MapReduce is a programming model and implementation for processing large datasets on clusters. It simplifies the parallelization, fault tolerance, and data distribution of complex computations by allowing users to specify map and reduce functions. The underlying runtime system automatically schedules communication between machines, handles machine failures, and optimizes network and disk usage. Google has implemented over 10,000 different MapReduce programs in the past four years, with an average of 100,000 jobs running daily, processing over 20 petabytes of data. Before MapReduce, Google engineers used hundreds of custom computational nodes to process large unprocessed data, such as crawled documents or web request logs. These computations were theoretically simple but required significant parallel processing across hundreds to thousands of machines, leading to complex code. To address this complexity, the authors designed a new abstraction inspired by functional programming languages like Lisp, where users define map and reduce functions. The map function processes input records to generate intermediate key/value pairs, and the reduce function merges these pairs to produce the final output. This model simplifies parallelization and enables fault tolerance through re-execution. MapReduce is designed to handle large-scale computations efficiently on commodity PC clusters. It supports various implementation choices, depending on the environment. Google's implementation targets a large cluster of x86 dual-processor machines with Linux, 4-8GB of memory, and 1 Gbps network bandwidth. The cluster uses a distributed file system (GFS) for storage, ensuring reliability and availability. MapReduce tasks are divided into map and reduce phases. The map function processes input data to generate intermediate key/value pairs, which are then sorted and grouped by key. The reduce function merges these groups to produce the final output. The system handles machine failures by rescheduling tasks and re-executing failed tasks. It also optimizes network bandwidth by leveraging local disk storage for input data. Google has implemented MapReduce for various tasks, including graph processing, text processing, data mining, machine learning, and statistical machine translation. The system has been used to process large datasets, with performance evaluations showing significant improvements over traditional methods. For example, the Grep program, which searches for patterns in 10^10 records, achieved a peak performance of 30 GB/s, while the Sort program, which sorts 10^10 records, achieved a peak performance of 13 GB/s. MapReduce's performance is further enhanced by techniques like backup tasks to handle machine failures and local optimization to reduce network bandwidth usage. The system's flexibility and efficiency have made it a powerful tool for large-scale data processing.MapReduce is a programming model and implementation for processing large datasets on clusters. It simplifies the parallelization, fault tolerance, and data distribution of complex computations by allowing users to specify map and reduce functions. The underlying runtime system automatically schedules communication between machines, handles machine failures, and optimizes network and disk usage. Google has implemented over 10,000 different MapReduce programs in the past four years, with an average of 100,000 jobs running daily, processing over 20 petabytes of data. Before MapReduce, Google engineers used hundreds of custom computational nodes to process large unprocessed data, such as crawled documents or web request logs. These computations were theoretically simple but required significant parallel processing across hundreds to thousands of machines, leading to complex code. To address this complexity, the authors designed a new abstraction inspired by functional programming languages like Lisp, where users define map and reduce functions. The map function processes input records to generate intermediate key/value pairs, and the reduce function merges these pairs to produce the final output. This model simplifies parallelization and enables fault tolerance through re-execution. MapReduce is designed to handle large-scale computations efficiently on commodity PC clusters. It supports various implementation choices, depending on the environment. Google's implementation targets a large cluster of x86 dual-processor machines with Linux, 4-8GB of memory, and 1 Gbps network bandwidth. The cluster uses a distributed file system (GFS) for storage, ensuring reliability and availability. MapReduce tasks are divided into map and reduce phases. The map function processes input data to generate intermediate key/value pairs, which are then sorted and grouped by key. The reduce function merges these groups to produce the final output. The system handles machine failures by rescheduling tasks and re-executing failed tasks. It also optimizes network bandwidth by leveraging local disk storage for input data. Google has implemented MapReduce for various tasks, including graph processing, text processing, data mining, machine learning, and statistical machine translation. The system has been used to process large datasets, with performance evaluations showing significant improvements over traditional methods. For example, the Grep program, which searches for patterns in 10^10 records, achieved a peak performance of 30 GB/s, while the Sort program, which sorts 10^10 records, achieved a peak performance of 13 GB/s. MapReduce's performance is further enhanced by techniques like backup tasks to handle machine failures and local optimization to reduce network bandwidth usage. The system's flexibility and efficiency have made it a powerful tool for large-scale data processing.