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https://issues.apache.org/jira/browse/SPARK-4727?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-4727.
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Resolution: Won't Fix
I suggest the timeseries ideas be implemented in the spark-timeseries project
since that's already running with the idea. It doesn't cover spatial. But both
seem like app-space concerns for a separate library rather than Spark core.
> Add "dimensional" RDDs (time series, spatial)
> ---------------------------------------------
>
> Key: SPARK-4727
> URL: https://issues.apache.org/jira/browse/SPARK-4727
> Project: Spark
> Issue Type: Brainstorming
> Components: Spark Core
> Affects Versions: 1.1.0
> Reporter: RJ Nowling
>
> Certain types of data (times series, spatial) can benefit from specialized
> RDDs. I'd like to open a discussion about this.
> For example, time series data should be ordered by time and would benefit
> from operations like:
> * Subsampling (taking every n data points)
> * Signal processing (correlations, FFTs, filtering)
> * Windowing functions
> Spatial data benefits from ordering and partitioning along a 2D or 3D grid.
> For example, path finding algorithms can optimized by only comparing points
> within a set distance, which can be computed more efficiently by partitioning
> data into a grid.
> Although the operations on time series and spatial data may be different,
> there is some commonality in the sense of the data having ordered dimensions
> and the implementations may overlap.
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