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Working with metric data (individual measurements) in geoprocessing centers around the Metric object and Metric object arrays. It's simple and readable JSON, but verbose. Both geoprocessing functions and UI components can work with it. Operations on metric data is homebrewed assemblages of classic JS functions like map, filter, reduce.
We can possibly improve gain access to a lot more capability. We want a library that:
supports some kind of generic data structure, could be simple or something more powerful:
vanilla JS array
apache arrow - newer, lends itself for distributed processing and fast loading/saving data in a binary format (parquet).
ndarray - older format used by numpy, less performant than arrow
can convert from common data formats (interchange) to the format used by the library to do operations
access to a library with lots of built-in transformations
ability to chain operations
ability to store and operate on other interesting types of data (Series)
is reasonably fast, in some cases maybe really fast
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Working with metric data (individual measurements) in geoprocessing centers around the
Metric
object and Metric object arrays. It's simple and readable JSON, but verbose. Both geoprocessing functions and UI components can work with it. Operations on metric data is homebrewed assemblages of classic JS functions like map, filter, reduce.We can possibly improve gain access to a lot more capability. We want a library that:
Possibilities;
Articles
Apache Arrow
Background
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