MapReduce Partitioned Intermediate Output Optimization
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Solution Overview
Problem
Conventional MapReduce systems incur high costs and time inefficiencies due to the extensive use of network resources during the shuffle operation between the map and reduce stages, as intermediate results are shuffled across the network.
Innovation Solution
Implementing a distributed computation system that partitions intermediate output on the same worker node, eliminating the need for re-partitioning and network transfer by keeping intermediate results on the same node for further processing in the reduce stage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If intermediate results are shuffled across the network from map nodes to reducer nodes, then the MapReduce system can process distributed data, but network resources are consumed and the shuffle operation becomes time-consuming
Solution Approach 1:
The patent segments the shuffle operation by partitioning intermediate results into distinct groups based on their reduce task destinations. Each partition contains intermediate results that can be processed together, allowing the system to organize and transfer data more efficiently by grouping related items rather than transferring individual results separately.
Solution Approach 2:
The patent performs preliminary partitioning of intermediate results before the shuffle operation begins. By pre-organizing the intermediate results into partitions based on their destination reduce tasks, the system eliminates the need for dynamic routing during the shuffle phase, reducing network overhead and operation time.
2Productivity
If intermediate results are shuffled across the network, then reduce stages can access processed data, but network resource usage increases expenses
Solution Approach 1:
The patent merges multiple intermediate results that are destined for the same reduce task into single partitions. This consolidation reduces the total number of network transfers required, as each partition is transferred once rather than having individual intermediate results sent separately, thereby reducing network resource consumption and expenses.
3Adaptability or versatility
If conventional MapReduce shuffle operations are used, then distributed computing can be achieved, but the system incurs high time and resource costs
Solution Approach 1:
The patent performs preliminary partitioning of intermediate results before the shuffle operation begins. By pre-organizing the intermediate results into partitions based on their destination reduce tasks, the system eliminates the need for dynamic routing during the shuffle phase, reducing network overhead and operation time.
Solution Approach 2:
The patent changes the organizational parameter of intermediate results from individual items to grouped partitions based on destination reduce tasks. This parameter change allows the shuffle operation to work with larger, pre-organized units of data, reducing the frequency of network operations and overall processing time while maintaining distributed computing capabilities.
Data Source
AI summary
Methods and systems for performing MapReduce optimization for partitioned intermediate output are disclosed. Partitions of a set of input data are provided to a plurality of worker nodes. Each worker node performs a first stage of computation on a respective partition of the input data to produce a respective partition of intermediate output data. Each worker node performs a sort operation on the respective partition of intermediate output data to produce a respective partition of sorted intermediate output data. Each worker node performs a second stage of computation on the respective partition of sorted intermediate output data to produce a respective partition of final output data.