Think about how to isolate the URL and then count its occurrences across the entire dataset.
- Solution:
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Problem: Evaluate the suitability of MapReduce for implementing a real-time recommendation system that needs to provide personalized recommendations instantly based on user click streams. What alternatives might be more appropriate?
- Solution: MapReduce is generally unsuitable for a real-time recommendation system because of its batch processing nature and inherent latency. It's designed for high-throughput, offline processing, not instant responses.
- More appropriate alternatives: Stream processing frameworks like Apache Kafka Streams, Apache Flink, or Apache Storm; or using in-memory databases and low-latency serving layers combined with machine learning models trained offline.
- Hint: Consider the critical requirement for 'real-time' and 'instantly' and how it clashes with MapReduce's core strengths.
- Solution: MapReduce is generally unsuitable for a real-time recommendation system because of its batch processing nature and inherent latency. It's designed for high-throughput, offline processing, not instant responses.
💡 Hint: Consider the processing model MapReduce excels at. ----- ## Challenge Problems 1. **Problem**: Design a high-level MapReduce job to count the frequency of unique URLs visited from a very large web server log file. Specify the input for the Mapper and Reducer, and their respective outputs. * **Solution**: * **Mapper Input**: `(line_offset, log_line_string)` * **Mapper Output**: `(URL, 1)` for each URL extracted from the log line. * **Reducer Input**: `(URL, list_of_ones)` (e.g., ('[www.example.com/page1](https://www.google.com/search?q=https://www.example.com/page1)', [1, 1, 1])) * **Reducer Output**: `(URL, total_count)` (e.g., ('[www.example.com/page1](https://www.google.com/search?q=https://www.example.com/page1)', 3)) * **Hint**: Think about how to isolate the URL and then count its occurrences across the entire dataset. 2. **Problem**: Evaluate the suitability of MapReduce for implementing a real-time recommendation system that needs to provide personalized recommendations instantly based on user click streams. What alternatives might be more appropriate? * **Solution**: MapReduce is generally *unsuitable* for a real-time recommendation system because of its batch processing nature and inherent latency. It's designed for high-throughput, offline processing, not instant responses. * **More appropriate alternatives**: Stream processing frameworks like Apache Kafka Streams, Apache Flink, or Apache Storm; or using in-memory databases and low-latency serving layers combined with machine learning models trained offline. * **Hint**: Consider the critical requirement for 'real-time' and 'instantly' and how it clashes with MapReduce's core strengths.
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