Deferred Dataflow Graphs for Complex Parallel Pipelines
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Solution Overview
Problem
Existing large-scale data processing systems face challenges in efficiently managing complex data-parallel pipelines, particularly in distributed environments, due to the complexity of chaining multiple mapreduce operations and the need for extensive low-level coordination and management of intermediate data.
Innovation Solution
A data parallel pipeline system that includes an evaluator, optimizer, and executor to generate and execute a dataflow graph of deferred parallel operations, allowing for graph transformations and translation of multiple operations into a single mapreduce operation, with the ability to execute locally or remotely based on data size, and caching results for future use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple mapreduce operations are chained together in a data-parallel pipeline, then data processing capability is improved, but device complexity and management difficulty increase
Solution Approach 1:
The patent introduces an intermediary dataflow graph representation that mediates between the high-level chained mapreduce operations and the low-level execution details. The dataflow graph serves as an intermediate abstraction layer that captures dependencies and data flows between operations, enabling complex pipelines to be managed through a standardized graph structure rather than direct coordination of multiple operations.
Solution Approach 2:
The patent segments the pipeline management function into separate components: an evaluator that constructs the dataflow graph from chained operations, and an executor that handles the actual execution. This segmentation separates the complexity of representing multiple operations from the complexity of executing them, allowing each component to focus on its specific task.
2Measurement precision
If low-level coordination of intermediate data is performed manually, then control precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements self-service by having the system automatically generate and manage the dataflow graph representation from the chained mapreduce operations. The evaluator component automatically constructs the graph structure, tracks data flows, and manages intermediate data coordination without requiring manual intervention from developers, thereby maintaining control precision while significantly reducing operational burden.
Solution Approach 2:
The patent performs preliminary action by pre-generating the dataflow graph representation before execution. The evaluator constructs the complete dataflow graph in advance, establishing all data dependencies and coordination requirements ahead of time, so that during execution the system can simply follow the pre-planned graph rather than coordinating data flows dynamically.
3Productivity
If multiple parallel operations are executed remotely, then productivity is improved, but loss of time for data transmission increases
Solution Approach 1:
The patent applies dynamics by making the execution location flexible rather than fixed. The executor can dynamically determine whether to execute operations locally or remotely based on the specific operation characteristics and data sizes. This dynamic decision-making allows the system to optimize between parallel processing benefits and data transmission costs for each operation context.
Solution Approach 2:
The patent applies local quality by allowing different operations within the same pipeline to have different execution characteristics. Not all operations need to be executed remotely; the system can execute smaller or less computationally intensive operations locally while reserving remote execution for operations that benefit most from parallel processing, thereby optimizing the trade-off between productivity and transmission time for each local context.
Data Source
AI summary
A data parallel pipeline may specify multiple parallel data objects that contain multiple elements and multiple parallel operations that operate on the parallel data objects. Based on the data parallel pipeline, a dataflow graph of deferred parallel data objects and deferred parallel operations corresponding to the data parallel pipeline may be generated and one or more graph transformations may be applied to the dataflow graph to generate a revised dataflow graph that includes one or more of the deferred parallel data objects and deferred, combined parallel data operations. The deferred, combined parallel operations may be executed to produce materialized parallel data objects corresponding to the deferred parallel data objects.