Dynamic Dataset Build Scheduling in Distributed Pipelines
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Solution Overview
Problem
Traditional techniques for rebuilding complex datasets in distributed data processing systems are time-intensive and resource-intensive, often failing to complete build operations before a scheduled cutoff time, especially when raw datasets arrive asynchronously and in large numbers.
Innovation Solution
A method for automatically scheduling build operations by detecting new raw or derived datasets and initiating builds for dependent datasets as soon as all dependencies are satisfied, using a dataset dependency and timing metadata system to identify ready datasets and trigger immediate builds without waiting for all datasets to arrive.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional techniques wait for all raw datasets to be updated before building complex datasets, then data accuracy is ensured, but build time and resource consumption increase significantly
Solution Approach 1:
The patent segments the build process into independent units based on dataset dependencies. Instead of building all complex datasets sequentially after all raw datasets are updated, the system identifies and builds only the specific complex datasets whose dependencies are satisfied, allowing parallel execution of independent build operations.
Solution Approach 2:
The system performs preliminary identification of ready-to-build datasets by continuously monitoring dependency status. When a complex dataset's dependencies are satisfied, the system proactively initiates the build operation immediately, rather than waiting for a scheduled cutoff time or all datasets to be updated.
2Ease of operation
If traditional techniques schedule complete build operations at fixed cutoff times, then resource allocation is simplified, but the system cannot utilize asynchronous data arrivals efficiently
Solution Approach 1:
The patent implements a dynamic build scheduling system that adapts to asynchronous data arrivals and dependency changes in real-time. The system continuously monitors which complex datasets are ready to be built and dynamically allocates resources accordingly, rather than relying on fixed scheduled cutoff times.
Solution Approach 2:
The system incorporates feedback loops that monitor dependency status and trigger build operations when conditions are met. This feedback mechanism allows the system to respond to actual data availability patterns and optimize resource allocation based on real-time dependency satisfaction status.
3Reliability
If the system builds all derived datasets completely before cutoff time, then data freshness is guaranteed, but computational resources are over-utilized
Solution Approach 1:
The patent applies local quality by building only the specific complex datasets that are actually ready based on their individual dependency status. Instead of uniformly building all datasets, the system identifies and builds only those whose input raw datasets are available, optimizing computational resource usage.
Solution Approach 2:
The system performs partial build actions by initiating builds for only the subset of complex datasets whose dependencies are satisfied at any given time. This partial action approach prevents excessive computational resource consumption by avoiding unnecessary builds of datasets that cannot be completed yet.
Data Source
AI summary
Techniques for automatically scheduling builds of derived datasets in a distributed database system that supports pipelined data transformations are described herein. In an embodiment, a data processing method comprises, in association with a distributed database system that implements one or more data transformation pipelines, each of the data transformation pipelines comprising at least a first dataset, a first transformation, a second derived dataset and dataset dependency and timing metadata, detecting an arrival of a new raw dataset or new derived dataset; in response to the detecting, obtaining from the dataset dependency and timing metadata a dataset subset comprising those datasets that depend on at least the new raw dataset or new derived dataset; for each member dataset in the dataset subset, determining if the member dataset has a dependency on any other dataset that is not yet arrived, and in response to determining that the member dataset does not have a dependency on any other dataset that is not yet arrived: initiating a build of a portion of the data transformation pipeline comprising the member dataset and all other datasets on which the member dataset is dependent, without waiting for arrival of other datasets.